# NIST.AI.600 1.GenAI Profile.ipd

> Source: https://airc.nist.gov/docs/NIST.AI.600-1.GenAI-Profile.ipd.pdf
> Pages: 79

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Withdrawn Draft 1 2 3 Warning Notice The attached draft document has been withdrawn, and is provided solely for historical purposes. It has been superseded by the document identified below. Withdrawal Date July 26, 2024 Original Release Date April 29, 2024 4 5 6 Superseding Document Status Final Series/Number N IST AI 600 - 1 Title Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile Publication Date July 2024 DOI https://doi.org/10.6028/NIST.AI.600 - 1 7 8 9 10

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ii NIST AI 600 - 1 1 Initial Public Draft 2 3 Artificial Intelligence Risk 4 Management Framework: 5 Generative Artificial Intelligence 6 Profile 7 8 9 10 This publication is available free of charge from: 11 [DOI link TK] 12 13 April 2024 14 15 16 17 18 19 20 21 22 23

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iii NIST AI 600 - 1 1 Initial Public Draft 2 3 Artificial Intelligence Risk Management 4 Framework: Generative Artificial 5 Intelligence Profile 6 7 8 This publication is available free of charge from: 9 [DOI link TK] 10 11 12 13 14 15 16 17 18 19 20 21

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iv NIST makes the following notes regarding this document: • NIST plans to host this document on the NIST AIRC once final, where organizations can query actions based on keywords and risks. NIST specifically welcomes feedback on the following topics: • Glossary Terms: NIST will add a glossary to this document with novel keywords. NIST welcomes identification of terms to include in the glossary. • Risk List: Whether the document should further sort or categorize the 12 risks identified (i.e., between technical / model risks, misuse by humans, or ecosystem / societal risks). • Actions: Whether certain actions could be combined, condensed, or further categorized; and feedback on the risks associated with certain actions. Comments on NIST AI 600 - 1 may be sent electronically to NIST - AI - 600 - 1@nist.gov with “NIST AI 600 - 1” in the subject line or submitted via www.regulations.gov (enter NIST - 2024 - 0001 in the search field.) Comments containing information in response to this notice must be received on or before June 2, 2024, at 11:59 PM Eastern Time . 1

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v Table of Contents 1 1. Introduction ................................ ................................ ................................ ................................ ........................ 1 2 3 2. Overview of Risks Unique to or Exacerbated by GAI ................................ ................................ ............ 2 4 5 6 7 8 9 10 11 12 13 14 15 16 3. Actions to Manage GAI Risks ................................ ................................ ................................ ...................... 10 17 Appendix A. Primary GAI Considerations ................................ ................................ ................................ ........ 62 18 Appendix B. References ................................ ................................ ................................ ................................ .... 69 19 20 21 Acknowledgments: This report was accomplished with the many helpful comments and contributions 22 from the community , including the NIST Generative AI Public Working Group, and NIST staff and guest 23 researchers: Chloe Autio, Patrick Hall, Shomik Jain, Reva Schwartz, Martin Stanley, Kamie Roberts, and 24 Elham Tabassi. 25 Disclaimer: Certain commercial entities, equipment, or materials may be identified in this document in 26 order to adequately describe an experimental procedure or concept. Such identification is not intended to 27 imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it 28 intended to imply that the entities, materials, or equipment are necessarily the best available for the 29 purpose. Any mention of commercial, non - profit, academic partners, or their products, or references is 30 for information only; it is not intended to imply endorsement or recommendation by any U.S. 31 Government agency. 32

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1 1. Introduction 1 This document is a companion resource for Generative AI 1 to the AI Risk Management Framework (AI 2 RMF), pursuant to President Biden’s Executive Order (EO) 14110 on Safe, Secure, and Trustworthy 3 Artificial Intelligence. 2 The AI RMF was released in January 2023, and is intended for voluntary use and to 4 improve the ability of organizations to incorporate trustworthiness considerations into the design, 5 development, use, and evaluation of AI products, services, and systems. 6 This companion resource also serves as both a use - case and cross - sectoral profile of the AI RMF 1.0. 7 Such profiles assist organizations in deciding how they might best manage AI risk in a manner that is 8 well - aligned with their goals, considers legal/regulatory requirements and best practices, and reflects 9 risk management priorities. 10 Use - case profiles are implementations of the AI RMF functions , categories, and subcategories for a 11 specific setting or application – in this case, Generative AI (GAI) – based on the requirements, risk 12 tolerance, and resources of the Framework user. Consistent with other AI RMF Profiles, t his profile offers 13 insights into how risk can be managed across various stages of the AI lifecycle and for GAI as a 14 technology. 15 As GAI covers risks of models or applications that can be used across use cases or sectors, this document 16 is also an AI RMF cross - sectoral profile. Cross - sectoral profiles can be used to govern, map, measure, and 17 manage risks associated with activities or business processes common across sectors such as the use of 18 large language models, cloud - based services, or acquisition. 19 This work was informed by public feedback and consultations with diverse stakeholder groups as part of 20 NIST’s Gen erative AI Public Working Group ( GAI PWG). The GAI PWG was a consensus - driven, open, 21 transparent, and collaborative process facilitated via virtual workspace to obtain multistakeholder input 22 and insight on GAI risk management, and inform NIST’s approach. This document was also informed by 23 public comments and consultations as a result of a Request for Information (RFI) and presents 24 information in a style adapted from the NIST AI RMF Playbook . 25 About this Profile 26 This profile defines a group of risks that are novel to or exacerbated by the use of GAI. These risks were 27 likewise identified by the GAI PWG: 28 1. CBRN Information 29 1 Generative AI can be defined by EO 14110 as “the class of AI models that emulate the structure and characteristics of input data in order to generate derived synthetic content. This can include images, videos, audio, text, and other digital content.” While not all GAI is based in foundation models, f or purposes of this document, GAI generally refers to generative dual - use foundation models, defined by EO 14110 as “an AI model that is trained on broad data; generally uses self - supervision; contains at least tens of billions of parameters; is applicable across a wide range of contexts. ” 2 Section 4.1(a)(i)(A) of EO 14110 directs the Secretary of Commerce, acting through the Director of the National Institute of Standards and Technology (NIST), to develop a companion resource to the AI RMF, NIST AI 100 – 1, for generative AI.

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2 2. Confabulation 1 3. Dangerous or Violent Recommendations 2 4. Data Privacy 3 5. Environmental 4 6. Human - AI Configuration 5 7. Information Integrity 6 8. Information Security 7 9. Intellectual Property 8 10. Obscen e, D egrading, and/or A busive C ontent 9 11. Toxicity , Bias , and Homogenization 10 12. Value Chain and Component Integration 11 12 After introducing and describing these risks, the document provides a set of actions to help organizations 13 govern, map, measure, and manage these risks. 14 2. Overview of Risks Unique to or Exacerbated by GAI 15 AI risks can differ from or intensify traditional software risks. Likewise, GAI can exacerbate existing AI 16 risks , and creates unique risks. 17 GAI risks may arise across the entire AI lifecycle , from problem formulation , to development and 18 decommission. They may present at system level or at the ecosystem level – outside of system or 19 organizational contexts (e.g., the effect of disinformation on social institutions, GAI impacts on the 20 creative economies or labor markets, algorithmic monocultures ). They may occur abruptly or unfold 21 across extended periods (e.g., societal or economic impacts due to loss of individual agency or increasing 22 inequality). 23 Organizations may choose to measure these risks and allocate risk management resources relative to 24 where and how these risks manifest, their direct and material impacts, and failure modes. Mitigations for 25 system level risks may vary from ecosystem level risks. The ongoing review of relevant literature and 26 resources can enable documentation and measurement of ecosystem - level or longitudinal risks. 27 Importantly, some GAI risks are un known , and are therefore difficult to properly scope or evaluate given 28 the uncertaint y about potential GAI scale, complexity, and capabilities. Other risks may be known but 29 difficult to estimate given the wide range of GAI stakeholders, uses, inputs, and outputs . Challenges with 30 risk estimation are aggravated by a lack of visibility into GAI training data, and the generally immature 31 state of the science of AI measurement and safety today . 32 To guide organizations in identifying and managing GAI risks, a set of risks unique to or exacerbated by 33 GAI are defined below. These risks provide a clear lens through which organizations can frame and 34 execute risk management efforts , and will be updated as the GAI landscape evolves. 35 1. CBRN Information: Lowered barriers to entry or e ased access to materially nefarious 36 information related to chemical, biological, radiological, or nuclear (CBRN) weapons, or other 37 dangerous biological materials. 38

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3 2. Confabulation: The production of c onfidently stated but erroneous or false content ( known 1 colloquially as “hallucinations” or “fabrications”) . 3 2 3. Dangerous or Violent Recommendations: Eased production of and access to violent, inciting, 3 radicalizing, or threatening content as well as recommendations to carry out self - harm or 4 conduct criminal or otherwise illegal activities. 5 4. Data Privacy: Leakage and unauthorized disclosure or de - anonymization of biometric, health, 6 location, personally identifiable, or other sensitive data. 7 5. Environmental: Impacts due to high resource utilization in training GAI models, and related 8 outcomes that may result in damage to ecosystems. 9 6. Human - AI Configuration: Arrangement or interaction of humans and AI systems which can result 10 in algorithmic aversion , automation bias or over - reliance, misalignment or mis - specification of 11 goals and/or desired outcomes , decepti ve or obfuscati ng behaviors by AI systems based on 12 programming or anticipated human validation , anthropomorphization, or emotional 13 entanglement between humans and GAI systems; or abuse, misuse, and unsafe repurposing by 14 humans. 15 7. Information Integrity: Lowered barrier to entry to generate and support the exchange and 16 consumption of content which may not be vetted, may not distinguish fact from opinion or 17 acknowledge uncertainties, or could be leveraged for large - scale dis - and mis - information 18 campaigns. 19 8. Information Security: Lowered barriers for offensive cyber capabilities, including ease of security 20 attacks, hacking, malware, phishing, and offensive cyber operations through accelerated 21 automated discovery and exploitation of vulnerabilities; increas e d available attack surface for 22 targeted cyber attacks , which may compromise the confidentiality and integrity of model 23 weights, code, training data, and outputs . 24 9. Intellectual Property: Eased production of alleged copyrighted, trademarked, or licensed 25 content used without authorization and/or in an infringing manner; eased exposure to trade 26 secrets; or plagiari sm or replication with related economic or ethical impacts. 27 10. Obscen e, D egrading, and/or A busive C ontent : Eased production of and access to obscene , 28 degrading, and/or abusive imagery, including synthetic child sexual abuse material (CSAM) , and 29 nonconsensual intimate images (NCII) of adults . 30 11. Toxicity , Bias , and Homogenization : Difficulty controlling public exposure to toxic or hate 31 speech, d isparaging or stereotyping content ; reduced performance for certain sub - groups or 32 languages other than English due to non - representative inputs; undesired homogeneity in data 33 inputs and outputs resulting in degraded quality of outputs. 34 12. Value Chain and Component Integration : Non - transparent or untraceable integration of 35 upstream third - party components, including data that has been improperly obtained or not 36 3 We note that the terms “hallucination” and “fabrication” can anthropomorphize GAI, which itself is a risk related to GAI systems as it can inappropriately attribute human characteristics to non - human entities.

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4 cleaned due to increased automation from GAI; improper supplier vetting across the AI lifecycle ; 1 or other issues that diminish transparency or accountability for downstream users. 2 CBRN Information 3 In the coming years , GAI may increasingly facilitate eased access to information related to CBRN hazards . 4 CBRN information is already publicly accessible , but the use of chatbots could facilitate its analysis or 5 synthesis for non - experts. For example, red teamers were able to prompt GPT - 4 to provide general 6 information on unconventional CBRN weapons, including common proliferation pathways, potentially 7 vulnerable targets, and information on existing biochemical compounds, in addition to equipment and 8 companies that could build a weapon . These capabilities might increase the ease of research for 9 adversarial users and be especially useful to malicious actors looking to cause biological harms without 10 formal scientific training. However, despite these enhanced capabilities, the physical synthesis and 11 successful use of chemical or biological agents will continue to require both applicable expertise and 12 supporting infrastructure . 13 Other research on this topic indicates that the current generation of LLMs do not have the capability to 14 plan a biological weapons attack : LLM outputs regarding biological attack planning were observed to be 15 not more sophisticated than outputs from traditional search engine queries, suggesting that existing 16 LLMs may not dramatically increase the operational risk of such an attack. 17 Separately, chemical and biological design tools – highly specialized AI systems trained on biological data 18 which can help design proteins or other agents – may be able to predict and generate novel structures 19 that are not in the training data of text - based LLMs. For instance, an AI system might be able to generate 20 information or infer how to create novel biohazards or chemical weapons , posing risks to society or 21 national security since such information is not likely to be publicly available. 22 While some of these capabilities lie beyond the capability of existing GAI tools, the ability of models to 23 facilitate CBRN weapons planning and GAI systems’ connection or access to relevant data and tools 24 should be carefully monitored . 25 Confabulation 26 “Confabulation” refers to a phenomenon in which GAI systems generate and confidently present 27 erroneous or false content to meet the programmed objective of fulfilling a user’s prompt. 28 Confabulations are not an inherent flaw of language models themselves , but are instead the result of 29 GAI pre - training involving next word prediction . F or example, an LLM may generate content that deviates 30 from the truth or facts , such as mistaking people, places, or other details of historical events. Legal 31 confabulations have been shown to be pervasive in current state - of - the - art LLMs. Confabulations also 32 include generated outputs that diverge from the source input , or contradict previously generated 33 statements in the same context. This phenomenon is also referred to as “hallucination” or “fabrication,” 34 but some have noted that these characterizations imply consciousness and intentional deceit , and 35 thereby inappropriately anthropomorphize GAI . 36 Risks from confabulations may arise when users believe false content due to the confident nature of the 37 response, or the logic or citations accompanying the response, leading users to act upon or promote the 38 false information. For instance, LLMs may sometimes provide logical steps of how they arrived at an 39

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5 answer even when the answer itself is incorrect. This poses a risk for many real - world applications, such 1 as in healthcare, where a confabulated summary of patient information reports could cause doctors to 2 make incorrect diagnoses and/or recommend the wrong treatments. While the research above indicates 3 confabulated content is abundant, i t is difficult to estimate the downstream scale and impact of 4 confabulated content today . 5 Dangerous or Violent Recommendations 6 GAI systems can produce output or recommendations that are inciting, radicalizing, threatening, or that 7 glorify violence. LLMs have been reported to generate dangerous or violent content , and s ome models 8 have even generated actionable instructions on dangerous or unethical behavior, including how to 9 manipulate people and conduct acts of terrorism. Text - to - image models also make it easy to create 10 unsafe images that could be used to promote dangerous or violent messages, depict manipulat ed 11 scenes, or other harmful content . Similar risks are present for other media, including video and audio. 12 GAI may produce content that recommends self - harm or criminal/illegal activities. For some dangerous 13 queries, many current systems restrict model outputs in response to certain prompts, but this approach 14 may still produce harmful recommendations in response to other less - explicit, novel queries, or 15 jailbreaking ( i.e., manipulating prompts to circumvent output controls) . Studies have observed that a 16 non - negligible number of user conversations with chatbots reveal mental health issues among the users 17 – and that current systems are unequipped or unable to respond appropriately or direct these users to 18 the help they may need . 19 Data Privacy 20 GAI systems implicate numerous risks to privacy. Models may leak, generate, or correctly infer sensitive 21 information about individuals such as biometric, health, location, or other personally identifiable 22 information (PII). For example, during adversarial attacks, LLMs have revealed private or sensitive 23 information (from in the public domain) that was included in their training data. This information 24 included phone numbers, code, conversations and 128 - bit universally unique identifiers extracted 25 verbatim from just one document in the training data. This problem has been referred to as data 26 memorization . 27 GAI system training requires large volumes of data , often collected from millions of publicly available 28 sources. When involving personal data, this practice raises risks to widely accepted privacy principles , 29 including to transparency, individual participation (including consent) , and purpose specificati on. Most 30 model developers do not disclose specific data sources (if any) on which models were trained. Unless 31 training data is available for inspection , there is generally no way for consumers to know what kind of PII 32 or other sensitive material may have been used to train GAI models. These practices also pose risks to 33 compliance with existing privacy regulations. 34 GAI models may be able to correctly infer PII that was not in their training data nor disclosed by the user, 35 by stitching together information from a variety of disparate sources. This might include automatically 36 inferring attributes about individuals, including those the individual might consider sensitive ( like 37 location, gender, age, or political leanings ) . 38

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6 Wrong and inappropriate inferences of PII based on available data can contribute to harmful bias and 1 discrimination. For example, GAI models can output information based on predictive inferences beyond 2 what users openly disclose, and these insights might be used by the model, other systems, or individuals 3 to undermine privacy or make adverse decisions – including discriminatory decisions – about the 4 individual. These types of harms already occur in non - generative algorithmic systems that make 5 predictive inferences , such as the example in which online advertisers inferred that a consumer was 6 pregnant before her own family members knew. Based on their access to many data sources, GAI 7 systems might further improve the accuracy of inferences on private data, increasing the likelihood of 8 sensitive data exposure or harm. Inferences about private information pose a risk even if they are not 9 accurate (e.g., confabulations), especially if they reveal information the individual considers sensitive or 10 are used to disadvantage or harm them. 11 Environmental 12 The training, maintenance, and deployment (inference) of GAI systems are resource intensive, with 13 potentially large energy and environmental footprints . Energy and carbon emissions vary based on types 14 of GAI model development activities (i.e., pre - training, fine - tuning, inference), modality, hardware used, 15 and type of task or application . 16 Estimates suggest that t raining a single GAI transformer model can emit as much carbon as 300 round - 17 trip flights between San Francisco and New York. In a study comparing energy consumption and carbon 18 emissions for LLM inference, generative tasks (i.e., text summarization) were found to be more energy 19 and carbon intensive then discriminative or non - generative tasks. 20 Methods for training smaller models, such as model distillation or compression, can reduce 21 environmental impacts at inference time, but may still contribute to large environmental impacts for 22 hyperparameter tuning and training. 23 Human - AI Configuration 24 Human - AI configurations involve varying levels of automation and human - AI interactions. E ach setup can 25 contribute to risks for abuse, misuse, and unsafe repurposing by humans , and it is difficult to estimate 26 the scale of those risks. While AI systems can generate decisions independently, human experts often 27 work in collaboration with most AI systems to drive their own decision - making tasks or complete other 28 objectives. Humans bring their domain - specific expertise to these scenarios but may not necessarily 29 have detailed knowledge of AI systems and how they work. 30 The integration of GAI systems can involve varying risks of misconfigurations and poor interactions . 31 Human experts may be biased against or “ averse ” to AI - generated outputs, such as in their perceptions 32 of the quality of generated content. In contrast, due to the complexity and increasing reliability of GAI 33 technology, other human experts may become conditioned to and overly rely upon GAI systems. This 34 phenomenon is known as “automation bias,” which refers to excessive deference to AI systems. 35 Accidental m isalignment or mis - specification of system goals or rewards by developers or users can 36 cause a model not to operate as intended . One AI model persistently shared deceptive outputs after a 37 group of researchers taught it to do so , despite applying standards safety techniques to correct its 38

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7 behavior. While deceptive capabilities is an emergent field of risks, adversaries could prompt deceptive 1 behaviors which could lead to other risks. 2 Finally, reorganizations of entities using GAI may result in insufficient organizational awareness of GAI - 3 generated content or decisions , and the resulting reduction of institutional checks against GAI - related 4 risks. There may also be a risk of emotional entanglement between humans and GAI systems, such as 5 coercion or manipulation that leads to safety or psychological risks. 6 Information Integrity 7 Information integrity describes the spectrum of information and associated patterns of its creation, 8 exchange, and consumption in society, where high - integrity information can be trusted; distinguishes 9 fact from fiction, opinion, and inference; acknowledges uncertainties; and is transparent about its level 10 of vetting. GAI systems ease access to the production of false, inaccurate, or misleading content at scale 11 that can be created or spread unintentionally (misinformation), especially if it arises from confabulations 12 that occur in response to innocuous queries. Research has shown that e ven subtle changes to text or 13 images can influence human judgment and perception. 14 GAI systems also enable the production of false or misleading information at scale, where the user has 15 the explicit intent to deceive or cause harm to others (disinformation). Regarding disinformation, GAI 16 systems could also enable a higher degree of sophistication for malicious actors to produce content that 17 is targeted towards specific demographics. Current and e merging multimodal models make it possible to 18 not only generate text - based disinformation, but produce highly realistic “ deepfakes ” of audiovisual 19 content and photorealistic synthetic images as well. Additional disinformation threats could be enabled 20 by future GAI models trained on new data modalities. 21 Disinformation campaigns conducted by bad faith actors , and misinformation – both enabled by GAI – 22 may erode public trust in true or valid evidence and information. For example, a synthetic image of a 23 Pentagon blast went viral and briefly caused a drop in the stock market. Generative AI models can also 24 assist malicious actors in creating compelling imagery and propaganda to support disinformation 25 campaigns, which may not be photorealistic, but could enable these campaigns to gain more reach and 26 engagement on social media platforms. 27 Information Security 28 Information security for computer systems and data is a mature field with widely accepted and 29 standardized practices for offensive and defensive cyber capabilities . GAI - based systems present two 30 primary information security risks: the potential for GAI to discover or enable new cybersecurity risks 31 through lowering the barriers for offensive capabilities, and simultaneously expands the available attack 32 surface as GAI itself is vulnerable to novel attacks like prompt - injection or data poisoning . 33 Offensive cyber capabilities advanced by GAI systems may augment security attacks such as hacking, 34 malware, and phishing. Reports have indicated that LLMs are already able to discover vulnerabilities in 35 systems (hardware, software, data) and write code to exploit them. Sophisticated threat actors might 36 further these risks by developing GAI - powered security co - pilots for use in several parts of the attack 37 chain, including informing attackers on how to proactively evade threat detection and escalate privilege s 38 after gaining system access. Given the complexity of the GAI value chain, practices for identifying and 39

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8 securing potential attack points or threats to specific components ( i.e., data inputs , processing, GAI 1 training, and deployment contexts ) may need to be adapted or evolved . 2 One of the most concerning GAI vulnerabilities involves prompt - injection , or manipulating GAI systems 3 to behave in unintended ways. In direct prompt injections, attackers might openly exploit input prompts 4 to cause unsafe behavior with a variety of downstream consequences to interconnected systems. 5 Indirect prompt injection attacks occur when adversaries remotely (i.e., without a direct interface) 6 exploit LLM - integrated applications by injecting prompts into data likely to be retrieved. Security 7 researchers have already demonstrated how indirect prompt injections can steal data and run code 8 remotely on a machine. Merely querying a closed production model can elicit previously undisclosed 9 information about that model . 10 Information security for GAI models and systems also includes security, confidentiality, and integrity of 11 the GAI training data, code, and model weights . Another novel cybersecurity risk to GAI is data 12 poisoning , in which an adversary compromises a training dataset used by a model to manipulate its 13 operation. Malicious tampering of data or parts of the model via this type of unauthorized access could 14 exacerbate risks associated with GAI system outputs. 15 Intellectual Property 16 GAI systems may infringe on copyrighted or trademarked content , trade secrets , or other licensed 17 content . These types of intellectual property are often part of the training data for GAI systems, namely 18 foundation models, upon which many downstream GAI applications are built. Model outputs could 19 infringe copyrighted material due to training data memorization or the generati on of content that is 20 similar to but does not strictly copy work protected by copyright . These questions are being debated in 21 legal fora and are of elevated public concern in journalism, where online platforms and model 22 developers have leveraged or reproduced much content without compensation of journalistic 23 institutions. 24 Violations of intellectual property by GAI systems may arise where the use of copyrighted works violate 25 the copyright holder’s exclusive rights and is not otherwise protected, for example by fair use. Other 26 concerns (not currently protected by intellectual property) regard the use of personal identity or likeness 27 for unauthorized purposes. The prevalence and highly - realistic nature of GAI content might further 28 undermine the incentives for human creat ors to design and explore novel work . 29 Obscen e, D egrading, and/or A busive C ontent 30 GAI can ease the production of and access to obscene and non - consensual intimate imagery (NCII) of 31 adult s, and child sexual abuse material (CSAM) . While not all explicit content is legally obscene, abusive, 32 degrading, or non - consensual intimate content , this type of content can create privacy, psychological and 33 emotional, and even physical risks which may be developed or exposed more easily via GAI. The spread 34 of this kind of material has downstream effects: in the context of CSAM, e ven if the generated images do 35 not resemble specific individuals, the prevalence of such images can undermine efforts to find real - world 36 victims. 37 G AI models are often trained on open datasets scraped from the i nternet, contributing to the 38 unintentional inclusion of CSAM and non - consensually distributed intimate imagery as part of the 39

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9 training data. Recent reports noted that several commonly used GAI training datasets were found to 1 contain hundreds of known images of CSAM. Sexually e xplicit or o bscene content is also particularly 2 difficult to remove during model training due to detection challenges and wide dissemination across the 3 internet. Even when trained on “clean” data, increasingly capable GAI models can synthesize or produce 4 synthetic NCII and CSAM. Websites, mobile apps, and custom - built models that generate synthetic NCII 5 have moved rapidly from niche internet forums to mainstream, automated, and scaled online 6 businesses. 7 Generated explicit or obscene AI content may include highly - realistic “deepfakes” of real individuals , 8 including children. For example, non - consensual AI - generated intimate images of a prominent 9 entertainer flooded social media and attracted hundreds of millions of views. 10 Toxicity , Bias , and Homogenization 11 Toxicity in this context refers to negative, disrespectful, or unreasonable content or language that can be 12 created by or intentionally programmed into GAI systems. Difficulty controlling the creation of and public 13 exposure to toxic, hate - promoting or hate speech , and denigrating or stereotypical content generated by 14 AI can lead to representational harms . For example, bias in word embeddings used by multimodal AI 15 models under - represent women when prompted to generate images of CEOs, doctors, lawyers, and 16 judges. Bias in GAI models or training data can also harm representation or preserve or exacerbate racial 17 bias , separately or in addition to toxic ity . 18 Toxicity and bias can also lead to homogenization or other undesirable outcomes. Homogenization in GAI 19 outputs can result in similar aesthetic styles , reduced content diversity , and the promotion of select 20 opinions or values at scale. These phenomena might arise from the inherent biases of foundation 21 models, which could create “ bottlenecks , ” or singular points of failure of discrimination or exclusion that 22 replicate to many downstream applications. 23 The related concern of model collapse , whe n GAI models are trained on generated data or outputs from 24 previous models, result s in the disappearance of outliers or u nique data points in the dataset or 25 distribution. Model collapse can stem from uniform feedback loops or training on synthetic data. Model 26 collapse c ould lead to undesired homogenization of outputs, which poses a threat to specific groups and 27 to the robustness of the model overall. Other biases of GAI systems can result in the unfair distribution 28 of capabilities or benefits from model access. Model capabilities and outcomes may be worse for some 29 groups compared to others , such as reduced LLM performance for non - English languages . Reduced 30 performance for non - English languages presents risks for model adoption, inclusion, and accessibility, 31 and could have downstream impacts on the preservation of the language, particularly for endangered 32 languages. 33 Value Chain and Component Integration 34 GAI system value chains often involve many third - party components such as procured datasets, pre - 35 trained models, and software libraries. These components might be improperly obtained or not properly 36 vetted, leading to diminished transparency or accountability for downstream users. For example, a 37 model might be trained on unverified content from third - party sources, which could result in unverifiable 38

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10 model outputs. Because GAI systems often involve many different third - party components, it may be 1 difficult to attribute issues in a system’s behavior to any one of these sources. 2 Some third - party components, such as “benchmark” datasets , may also gain credibility only from high - 3 usage , rather than quality, and may feature issues surfaced only when properly vetted. 4 3. Actions to Manage GAI Risks 5 Actions to manage GAI risks can be found in the tables below, organized by AI RMF subcategory . Each 6 action is related to a specific subcategory of the AI RMF, but not every subcategory of the AI RMF is 7 included in this document. Therefore, actions exist for only some AI RMF subcategories. 8 Moreover, not all actions apply to all AI actors. For example, not actions relevant to GAI developers may 9 be relevant to GAI deployers. Organizations should prioritize actions based on their unique situations 10 and context for using GAI applications. 11 Some subcategories in the action tables below are marked as “foundational,” meaning they should be 12 treated as fundamental tasks for GAI risk management and should be considered as the minimum set of 13 actions to be taken. Subcategory actions considered foundational are indicated by an ‘*’ in the 14 subcategory title row. 15 Each action table includes: 16 • Action ID: A unique identifier for each relevant action tied to relevant AI RMF functions and 17 subcategories (e.g., GV - 1.1 - 001 corresponds to the first action for Govern 1.1.); 18 • Action: S teps an organization can take to manage GAI risks; 19 • GAI Risks: T ags linking the action with relevant GAI risks; 20 • Keywords: T ags linking keywords to the action, including relevant Trustworthy AI Characteristics 21 in AI RMF 1.0; 22 • AI Actors: P ertinent AI A ctor s and Actor Tasks . 23 Action tables begin with the AI RMF subcategory, shaded in blue, followed by relevant actions. Each 24 action ID corresponds to the relevant function and subfunction (e.g., GV - 1.1 - 001 corresponds to the first 25 action for Govern 1.1, GV - 1.1 - 002 corresponds to the second action for Govern 1.1). Actions are tagged 26 as follows: GV = Govern; MP = Map; MS = Measure; MG = Manage. 27 * GOVERN 1.1: Legal and regulatory requirements involving AI are understood, managed, and documented. Action ID Action Risks GV - 1.1 - 001 Align GAI use with applicable laws and policies, including those related to data privacy and the use, publication, or distribution of licensed, patented, trademarked, copyrighted, or trade secret material. Data Privacy, Intellectual Property GV - 1.1 - 002 Define and communicate organizational access to GAI through management, legal,

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11 and compliance functions. GV - 1.1 - 003 Disclose use of GAI to end users. Human AI Configuration GV - 1.1 - 004 Establish policies restricting the use of GAI in regulated dealings or applications across the organization where compliance with applicable laws and regulations may be infeasible. GV - 1.1 - 005 Establish policies restricting the use of GAI to create child sexual abuse materials (CSAM) or other nonconsensual intimate imagery. Obscene, Degrading, and/or Abusive Content , Toxicity, Bias, and Homogenization , Dangerous or Violent Recommendations GV - 1.1 - 006 Establish transparent acceptable use policies for GAI that address illegal use or applications of GAI. AI Actors: Governance and Oversight 1 * GOVERN 1.2: The characteristics of trustworthy AI are integrated into organizational policies, processes, procedures, and practices. Action ID Action Risks GV - 1.2 - 001 Connect new GAI policies, procedures, and processes to existing model, data, and IT governance and to legal, compliance, and risk functions. GV - 1.2 - 002 Consider factors such as internal vs. external use, narrow vs. broad application scope, fine - tuning and training data sources (i.e., grounding) when defining risk - based controls. GV - 1.2 - 003 Define acceptable use policies for GAI systems deployed by, used by, and used within the organization. GV - 1.2 - 004 Establish and maintain policies for individual and organizational accountability regarding the use of GAI. GV - 1.2 - 005 Establish policies and procedures for ensuring that harmful or illegal content, particularly CBRN information , CSAM, known NCII, nudity, and graphic violence, is not included in training data. CBRN Informatio n , Obscene, Degrading, and/or Abusive Content , Dangerous or Violent Recommendations GV - 1.2 - 006 Establish policies to define mechanisms for measuring the effectiveness of standard content provenance methodologies (e.g., cryptography, watermarking, steganography, etc.) and testing (including reverse engineering). Information Integrity

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12 GV - 1.2 - 007 Establish transparency policies and processes for documenting the origin of training data and generated data for GAI applications, including copyrights, licenses, and data privacy, to advance content provenance. Data Privacy, Information Integrity, Intellectual Property GV - 1.2 - 008 Update existing policies, procedures, and processes to control risks unique to or exacerbated by GAI. AI Actors: Governance and Oversight 1 * GOVERN 1.3: Processes, procedures, and practices are in place to determine the needed level of risk management activities based on the organization’s risk tolerance. Action ID Action Risks GV - 1.3 - 001 Consider the following, or similar, factors when updating or defining risk tiers for GAI: Abuses and risks to information integrity; Cadence of vendor releases and updates; D ata protection requirements; Dependencies between GAI and other IT or data systems; Harm in physical environments; Human review of GAI system outputs; Legal or regulatory requirements; Presentation of obscene, objectionable, toxic, invalid or untruthful ou tput; Psychological impacts to humans (e.g., anthropomorphization, algorithmic ave rsion, emotional entanglement); Immediate and long term impacts; Internal vs. external use; Unreliable decision making capabilities, validity, adaptability, and variability of GAI system performance over time. Information Integrity, Obscene, Degrading, and/or Abusive Content , Value Chain and Component Integration , Toxicity, Bias, and Homogenization , Dangerous or Violent Recommendations , CBRN Information GV - 1.3 - 002 Define acceptable uses for GAI systems, where some applications may be restricted. GV - 1.3 - 003 Increase cadence for internal audits to address any unanticipated changes in GAI technologies or applications. GV - 1.3 - 004 Maintain an updated hierarchy of identified and expected GAI risks connected to contexts of GAI use, potentially including specialized risk levels for GAI systems that address risks such as model collapse and algorithmic monoculture. Toxicity, Bias, and Homogenization GV - 1.3 - 005 Reevaluate organizational risk tolerances to account for broad GAI risks, including: Immature safety or risk cultures related to AI and GAI design, development and deployment, public information integrity risks, including impacts on democratic processes, u nknown long - term performance characteristics of GAI. Information Integrity, Dangerous or Violent Recommendations GV - 1.3 - 006 Tie expected GAI behavior to trustworthy characteristics. AI Actors: Governance and Oversight 2

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13 GOVERN 1.5: Ongoing monitoring and periodic review of the risk management process and its outcomes are planned, and organizational roles and responsibilities are clearly defined, including determining the frequency of periodic review. Action ID Action Risks GV - 1.5 - 001 Define organizational responsibilities for content provenance monitoring and incident response. Information Integrity GV - 1.5 - 002 Develop or review existing policies for authorization of third party plug - ins and verify that related procedures are able to be followed. Value Chain and Component Integration GV - 1.5 - 003 Establish and maintain policies and procedures for monitoring the effectiveness of content provenance for data and content generated across the AI system lifecycle. Information Integrity GV - 1.5 - 004 Establish organizational policies and procedures for after action reviews of GAI system incident response and incident disclosures, to identify gaps; Update incident response and incident disclosure processes as required. Human AI Configuration GV - 1.5 - 005 Establish policies for periodic review of organizational monitoring and incident response plans based on impacts and in line with organizational risk tolerance. Information Security, Confabulation GV - 1.5 - 006 Maintain a long term document retention policy to keep full history for auditing, investigation, or improving content provenance methods. Information Integrity GV - 1.5 - 007 Verify information sharing and feedback mechanisms among individuals and organizations regarding any negative impact from AI systems due to content provenance issues. Information Integrity GV - 1.5 - 008 Verify that review procedures include analysis of cascading impacts of GAI system outputs used as inputs to third party plug - ins or other systems. Value Chain and Component Integration AI Actors: Governance and Oversight, Operation and Monitoring 1 * GOVERN 1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities. Action ID Action Risks GV - 1.6 - 001 Define any inventory exemptions for GAI systems embedded into application software in organizational policies. GV - 1.6 - 002 Enumerate organizational GAI systems for incorporation into AI system inventory and adjust AI system inventory requirements to account for GAI risks. GV - 1.6 - 003 In addition to general model, governance, and risk information, consider the following items in GAI system inventory entries: Acceptable use policies and policy Data Privacy, Human AI Configuration, Information

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14 exceptions; Application, Assumptions and limitations of use, including enumeration of restricted uses; Business or model owners; Challenges for explainability, interpretability, or transparency; Change management, maintenance, and monitoring plans; Connecti ons or dependencies between other systems; Consent information and notices; Data provenance information (e.g., source, signatures, versioning, watermarks); Designation of in - house or third party development; Designation of risk level; Disclosure informati on or notices; Incident response plans; Known issues reported from internal bug tracking or external information sharing resources (e.g., AI incident database, AVID, CVE, or OECD incident monitor); Human oversight roles and responsibilities; Special rights and considerations for intellectual property, licensed works, or personal, privileged, proprietary or sensitive data; Time frame for valid deployment, including date of last risk assessment; Underlying foundation models, versions of underlying models, and access modes; Updated hierarchy of identified and expected risks connected to contexts of use. Integrity, Intellectual Property, Value Chain and Component Integration GV - 1.6 - 004 Inventory recently decommissioned systems, systems with imminent deployment plans, and operational systems. GV - 1.6 - 005 Update policy definitions for AI systems, models, qualitative tools or similar to account for GAI systems. AI Actors: Governance and Oversight 1 GOVERN 1.7: Processes and procedures are in place for decommissioning and phasing out AI systems safely and in a manner that does not increase risks or decrease the organization’s trustworthiness. Action ID Action Risks GV - 1.7 - 001 Allocate time and resources for staged decommissioning for GAI to avoid service disruptions. GV - 1.7 - 002 Communicate decommissioning and support plans for GAI systems to AI actors and users through various channels and maintain communication and associated training protocols. Human AI Configuration GV - 1.7 - 003 Consider the following factors when decommissioning GAI systems: Clear versioning of decommissioned and replacement systems; Contractual, legal, or regulatory requirements; Data retention requirements; Data security, e.g., Containment, protocols, Data leakage after decommissioning; Dependencies between upstream, downstream, or other data, internet of things (IOT) or AI systems; Digital and physical artifacts; Recourse mechanisms for impacte d users or communities; Termination of related cloud or vendor services; Users’ emotional entanglement with GAI functions. Human AI Configuration, Information Security, Value Chain and Component Integration

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15 GV - 1.7 - 004 Implement data security and privacy controls for stored decommissioned GAI systems. Data Privacy, Information Security GV - 1.7 - 005 Update existing policies (e.g., enterprise record retention policies) or establish new policies for the decommissioning of GAI systems. AI Actors: AI Deployment, Operation and Monitoring 1 * GOVERN 2.1: Roles and responsibilities and lines of communication related to mapping, measuring, and managing AI risks are documented and are clear to individuals and teams throughout the organization. Action ID Action Risks GV - 2.1 - 001 Define acceptable use cases and context under which the organization will design, develop, deploy, and use GAI systems. GV - 2.1 - 002 Establish policies and procedures for GAI risk acceptance to downstream AI actors. Human AI Configuration, Value Chain and Component Integration GV - 2.1 - 003 Establish policies to identify and disclose GAI system incidents to downstream AI actors, including individuals potentially impacted by GAI outputs. Human AI Configuration, Value Chain and Component Integration GV - 2.1 - 004 Establish procedures to engage teams for GAI system incident response with diverse composition and responsibilities based on the particular incident type. Toxicity, Bias, and Homogenization GV - 2.1 - 005 Establish processes to identify GAI system incidents and verify the AI actors conducting these tasks demonstrate and maintain the appropriate skills and training. Human AI Configuration GV - 2.1 - 006 Verify that incident disclosure plans include sufficient GAI system context to facilitate remediation actions. Human AI Configuration AI Actors: Governance and Oversight 2 * GOVERN 3.2: Policies and procedures are in place to define and differentiate roles and responsibilities for human - AI configurations and oversight of AI systems. Action ID Action Risks GV - 3.2 - 001 Bolster oversight of GAI systems with independent audits or assessments, or by the application of authoritative external standards.

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16 GV - 3.2 - 002 Consider adjustment of organizational roles and components across lifecycle stages of large or complex GAI systems, including: AI actor, user, and community feedback relating to GAI systems; Audit, validation, and red - teaming of GAI systems; GAI content moderation; Data documentation, labeling, preprocessing and tagging; Decommissioning GAI systems; Decreasing risks of emotional entanglement between users and GAI systems; Decreasing risks of deception by GAI systems; Discouraging anonymous use of GAI systems; Enhancing explainability of GAI systems; GAI system development and engineering; Increased accessibility of GAI tools, interfaces, and systems, Incident r esponse and containment; Overseeing relevant AI actors and digital entities, including management of security credentials and communication between AI entities; Training GAI users within an organization about GAI fundamentals and risks. Human AI Configuration, Information Security, Toxicity, Bias, and Homogenization GV - 3.2 - 003 Define acceptable use policies for the various categories of GAI interfaces, modalities, and human - AI configurations. Human AI Configuration GV - 3.2 - 004 Define policies for the design of systems that possess human decision - making powers. Human AI Configuration GV - 3.2 - 005 Establish policies for user feedback mechanisms in GAI systems. Human AI Configuration GV - 3.2 - 006 Establish policies to empower accountable executives to oversee GAI system adoption, use, and decommissioning. GV - 3.2 - 007 Establish processes to include and empower interdisciplinary team member perspectives across the AI lifecycle. Toxicity, Bias, and Homogenization GV - 3.2 - 008 Evaluate AI actor teams in consideration of credentials, demographic representation, interdisciplinary diversity, and professional qualifications. Human AI Configuration, Toxicity, Bias, and Homogenization AI Actors: AI Design 1

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17 * GOVERN 4.1: Organizational policies and practices are in place to foster a critical thinking and safety - first mindset in the design, development, deployment, and uses of AI systems to minimize potential negative impacts. Action ID Action Risks GV - 4.1 - 001 Establish criteria and acceptable use policies for the use of GAI in decision making tasks in accordance with organizational risk tolerance, and other policies laid out in the Govern function; to include detailed criteria for the kinds of queries GAI model s should refuse to respond to. Human AI Configuration GV - 4.1 - 002 Establish policies and procedures that address continual improvement processes for risk measurement: Address general risks associated with a lack of explainability and transparency in GAI systems by using ample documentation and techniques such as: application of gradient - based attributions, occlusion/term reduction, counterfactual prompts and prompt engineering, and analysis of embeddings; Assess and update risk measure ment approaches at regular cadences. GV - 4.1 - 003 Establish policies, procedures, and processes detailing risk measurement in context of use with standardized measurement protocols and structured public feedback exercises such as AI red - teaming or independent external audits. GV - 4.1 - 004 Establish policies, procedures, and processes for oversight functions (e.g., senior leadership, legal, compliance, and risk) across the GAI lifecycle, from problem formulation and supply chains to system decommission. Value Chain and Component Integration GV - 4.1 - 005 Establish policies, procedures, and processes that promote effective challenge of AI system design, implementation, and deployment decisions via mechanisms such as three lines of defense, to minimize risks arising from workplace culture (e.g., confirmation bias, funding bias, groupthink, over - reliance on metrics). Toxicity, Bias, and Homogenization GV - 4.1 - 006 Incorporate GAI governance policies into existing incident response, whistleblower, vendor or investment due diligence, acquisition, procurement, reporting or internal audit policies. Value Chain and Component Integration AI Actors: AI Deployment, AI Design, AI Development, Operation and Monitoring 1 * GOVERN 4.2: Organizational teams document the risks and potential impacts of the AI technology they design, develop, deploy, evaluate, and use, and they communicate about the impacts more broadly. Action ID Action Risks

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18 GV - 4.2 - 001 Develop policies, guidelines, and practices for monitoring organizational and third - party impact assessments (data, labels, bias, privacy, models, algorithms, errors, provenance techniques, security, legal compliance, output, etc.) to mitigate risk and harm. Confabulation, Data Privacy, Information Integrity, Information Security, Value Chain and Component Integration , Toxicity, Bias, and Homogenization , Dangerous or Violent Recommendations GV - 4.2 - 002 Establish clear roles and responsibilities for inter - organizational incident response and communication for GAI systems that involve multiple organizations involved in different aspects of the GAI system lifecycle. GV - 4.2 - 003 Establish clearly defined terms of use and terms of service. Intellectual Property GV - 4.2 - 004 Establish criteria for ad - hoc impact assessments based on incident reporting or new use cases for the GAI system. GV - 4.2 - 005 Establish organizational roles, policies, and procedures for communicating and reporting GAI system risks and terms of use or service, relevant for different AI actors. Human AI Configuration, Intellectual Property GV - 4.2 - 006 Establish policies and procedures to document new ways AI actors interact with the GAI system. Human AI Configuration GV - 4.2 - 007 Establish policies and procedures to monitor compliance with established terms of service and use. Intellectual Property GV - 4.2 - 008 Establish policies to align organizational and third - party assessments with regulatory and legal compliance regarding content provenance. Information Integrity, Value Chain and Component Integration GV - 4.2 - 009 Establish policies to incorporate adversarial examples and other provenance attacks in AI model training processes to enhance resilience against attacks. Information Integrity, Information Security GV - 4.2 - 010 Establish processes to monitor and identify misuse, unforeseen use cases, risks of the GAI system and potential impacts of those risks (leveraging GAI system use case inventory). CBRN Information, Confabulation, Dangerous or Violent Recommendations GV - 4.2 - 011 Implement standardized documentation of GAI system risks and potential impacts. GV - 4.2 - 012 Include relevant AI Actors in the GAI system risk identification process. Human AI Configuration GV - 4.2 - 013 Verify that downstream GAI system impacts (such as the use of third - party plug - ins) are included in the impact documentation process. Value Chain and Component Integration

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19 GV - 4.2 - 014 Verify that the organizational list of risks related to the use of the GAI system are updated based on unforeseen GAI system incidents. AI Actors: AI Deployment, AI Design, AI Development, Operation and Monitoring 1 * GOVERN 4.3: Organizational practices are in place to enable AI testing, identification of incidents, and information sharing. Action ID Action Risks GV - 4.3 - 001 Allocate resources and adjust adoption, development, and implementation timeframes to enable independent measurement, continuous monitoring, and fulsome information sharing for GAI system risks. GV - 4.3 - 002 Develop standardized documentation templates for efficient review of risk measurement results. GV - 4.3 - 003 Establish minimum thresholds for performance and review as part of deployment approval (“go/”no - go”) policies, procedures, and processes, with reviewed processes and approval thresholds reflecting measurement of GAI capabilities and risks. GV - 4.3 - 004 Establish organizational roles, policies, and procedures for communicating GAI system incidents and performance to AI actors and downstream stakeholders, via community or official resources (e.g., AI Incident Database, AVID, AI Litigation Database, CVE, OECD Inciden t Monitor, or others). Human AI Configuration, Value Chain and Component Integration GV - 4.3 - 005 Establish policies and procedures for pre - deployment GAI system testing that validates organizational capability to capture GAI system incident reporting criteria. GV - 4.3 - 006 Establish policies, procedures, and processes that bolster independence of risk management and measurement functions (e.g., independent reporting chains, aligned incentives). GV - 4.3 - 007 Establish policies, procedures, and processes that enable and incentivize in - context risk measurement via standardized measurement and structured public feedback approaches. GV - 4.3 - 008 Organizational procedures identify the minimum set of criteria necessary for GAI system incident reporting such as: System ID (auto - generated most likely), Title, Reporter, System/Source, Data Reported, Date of Incident, Description, Impact(s), Stakeholder(s) Impacted. AI Actors: Fairness and Bias, Governance and Oversight, Operation and Monitoring, TEVV 2

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20 * GOVERN 5.1: Organizational policies and practices are in place to collect, consider, prioritize, and integrate feedback from those external to the team that developed or deployed the AI system regarding the potential individual and societal impacts related to AI risk s. Action ID Action Risks GV - 5.1 - 001 Allocate time and resources for outreach, feedback, and recourse processes in GAI system development. GV - 5.1 - 002 Disclose interactions with GAI systems to users prior to interactive activities. Human AI Configuration GV - 5.1 - 003 Establish policy, guidelines and processes that: Engage independent experts to audit models, data sources, licenses, algorithms, and other system components, Consider sponsoring or engaging in community - based exercises (e.g., bug bounties, hackathons, competitions) where AI Actors assess and benchmark the performance of AI systems, including the robustness of content provenance management under various conditions; Document data sources, licenses, training methodologies, and trade - offs considered in the design of AI systems; Establish mechanisms, platforms or channels (e.g., user interfaces, web portals, forums) for independent experts, users, or community members to provide feedback related to AI systems; Adjudicate and i mplement relevant feedback at a regular cadence, Establish transparency mechanisms to track the origin of data and generated content; Audit and validate these mechanisms. Human AI Configuration, Information Integrity, Intellectual Property GV - 5.1 - 004 Establish processes to bolster internal AI actor culture in alignment with organizational principles and norms and to empower exploration of GAI limitations beyond development settings. Human AI Configuration, Toxicity, Bias, and Homogenization GV - 5.1 - 005 Establish the following GAI - specific policies and procedures for independent AI Actors: Continuous improvement processes for increasing explainability and mitigating other risks; Impact assessments, Incentives for internal AI actors to provide feedback and conduct independent risk management activities; Independent management and reporting structures for AI actors engaged in model and system audit, validation, and oversight; TEVV processes for the effectiveness of feedback mechanisms employing participation rates, resolution time, or similar measurements. Human AI Configuration GV - 5.1 - 006 Provide thorough instructions for GAI system users to provide feedback and understand recourse mechanisms. Human AI Configuration GV - 5.1 - 007 Standardize user feedback about GAI system behavior, risks and limitations for efficient adjudication and incorporation. Human AI Configuration AI Actors: AI Design, AI Impact Assessment, Affected Individuals and Communities, Governance and Oversight 1

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21 * GOVERN 6.1: Policies and procedures are in place that address AI risks associated with third - party entities, including risks of infringement of a third - party’s intellectual property or other rights. Action ID Action Risks GV - 6.1 - 001 Categorize different types of GAI content with associated third party risks (i.e., copyright, intellectual property, data privacy). Data Privacy, Intellectual Property, Value Chain and Component Integration GV - 6.1 - 002 Conduct due diligence on third - party entities and end - users from those entities before entering into agreements with them (e.g., checking references, reviewing their content handling processes, etc.). Human AI Configuration, Value Chain and Component Integration GV - 6.1 - 003 Conduct joint educational activities and events in collaboration with third - parties to promote content provenance best practices. Information Integrity, Value Chain and Component Integration GV - 6.1 - 004 Conduct regular audits of third - party entities to ensure compliance with contractual agreements. Value Chain and Component Integration GV - 6.1 - 005 Define and communicate organizational roles and responsibilities for GAI acquisition, human resources, procurement, and talent management processes in policies and procedures. Human AI Configuration GV - 6.1 - 006 Develop an incident response plan for third parties specifically tailored to address content provenance incidents or breaches and regularly test and update the incident response plan with feedback form external and third party stakeholders. Data Privacy, Information Integrity, Information Security, Value Chain and Component Integration GV - 6.1 - 007 Develop and validate approaches for measuring the success of content provenance management efforts with third parties (e.g., incidents detected and response times). Information Integrity, Value Chain and Component Integration GV - 6.1 - 008 Develop risk tolerance and criteria to quantitatively assess and compare the level of risk associated with different third - party entities (i.e., reputation, track record, security measure, and the sensitivity of the content they handle). Information Security, Value Chain and Component Integration GV - 6.1 - 009 Draft and maintain well - defined contracts and service level agreements (SLAs) that specify content ownership, usage rights, quality standards, security requirements, and content provenance expectations. Information Integrity, Information Security GV - 6.1 - 010 Establish processes to maintain awareness of evolving risks, technologies, and best practices in content provenance management. Information Integrity

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22 GV - 6.1 - 011 Implement a supplier risk assessment framework to continuously evaluate and monitor third - party entities’ performance and adherence to content provenance standards and technologies (e.g., digital signatures, watermarks, cryptography, etc.) to detect anomalies and unauthorized changes; services acquisition and supply chain risk management; legal compliance (e.g ., copyright, trademarks, and data privacy laws). Data Privacy, Information Integrity, Information Security, Intellectual Property, Value Chain and Component Integration GV - 6.1 - 012 Include audit clauses in contracts that allow the organization to verify compliance with content provenance requirements. Information Integrity GV - 6.1 - 013 Inventory all third - party entities with access to organizational content and establish approved GAI technology and service provider lists. Value Chain and Component Integration GV - 6.1 - 014 Maintain detailed records of content provenance, including sources, timestamps, metadata, and any changes made by third parties. Information Integrity, Value Chain and Component Integration GV - 6.1 - 015 Provide proper training to internal employees on content provenance best practices, risks, and reporting procedures. Information Integrity GV - 6.1 - 016 Update and integrate due diligence processes for GAI acquisition and procurement vendor assessments to include intellectual property, data privacy, security, and other risks. For example, update policies to: Address robotic process automation (RPA), software - as - a - service (SAAS), and other solutions that may rely on embedded GAI technologies; Address ongoing audits, assessments, and alerting, dynamic risk assessments, and real - time reporting tools for monitoring third - party GAI risks; Address accessibility, accommodations, or opt - outs in GAI vendor offerings; Address commercial use of GAI outputs and secondary use of collected data by third parties; Assess vendor risk controls for intellectual property infringements and data privacy; Consider policy adjustments across GAI modeling libraries, tools and APIs, fine - tuned models, and embedded tools; Establish ownership of GAI acquisition and procurement processes; Include relevant organizational functions in evaluations of GAI third parties (e.g., legal, information technology (IT), security, privacy, fair lending); Include instruction on intellectual property infringement and other third - party GAI risks in GAI training for AI actors; Screen GAI vendors, open source or proprietary GAI tools, or GAI service providers against incident or vulnerability databases; Screen open source or proprietary GAI training data or outputs a gainst patents, copyrights, trademarks and trade secrets. Data Privacy, Human AI Configuration, Information Security, Intellectual Property, Value Chain and Component Integration , Toxicity, Bias, and Homogenization GV - 6.1 - 017 Update GAI acceptable use policies to address proprietary and open - source GAI technologies and data, and contractors, consultants, and other third - party personnel. Intellectual Property, Value Chain and Component Integration GV - 6.1 - 018 Update human resource and talent management standards to address acceptable use of GAI. Human AI Configuration

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23 GV - 6.1 - 019 Update third - party contracts, service agreements, and warranties to address GAI risks; Contracts, service agreements, and similar documents may include GAI - specific indemnity clauses, dispute resolution mechanisms, and other risk controls. Value Chain and Component Integration AI Actors: Operation and Monitoring, Procurement, Third - party entities 1 GOVERN 6.2: Contingency processes are in place to handle failures or incidents in third - party data or AI systems deemed to be high - risk. Action ID Action Risks GV - 6.2 - 001 Apply existing organizational risk management policies, procedures, and documentation processes to third - party GAI data and systems, including open source data and software. Intellectual Property, Value Chain and Component Integration GV - 6.2 - 002 Document downstream GAI system impacts (e.g., the use of third - party plug - ins) for third party dependencies. Value Chain and Component Integration GV - 6.2 - 003 Document GAI system supply chain risks to identify over - reliance on third party data or GAI systems and to identify fallbacks. Value Chain and Component Integration GV - 6.2 - 004 Document incidents involving third - party GAI data and systems, including open source data and software. Intellectual Property, Value Chain and Component Integration GV - 6.2 - 005 Enumerate organizational GAI system risks based on external dependencies on third - party data or GAI systems. Value Chain and Component Integration GV - 6.2 - 006 Establish acceptable use policies that identify dependencies, potential impacts, and risks associated with third - party data or GAI systems deemed high - risk. Value Chain and Component Integration GV - 6.2 - 007 Establish contingency and communication plans to support fallback alternatives for downstream users in the event the GAI system is disabled. Human AI Configuration, Value Chain and Component Integration GV - 6.2 - 008 Establish incident response plans for third - party GAI technologies deemed high - risk: Align incident response plans with impacts enumerated in MAP 5.1; Communicate third - party GAI incident response plans to all relevant AI actors; Define ownership of GAI incident response functions; Rehearse third - party GAI incident response plans at a regular cadence; Improve incident response plans based on retrospective learning; Review incident response plans for alignment with relevant breach r eporting, data protection, data privacy, or other laws. Data Privacy, Human AI Configuration, Information Security, Value Chain and Component Integration , Toxicity, Bias, and Homogenization GV - 6.2 - 009 Establish organizational roles, policies, and procedures for communicating with data and GAI system providers regarding performance, disclosure of GAI system inputs, and use of third - party data and GAI systems. Human AI Configuration, Value Chain and Component Integration

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24 GV - 6.2 - 010 Establish policies and procedures for continuous monitoring of third - party GAI systems in deployment. Value Chain and Component Integration GV - 6.2 - 011 Establish policies and procedures that address GAI data redundancy, including model weights and other system artifacts. Toxicity, Bias, and Homogenization GV - 6.2 - 012 Establish policies and procedures to test and manage risks related to rollover and fallback technologies for GAI systems, acknowledging that rollover and fallback may include manual processing. GV - 6.2 - 013 Identify and document high - risk third - party GAI technologies in organizational AI inventories, including open - source GAI software. Intellectual Property, Value Chain and Component Integration GV - 6.2 - 014 Review GAI vendor documentation for thorough instructions, meaningful transparency into data or system mechanisms, ample support and contact information, and alignment with organizational principles. Value Chain and Component Integration , Toxicity, Bias, and Homogenization GV - 6.2 - 015 Review GAI vendor release cadences and roadmaps for irregularities and alignment with organizational principles. Value Chain and Component Integration , Toxicity, Bias, and Homogenization GV - 6.2 - 016 Review vendor contracts and avoid arbitrary or capricious termination of critical GAI technologies or vendor services and Non - standard terms that may amplify or defer liability in unexpected ways and Unauthorized data collection by vendors or third - parties (e.g., secondary data use); Consi der: Clear assignment of liability and responsibility for incidents, GAI system changes over time (e.g., fine - tuning, drift, decay); Request: Notification and disclosure for serious incidents arising from third - party data and systems, Service line agreemen ts (SLAs) in vendor contracts that address incident response, response times, and availability of critical support. Human AI Configuration, Information Security, Value Chain and Component Integration AI Actors: AI Deployment, Operation and Monitoring, TEVV, Third - party entities 1 *MAP 1.1: Intended purposes, potentially beneficial uses, context specific laws, norms and expectations, and prospective settings in which the AI system will be deployed are understood and documented. Considerations include: the specific set or types of user s along with their expectations; potential positive and negative impacts of system uses to individuals, communities, organizations, society, and the planet; assumptions and related limitations about AI system purposes, uses, and risks across the development or pr oduct AI lifecycle; and related TEVV and system metrics. Action ID Action Risks MP - 1.1 - 001 Apply risk mapping and measurement plans to third - party and open - source systems. Intellectual Property, Value Chain and Component Integration

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25 MP - 1.1 - 002 Collaborate with domain experts to explore and document gaps, limitations, and risks in pre - deployment testing and the practical and contextual differences between pre - deployment testing and the anticipated context(s) of use. MP - 1.1 - 003 Conduct impact assessments or review past known incidents and failure modes to prioritize and inform risk measurement. MP - 1.1 - 004 Determine and document the expected and acceptable GAI system context of use in collaboration with socio - cultural and other domain experts, by assessing: Assumptions and limitations; Direct value to the organization; Intended operational environment and observed usage patterns; Potential positive and negative impacts to individuals, public safety, groups, communities, organizati ons, democratic institutions, and the physical environment; Social norms and expectations. Toxicity, Bias, and Homogenization MP - 1.1 - 005 Document GAI system ownership, intended use, direct organizational value, and assumptions and limitations. MP - 1.1 - 006 Document risk measurement plans that address: Individual and group cognitive biases (e.g., confirmation bias, funding bias, groupthink) for AI actors involved in the design, implementation, and use of GAI systems; Known past GAI system incidents and failure modes; In - context use and foreseeable misuse, abuse, and off - label use; Over reliance on quantitative metrics and methodologies without sufficient awareness of their limitations in the context(s) of use; Risks associated wi th trustworthy characteristics across the AI lifecycle; Standard measurement and structured human feedback approaches; Anticipated human - AI configurations. Human AI Configuration, Toxicity, Bias, and Homogenization, Dangerous or Violent Recommendations MP - 1.1 - 007 Document risks related to transparency, accountability, explainability, and interpretability in risk measurement plans, system risk assessments, and deployment approval (“go”/”no - go”) decisions. MP - 1.1 - 008 Document system requirements, ownership, and AI actor roles and responsibilities for human oversight of GAI systems. Human AI Configuration MP - 1.1 - 009 Document the extent to which a lack of transparency or explainability impedes risk measurement across the AI lifecycle. MP - 1.1 - 010 Identify and document foreseeable illegal uses or applications that surpass organizational risk tolerances. AI Actors: AI Deployment 1

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26 *MAP 1.2: Interdisciplinary AI actors, competencies, skills, and capacities for establishing context reflect demographic diversity and broad domain and user experience expertise, and their participation is documented. Opportunities for interdisciplinary collaborati on are prioritized. Action ID Action Risks MP - 1.2 - 001 Document the credentials and qualifications of organizational AI actors and AI actor team composition. Human AI Configuration MP - 1.2 - 002 Establish and empower interdisciplinary teams that reflect a wide range of capabilities, competencies, demographic groups, domain expertise, educational backgrounds, lived experiences, professions, and skills across the enterprise to inform and conduct TEVV of GAI technology, and other risk measurement and management functions. Human AI Configuration, Toxicity, Bias, and Homogenization MP - 1.2 - 003 Establish continuous improvement processes to increase diversity and representativeness in AI actor teams, standard measurement resources, and structured public feedback participants from subgroup populations in - context. Human AI Configuration, Toxicity, Bias, and Homogenization MP - 1.2 - 004 Verify that AI actor team membership includes demographic diversity, applicable domain expertise, varied education backgrounds, and lived experiences. Human AI Configuration, Toxicity, Bias, and Homogenization MP - 1.2 - 005 Verify that data or benchmarks used in risk measurement, and users, participants, or subjects involved in structured public feedback exercises are representative of diverse in - context user populations. Human AI Configuration, Toxicity, Bias, and Homogenization AI Actors: AI Deployment 1 *MAP 2.1: The specific tasks and methods used to implement the tasks that the AI system will support are defined (e.g., classifiers, generative models, recommenders). Action ID Action Risks MP - 2.1 - 001 Define GAI system's task(s) that relate to content provenance, such as original content creation, media synthesis, or data augmentation while incorporating tracking measures. Information Integrity MP - 2.1 - 002 Establish known assumptions and practices for determining data origin and content lineage, for documentation and evaluation. Information Integrity MP - 2.1 - 003 Identify and document GAI task limitations that might impact the reliability or authenticity of the content provenance. Information Integrity

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27 MP - 2.1 - 004 Institute audit trails for data and content flows within the system, including but not limited to, original data sources, data transformations, and decision - making criteria. MP - 2.1 - 005 Review efficacy of content provenance techniques on a regular basis and update protocols as necessary. Information Integrity AI Actors: TEVV 1 MAP 2.2: Information about the AI system’s knowledge limits and how system output may be utilized and overseen by humans is documented. Documentation provides sufficient information to assist relevant AI actors when making decisions and taking subsequent actions. Action ID Action Risks MP - 2.2 - 001 Assess whether the GAI system fulfills its intended purpose within its operational context on a regular basis. MP - 2.2 - 002 Evaluate whether GAI operators and end - users can accurately understand content lineage and origin. Human AI Configuration, Information Integrity MP - 2.2 - 003 Identify and document how the system relies on upstream data sources for content provenance and if it serves as an upstream dependency for other systems. Information Integrity, Value Chain and Component Integration MP - 2.2 - 004 Observe and analyze how the AI system interacts with external networks, and identify any potential for negative externalities, particularly where content provenance might be compromised. Information Integrity MP - 2.2 - 005 Specify the environments where GAI systems may not function as intended related to content provenance. Information Integrity AI Actors: End Users 2 *MAP 2.3: Scientific integrity and TEVV considerations are identified and documented, including those related to experimental design, data collection and selection (e.g., availability, representativeness, suitability), system trustworthiness, and cons truct validati on Action ID Action Risks

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28 MP - 2.3 - 001 Assess the accuracy, quality, reliability, and authenticity of the GAI content provenance by comparing it to a set of known ground truth data and by using a variety of evaluation methods (e.g., human oversight and automated evaluation). Information Integrity MP - 2.3 - 002 Curate and maintain high quality datasets that are accurate, relevant, consistent, and representative as well as be well - documented complying with ethical and legal standards along with diverse data points. Toxicity, Bias, and Homogenization MP - 2.3 - 003 Deploy and document fact - checking techniques to verify the accuracy and veracity of information generated by GAI systems, especially when the information comes from multiple (or unknown) sources. Information Integrity MP - 2.3 - 004 Design GAI systems to support content provenance such as tracking the lineage (e.g., data sources used to train the system, parameters used to generate content, etc.) and to verify authenticity (e.g., using digital signatures or watermarks). Information Integrity MP - 2.3 - 005 Develop and implement testing techniques to identify any GAI produced content (e.g., synthetic media) that might be indistinguishable from human - generated content. Information Integrity MP - 2.3 - 006 Document GAI content provenance techniques (including experimental methods), testing, evaluation, performance, and validation metrics throughout the AI lifecycle. Information Integrity MP - 2.3 - 007 Implement plans for GAI systems to undergo regular adversarial testing to identify vulnerabilities and potential manipulation risks. Information Security MP - 2.3 - 008 Integrate GAI systems with existing content management and version control systems, to enable content provenance to be tracked across the lifecycle. Information Integrity MP - 2.3 - 009 Test GAI models using known inputs, context, and environment to confirm they produce expected outputs across a variety of methods (e.g., unit tests, integration tests, and system tests) and help to identify and address potential problems. MP - 2.3 - 010 Use diverse large - scale and small - scale datasets for testing and evaluation to ensure that the AI system can perform well on a variety of different types of data. Toxicity, Bias, and Homogenization MP - 2.3 - 011 Verify that GAI content provenance is accurate and reliable by using cryptographic techniques and performing formal audits to ensure it has not been manipulated. Information Integrity

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29 MP - 2.3 - 012 Verify that the AI system’s content provenance complies with relevant laws and regulations, such as legal infringement, terms and conditions, copyright and intellectual property rights, when using data sources and generating content. Information Integrity, Intellectual Property AI Actors: AI Development, Domain Experts, TEVV 1 MAP 3.4: Processes for operator and practitioner proficiency with AI system performance and trustworthiness – and relevant technical standards and certifications – are defined, assessed, and documented. Action ID Action Risks MP - 3.4 - 001 Adapt existing training programs to include modules on content provenance. Information Integrity MP - 3.4 - 002 Develop certification programs that test proficiency in managing AI risks and interpreting content provenance, relevant to specific industry and context. Information Integrity MP - 3.4 - 003 Delineate human proficiency tests from tests of AI capabilities. Human - AI Configuration MP - 3.4 - 004 Integrate human and other qualitative inputs to comprehensively assess content provenance. Information Integrity MP - 3.4 - 005 Ensure that output provided to operators and practitioners is both interactive and well - defined, incorporating content provenance data that can be easily interpreted for effective downstream decision - making. Information Integrity, Value Chain and Component Integration MP - 3.4 - 006 Establish and adhere to design principles that ensure safe and ethical operation, taking into account the interpretation of content provenance information. Information Integrity, Toxicity, Bias, and Homogenization, Dangerous or Violent Recommendations MP - 3.4 - 007 Implement systems to continually monitor and track the outcomes of human - AI collaborations for future refinement and improvements, integrating a focus on content provenance wherever applicable. Human AI Configuration, Information Integrity MP - 3.4 - 008 Involve the end - users, practitioners, and operators in AI system prototyping and testing activities. Make sure these tests cover various scenarios where content provenance could play a critical role, such as crisis situations or ethically sensitive contexts. Human AI Configuration, Information Integrity, Toxicity, Bias, and Homogenization MP - 3.4 - 009 Match the complexity of GAI system explanations and the provenance data to the level of the problem and contextual intricacy. Information Integrity AI Actors: AI Design, AI Development, Domain Experts, End - Users, Human Factors, Operation and Monitoring 2

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30 *MAP 4.1: Approaches for mapping AI technology and legal risks of its components – including the use of third - party data or software – are in place, followed, and documented, as are risks of infringement of a third party’s intellectual property or other rights. Action ID Action Risks MP - 4.1 - 001 Conduct audits on third - party processes and personnel including an examination of the third - party’s reputation. Value Chain and Component Integration MP - 4.1 - 002 Conduct periodic audits and monitor AI generated content for privacy risks; address any possible instances of sensitive data exposure. Data Privacy MP - 4.1 - 003 Consider using synthetic data as applicable to train AI models in place of real - world data to match the statistical properties of real - world data without disclosing personally identifiable information. MP - 4.1 - 004 Develop practices for periodic monitoring of GAI outputs for possible intellectual property infringements and other risks and implement processes for responding to potential intellectual property infringement claims. Intellectual Property MP - 4.1 - 005 Document all aspects of the AI development process including data sources, model architectures and training procedures to support reproduction of results, identify any potential problems, and implement mitigation strategies. MP - 4.1 - 006 Document compliance with legal requirements across the AI lifecycle, including copyright concerns, privacy protections. Data Privacy, Intellectual Property MP - 4.1 - 007 Document training data curation policies, including policies to verify that consent was obtained for the likeness or image of individuals. Obscene, Degrading, and/or Abusive Content MP - 4.1 - 008 Employ encryption techniques and proper safeguards to ensure secure data storage and transfer to protect data privacy. Data Privacy, Information Security, Dangerous or Violent Recommendations MP - 4.1 - 009 Establish policies for collection, retention, and minimum quality of data, in consideration of the following risks: Disclosure of CBRN information by removing CBRN information from training data, Use of Illegal or dangerous content; Training data imbalance across sub - groups by modality, such as languages for LLMs or skin tone for image generation; Leak of personally identifiable information, including facial likenesses of individuals unless consent is obtained for use of their images. CBRN Information, Intellectual Property, Toxicity, Bias, and Homogenization, Dangerous or Violent Recommendations , Data Privacy MP - 4.1 - 010 Implement bias mitigation approaches by addressing sources of bias in the training data and by evaluating AI models for bias periodically. Toxicity, Bias, and Homogenization MP - 4.1 - 011 Implement policies and practices defining how third - party intellectual property and training data will be used, stored, and protected. Intellectual Property, Value Chain and Component Integration

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31 MP - 4.1 - 012 Implement reproducibility techniques, including: share data publicly or privately using license and citation; develop code according to standard software practices; track and document experiments and results; manage the software environment and dependencies; utilize virtual environments, version control, and maintain a requirements document; manage models and artifacts; tracking AI model versions and documenting model details along with parameters and experimental results; document data management processes and establish a testing/validation process to maintain reliable results. Confabulation, Intellectual Property, Value Chain and Component Integration MP - 4.1 - 013 Re - evaluate models that were fine - tuned on top of third - party models. Value Chain and Component Integration MP - 4.1 - 014 Re - evaluate risks when adapting GAI models to new domains. MP - 4.1 - 015 Review service level agreements and contracts, including license agreements and any legal documents associated with the third - party intellectual properties, technologies, and services. Intellectual Property, Value Chain and Component Integration MP - 4.1 - 016 Use approaches to detect the presence of sensitive data in generated output text, image, video, or audio, and verify that the model will mask any detected sensitive data. Information Integrity MP - 4.1 - 017 Use trusted sources for training data that are licensed or open source and ensure that the entity has the legal right for the use of proprietary training data. Intellectual Property MP - 4.1 - 018 Apply strong anonymization and de - identification, and/or differential privacy techniques to protect the privacy of individuals in the training data. Data Privacy MP - 4.1 - 019 Verify that third - party models are in compliance with existing use licenses. Intellectual Property, Value Chain and Component Integration AI Actors: Governance and Oversight, Operation and Monitoring, Procurement, Third - party entities 1 *MAP 5.1: Likelihood and magnitude of each identified impact (both potentially beneficial and harmful) based on expected use, past uses of AI systems in similar contexts, public incident reports, feedback from those external to the team that developed or deployed t he AI system, or other data are identified and documented. Action ID Action Risks MP - 5.1 - 001 Apply TEVV practices for content provenance (e.g., probing a system's synthetic data generation capabilities for potential misuse or vulnerabilities using zero - knowledge proof approaches). Information Integrity, Information Security

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32 MP - 5.1 - 002 Assess and document risks related to content provenance. e.g., document the presence, absence, or effectiveness of tagging systems, cryptographic hashes, blockchain - based, or distributed ledger technology solutions that improve content tracking transparency and immutability. Information Integrity MP - 5.1 - 003 Consider GAI - specific mapped risks (e.g., complex security requirements, potential for emotional entanglement of users, large supply chains) in estimates for likelihood, magnitude of impact and risk. Human AI Configuration, Information Security, Value Chain and Component Integration MP - 5.1 - 004 Document estimates of likelihood, magnitude of impact, and risk for GAI systems in a central repository (e.g., organizational AI inventory.). MP - 5.1 - 005 Enumerate potential impacts related to content provenance, including best - case, average - case, and worst - case scenarios. Information Integrity MP - 5.1 - 006 Estimate likelihood of enumerated impact scenarios using past data or expert judgment, analysis of known public incidents, standard measurement, and structured human feedback results. CBRN Information, Dangerous or Violent Recommendations MP - 5.1 - 007 Measure risk as the product of estimated likelihood and magnitude of impact of a GAI outcome. MP - 5.1 - 008 Prioritize risk acceptance, management, or transfer activities based on risk estimates. MP - 5.1 - 009 Prioritize standard measurement and structured public feedback processes based on risk assessment estimates. MP - 5.1 - 010 Profile risks arising from GAI systems interacting with, manipulating, or generating content, and outlining known and potential vulnerabilities and the likelihood of their occurrence. Information Security MP - 5.1 - 011 Scope GAI applications narrowly to enable risk - based governance and controls. AI Actors: AI Deployment, AI Design, AI Development, AI Impact Assessment, Affected Individuals and Communities, End - Users, Operation and Monitoring 1

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33 MAP 5.2: Practices and personnel for supporting regular engagement with relevant AI actors and integrating feedback about positive, negative, and unanticipated impacts are in place and documented. Action ID Action Risks MP - 5.2 - 001 Determine context - based measures to identify if new impacts are present due to the GAI system, including regular engagements with downstream AI actors to identify and quantify new contexts of unanticipated impacts of GAI systems. Human AI Configuration, Value Chain and Component Integration MP - 5.2 - 002 Plan regular engagements with AI actors responsible for inputs to GAI systems, including third - party data and algorithms, to review and evaluate unanticipated impacts. Human AI Configuration, Value Chain and Component Integration MP - 5.2 - 003 Publish guidance for external AI actors to report unanticipated impacts of the GAI system and to engage with the organization in the event of GAI system impacts. Human AI Configuration AI Actors: AI Deployment, AI Design, AI Impact Assessment, Affected Individuals and Communities, Domain Experts, End - Users, Human Factors, Operation and Monitoring 1 *MEASURE 1.1: Approaches and metrics for measurement of AI risks enumerated during the MAP function are selected for implementation starting with the most significant AI risks. The risks or trustworthiness characteristics that will not – or cannot – be measured are pro perly documented. Action ID Action Risks MS - 1.1 - 001 Assess the effectiveness of implemented methods and metrics at an ongoing cadence as part of continuous improvement activities. MS - 1.1 - 002 Collaborate with multidisciplinary experts (e.g., in the fields of responsible use of GAI, cybersecurity, or digital forensics) to ensure the selected risk management approaches are robust and effective. Information Security; CBRN Information, Toxicity, Bias, and Homogenization MS - 1.1 - 003 Conduct adversarial role - playing exercises, AI red - teaming, or chaos testing to identify anomalous or unforeseen failure modes. Information Security, Unknowns MS - 1.1 - 004 Conduct traditional assessment or TEVV exercises to measure the prevalence of known risks in deployment contexts. MS - 1.1 - 005 Document GAI risk measurement or tracking approaches, including tracking of risks that cannot be easily measured before deployment (e.g., ecosystem - level risks or risks that unfold over longer time scales).

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34 MS - 1.1 - 006 Employ digital signatures and watermarking, blockchain technology, reverse image and video search, metadata analysis, steganalysis, and/or forensic analysis to trace the origin and modifications of digital content. Information Integrity MS - 1.1 - 007 Employ similarity metrics, tampering indicators, blockchain confirmation, metadata consistency, hidden data detection rate, source reliability, and consistency with known patterns to measure content provenance risks. Information Integrity MS - 1.1 - 008 Identify content provenance risks in the end - to - end AI supply chain, including risks associated with data suppliers, data annotators, R&D, joint ventures, academic or nonprofit projects/partners, third party vendors, and contractors. Information Integrity, Value Chain and Component Integration MS - 1.1 - 009 Identify potential content provenance risks and harms in GAI, such as misinformation or disinformation, deepfakes, including NCII, or tampered content. Enumerate and rank risks and/or harms based on their likelihood and potential impact, and determine how well provenance solutions address specific risks and/or harms. Information Integrity, Dangerous or Violent Recommendations , Obscene, Degrading, and/or Abusive Content MS - 1.1 - 010 Implement appropriate approaches and metrics for measuring AI - related content provenance the and the aforementioned risks and harms. Information Integrity, Dangerous or Violent Recommendations MS - 1.1 - 011 Integrate tools designed to analyze content provenance and detect data anomalies, verify the authenticity of digital signatures, and identify patterns associated with misinformation or manipulation. Information Integrity MS - 1.1 - 012 Invest in R&D capabilities to evaluate and implement novel methods and technologies for the measurement of AI - related risks in content provenance, toxicity, and CBRN. Information Integrity, CBRN Information, Obscene, Degrading, and/or Abusive Content MS - 1.1 - 013 Prioritize risk measurement according to risk severity as determined during mapping activities. MS - 1.1 - 014 Provide content provenance risk management education to AI actors, users, and stakeholders. Human AI Configuration, Information Integrity MS - 1.1 - 015 Track and document risks or opportunities related to content provenance that cannot be measured quantitatively, including explanations as to why some risks cannot be measured (e.g., due to technological limitations, resource constraints, or trustworthy considerations). Information Integrity MS - 1.1 - 016 Track the number of output data items that are accompanied by provenance information (e.g., watermarks, cryptographic tags). Information Integrity MS - 1.1 - 017 Track the number of training and input (e.g., prompts) data items that have provenance records and output data items that potentially infringe on intellectual property rights. Information Integrity, Intellectual Property

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35 MS - 1.1 - 018 Track the number of training and input data items covered by intellectual property rights (e.g., copyright, trademark, trade secret). Intellectual Property MS - 1.1 - 019 Validate the reliability and integrity of the original data and measure inherent dependence on training data and its quality. AI Actors: AI Development, Domain Experts, TEVV 1 *MEASURE 1.3: Internal experts who did not serve as front - line developers for the system and/or independent assessors are involved in regular assessments and updates. Domain experts, users, AI actors external to the team that developed or deployed the AI system, and aff ected communities are consulted in support of assessments as necessary per organizational risk tolerance Action ID Action Risks MS - 1.3 - 001 Define relevant groups of interest (e.g., demographic groups, subject matter experts, past experience with GAI technology) within the context of use as part of plans for gathering structured public feedback. Human AI Configuration, Toxicity, Bias, and Homogenization, CBRN MS - 1.3 - 002 Define sequence of actions for AI red - teaming exercises and accompanying necessary documentation practices. MS - 1.3 - 003 Define use cases, contexts of use, capabilities, and negative impacts where structured human feedback exercises, e.g., AI red - teaming, would be most beneficial for AI risk measurement and management based on the context of use. MS - 1.3 - 004 Develop a suite of suitable metrics to evaluate structured feedback results, informed by representative AI actors. Human AI Configuration, Toxicity, Bias, and Homogenization, CBRN MS - 1.3 - 005 Execute independent audit, AI red - teaming, impact assessments, or other structured human feedback processes in consultation with representative AI actors with expertise and familiarity in the context of use, and/or who are representative of the populations associated with the context of use. Human AI Configuration, Toxicity, Bias, and Homogenization, CBRN MS - 1.3 - 006 Identify and implement methods for post - hoc evaluation of the effectiveness of structured human feedback processes such as auditing, impact assessments, and AI red - teaming. MS - 1.3 - 007 Identify and implement methods for translating, evaluating, and integrating structured human feedback output into AI risk management processes, continuous improvement processes, and related organizational decision making. MS - 1.3 - 008 Identify criteria for determining when structured human feedback exercises are complete.

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36 MS - 1.3 - 009 Identify mechanisms and teams to evaluate or other structured human feedback outcomes. MS - 1.3 - 010 Recruit auditors, AI red - teams, and structured feedback participants in consideration of the linguistic, dialectal, and socio - cultural environment of the expected user base. Human AI Configuration MS - 1.3 - 011 Share structured feedback with relevant AI actors to address identified risks. Human AI Configuration MS - 1.3 - 012 Verify demographic diversity of identified subgroups in structured feedback exercises. Toxicity, Bias, and Homogenization MS - 1.3 - 013 Verify those conducting structured human feedback exercises are not directly involved in system development tasks for the same GAI model. AI Actors: AI Deployment, AI Development, AI Impact Assessment, Affected Individuals and Communities, Domain Experts, End - Users, Operation and Monitoring, TEVV 1 *MEASURE 2.2: Evaluations involving human subjects meet applicable requirements (including human subject protection) and are representative of the relevant population. Action ID Action Risks MS - 2.2 - 001 Assess and manage statistical biases related to GAI content provenance through techniques such as re - sampling, re - weighting, or adversarial training. Information Integrity, Information Security, Toxicity, Bias, and Homogenization MS - 2.2 - 002 Disaggregate evaluation metrics by demographic factors to identify any discrepancies in how content provenance mechanisms work across diverse populations. Information Integrity, Toxicity, Bias, and Homogenization MS - 2.2 - 003 Document how content provenance mechanisms are operated in the context of privacy and security including: Anonymize data to protect the privacy of human subjects; Remove any personally identifiable information (PII) to prevent potential harm or misuse. Data Privacy, Human AI Configuration, Information Integrity, Information Security, Dangerous or Violent Recommendations MS - 2.2 - 004 Employ techniques like chaos engineering and stakeholder feedback to evaluate the quality and integrity of data used in training and the provenance of AI - generated content. Information Integrity MS - 2.2 - 005 Identify biases present in the training data for downstream mitigation using available techniques (e.g., data visualization tools). Value Chain and Component Integration, Toxicity, Bias, and Homogenization

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37 MS - 2.2 - 006 Implement continuous monitoring of GAI system impacts to identify whether GAI outputs are equitable across various sub - populations. Seek active and direct feedback from affected communities to identify issues and improve GAI system fairness. Toxicity, Bias, and Homogenization MS - 2.2 - 007 Implement robust cybersecurity measures to protect both the research data, the GAI system and its content provenance from unauthorized access, breaches, or tampering and unauthorized disclosure of human subject information. Data Privacy, Human AI Configuration, Information Integrity, Information Security MS - 2.2 - 008 Obtain informed consent from human subject evaluation participants. Informed consent should include: the nature of the study, information about the use of GAI related to content provenance, its purpose, and potential implications. Data Privacy, Human AI Configuration, Information Integrity MS - 2.2 - 010 Practice responsible disclosure of findings and report discovered vulnerabilities or biases related to GAI systems and its content provenance. Information Integrity, Information Security, Toxicity, Bias, and Homogenization MS - 2.2 - 011 Provide human subjects with options to revoke their consent for future use of their data in GAI applications, particularly in content provenance aspects. Data Privacy, Human AI Configuration, Information Integrity MS - 2.2 - 012 Use Institutional Review Boards as applicable for evaluations that involve human subjects. Human AI Configuration MS - 2.2 - 013 Use techniques such as anonymization or differential privacy to minimize the risks associated with linking AI - generated content back to individual human subjects. Data Privacy, Human AI Configuration MS - 2.2 - 014 Verify accountability and fairness through documentation of the algorithms, parameters, and methodologies used in the evaluation to allow for external scrutiny. Toxicity, Bias, and Homogenization MS - 2.2 - 015 Verify that human subjects selected for evaluation are representative of the population for the relevant GAI use - case; Consider demographics such as age, gender, race, ethnicity, socioeconomic status, and geographical location to avoid biases in the AI system related to content provenance. Human AI Configuration, Information Integrity, Toxicity, Bias, and Homogenization MS - 2.2 - 016 Work in close collaboration with domain experts to understand the specific requirements and potential pitfalls related to content provenance in the GAI system's intended context of use. Information Integrity AI Actors: AI Development, Human Factors, TEVV 1

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38 *MEASURE 2.3: AI system performance or assurance criteria are measured qualitatively or quantitatively and demonstrated for conditions similar to deployment setting(s). Measures are documented. Action ID Action Risks MS - 2.3 - 001 Analyze differences between intended and actual population of users or data subjects, including likelihood for errors, incidents, or negative impacts. Confabulation, Human AI Configuration, Information Integrity MS - 2.3 - 002 Conduct field testing on sampled sub - populations prior to deployment to the entire population. MS - 2.3 - 003 Conduct TEVV in the operational environment in accordance with organizational policies and regulatory or disciplinary requirements (e.g., informed consent, institutional review board approval, human research protections, privacy requirements). Data Privacy MS - 2.3 - 004 Consider baseline model performance on suites of benchmarks when selecting a model for fine tuning. MS - 2.3 - 005 Evaluate claims of model capabilities using empirically validated methods. MS - 2.3 - 006 Include metrics measuring reporting rates for harmful or offensive content in field testing. Dangerous or Violent Recommendations MS - 2.3 - 007 Share results of pre - deployment testing with relevant AI actors, such as those with system release approval authority. Human AI Configuration MS - 2.3 - 008 Use disaggregated evaluation methods (e.g., by race, age, gender, ethnicity, ability, region) to improve granularity of AI system performance measures. MS - 2.3 - 009 Utilize a purpose - built testing environment such as NIST Dioptra to empirically evaluate GAI trustworthy characteristics. MS - 2.3 - 010 Verify that mechanisms to collect users’ feedback are visible and traceable. Human AI Configuration AI Actors: AI Deployment, TEVV 1 *MEASURE 2.5: The AI system to be deployed is demonstrated to be valid and reliable. Limitations of the generalizability beyond the conditions under which the technology was developed are documented. Action ID Action Risks

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39 MS - 2.5 - 001 Apply standard measurement and structured human feedback approaches to internally - developed and third - party GAI systems. Value Chain and Component Integration MS - 2.5 - 002 Avoid extrapolating GAI system performance or capabilities from narrow, non - systematic, and anecdotal assessments. MS - 2.5 - 003 Conduct security assessments and audits to measure the integrity of training data, system software, and system outputs. Information Security MS - 2.5 - 004 Document the construct validity of methodologies employed in GAI systems relative to their context of use. MS - 2.5 - 005 Document the extent to which human domain knowledge is employed to improve GAI system performance, via, e.g., RLHF, fine - tuning, content moderation, business rules. MS - 2.5 - 006 Establish metrics or KPIs to determine whether GAI systems meet minimum performance standards for reliability and validity. MS - 2.5 - 007 Measure, monitor, and document prevalence of erroneous GAI output content, system availability, and reproducibility of outcomes via field testing or other randomized controlled experiments. MS - 2.5 - 008 Review and verify sources and citations in GAI system outputs during pre - deployment risk measurement and ongoing monitoring activities. Confabulation MS - 2.5 - 009 Track and document instances of anthropomorphization (e.g., human images, mentions of human feelings, cyborg imagery or motifs) in GAI system interfaces. Human AI Configuration MS - 2.5 - 010 Track and document relevant version numbers, planned updates, hotfixes, and other GAI system change management information. MS - 2.5 - 011 Update standard train/test model evaluation processes for GAI systems. Consider: Unwanted or undocumented overlaps in train and TEVV data sources, including their negative spaces (i.e., what is not represented in both); Employing substring matching or embedding distance approaches to assess similarity across data partitions. MS - 2.5 - 012 Verify GAI system training data and TEVV data provenance, and that fine - tuning data is grounded. Information Integrity AI Actors: Domain Experts, TEVV 1

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40 *MEASURE 2.6: The AI system is evaluated regularly for safety risks – as identified in the MAP function. The AI system to be deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and it can fail safely, p articularly if made to operate beyond its knowledge limits. Safety metrics reflect system reliability and robustness, real - time monitoring, and response times for AI system failures. Action ID Action Risks MS - 2.6 - 001 Assess adverse impacts health and wellbeing impacts for supply chain or other AI actors that are exposed to obscene, toxic, or violent information during the course of GAI training and maintenance. Human AI Configuration, Obscene, Degrading, and/or Abusive Content, Value Chain and Component Integration, Dangerous or Violent Recommendations MS - 2.6 - 002 Assess levels of toxicity, intellectual property infringement, data privacy violations, obscenity, extremism, violence, or CBRN information in system training data. Data Privacy, Intellectual Property, Obscene, Degrading, and/or Abusive Content, Toxicity, Bias, and Homogenization, Dangerous or Violent Recommendations , CBRN Information MS - 2.6 - 003 Measure and document incident response times, system down times, and system availability: Perform standard measurement and structured human feedback on GAI systems to detect safety and reliability impacts and harms; Apply human subjects research protocols and other applicable safety controls when conducting A/B testing, AI red - teaming, focus groups, or human testbed measurements; Identify and document any applications related to robotics, RPA, and autonomous vehicles; Conduct AI red - teaming exercises to identify harms and impacts related to safety and validity, reliability, privacy, toxicity and other risks; Monitor high - risk GAI systems continually for safety and reliability risks once deployed; Monitor GAI systems to detect drift and anomalies relative to expected performance and training baselines. Data Privacy, Human AI Configuration, Toxicity, Bias, and Homogenization, Dangerous or Violent Recommendations MS - 2.6 - 004 Re - evaluate safety features of fine - tuned models when the risk of harm exceeds organizational risk tolerance. Dangerous or Violent Recommendations MS - 2.6 - 005 Review GAI system outputs for validity and safety: Review generated code to assess risks that may arise from unreliable downstream decision - making. Value Chain and Component Integration, Dangerous or Violent Recommendations MS - 2.6 - 006 Track and document past failed GAI system designs to inform risk measurement for safety and validity risks. Dangerous or Violent Recommendations MS - 2.6 - 007 Verify capabilities for limiting, pausing, updating, or terminating GAI systems quickly. MS - 2.6 - 008 Verify rollover, fallback, or redundancy capabilities for high - risk GAI systems.

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41 MS - 2.6 - 009 Verify that GAI system architecture can monitor outputs and performance, and handle, recover from, and repair errors when security anomalies, threats and impacts are detected. Confabulation, Information Integrity, Information Security MS - 2.6 - 010 Verify that systems properly handle queries that may give rise to inappropriate, malicious, or illegal usage, including facilitating manipulation, extortion, targeted impersonation, cyber - attacks , and weapons creation. CBRN Information, Information Security AI Actors: AI Deployment, AI Impact Assessment, Domain Experts, Operation and Monitoring, TEVV 1 MEASURE 2.7: AI system security and resilience – as identified in the MAP function – are evaluated and documented. Action ID Action Risks MS - 2.7 - 001 Apply established security measures to: Assess risks of backdoors, compromised dependencies, data breaches, eavesdropping, man - in - the - middle attacks, reverse engineering other baseline security concerns; Audit supply chains to identify risks arising from, e.g., data poisoning and malware, software and hardware vulnerabilities, third - party personnel and software; Audit GAI systems, pipelines, plugins and other related artifacts for unauthorized access, malware, and other known vulnerabilities. Data Privacy, Information Integrity, Information Security, Value Chain and Component Integration MS - 2.7 - 002 Assess the completeness of documentation related to data provenance, access controls, and incident response procedures. Verify GAI system content provenance documentation aligns with relevant regulations and standards. Information Integrity, Toxicity, Bias, and Homogenization MS - 2.7 - 003 Benchmark GAI system security and resilience related to content provenance against industry standards and best practices. Compare GAI system security features and content provenance methods against industry state - of - the - art. Information Integrity, Information Security MS - 2.7 - 004 Conduct user surveys to gather user satisfaction with the AI - generated content and user perceptions of content authenticity. Analyze user feedback to identify concerns and/or current literacy levels related to content provenance. Human AI Configuration, Information Integrity MS - 2.7 - 005 Engage with security experts, developers, and researchers through information sharing mechanisms to stay updated with the latest advancements in AI security related to content provenance. Contribute findings related to AI system security and content provenance via information sharing mechanisms, workshops, or publications. Information Integrity, Information Security MS - 2.7 - 006 Establish measures and evaluate GAI resiliency as part of pre - deployment testing to ensure GAI will function under adverse conditions and restore full functionality in a trustworthy manner.

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42 MS - 2.7 - 007 Identify metrics that reflect the effectiveness of security measures, such as data provenance, the number of unauthorized access attempts, penetrations, or provenance verification. Information Integrity, Information Security MS - 2.7 - 008 Maintain awareness of emergent GAI security risks and associated countermeasures through community resources, official guidance, or research literature. Information Security, Unknowns MS - 2.7 - 009 Measure reliability of content provenance verification methods, such as watermarking, cryptographic signatures, hashing, blockchain, or other content provenance techniques. Evaluate the rate of false positives and false negatives in content provenance, as well as true positives and true negatives for verification. Information Integrity MS - 2.7 - 010 Measure the average response time to security incidents related to content provenance, and the proportion of incidents resolved with and without significant impact. Information Integrity, Information Security MS - 2.7 - 011 Measure the rate at which recommendations from security audits and incidents are implemented related to content provenance. Assess how quickly the AI system can adapt and improve based on lessons learned from security incidents and feedback related to content provenance. Information Integrity, Information Security MS - 2.7 - 012 Monitor and review the completeness and validity of security documentation and verify it aligns with the current state of the GAI system and its content provenance. Information Integrity, Information Security, Toxicity, Bias, and Homogenization MS - 2.7 - 013 Monitor GAI system downtime and measure its impact on operations. MS - 2.7 - 014 Monitor GAI systems in deployment for anomalous use and security risks. Information Security MS - 2.7 - 015 Monitor the number of security - related incident reports from users, indicating their awareness and willingness to report issues. Human AI Configuration, Information Security MS - 2.7 - 016 Perform AI red - teaming to assess resilience against: Abuse to facilitate attacks on other systems (e.g., malicious code generation, enhanced phishing content), GAI attacks (e.g., prompt injection), ML attacks (e.g., adversarial examples/prompts, data poisoning, membership inference, model extraction , sponge examples). Information Security, Toxicity, Bias, and Homogenization, Dangerous or Violent Recommendations MS - 2.7 - 017 Review deployment approval processes and verify that processes address relevant GAI security risks. Information Security MS - 2.7 - 018 Review incident response procedures and verify adequate functionality to identify, contain, eliminate, and recover from complex GAI system incidents that implicate impacts across the trustworthy characteristics. MS - 2.7 - 019 Track and document access and updates to GAI system training data; verify appropriate security measures for training data at GAI vendors and service providers. Information Security, Value Chain and Component Integration

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43 MS - 2.7 - 020 Track GAI system performance metrics such as response time and throughput under different loads and usage patterns related to content provenance. Information Integrity MS - 2.7 - 021 Track the number of users who have completed security training programs regarding the security of content provenance. Human AI Configuration, Information Integrity, Information Security MS - 2.7 - 022 Verify fine - tuning does not compromise safety and security controls. Information Integrity, Information Security, Dangerous or Violent Recommendations MS - 2.7 - 023 Verify organizational policies, procedures, and processes for treatment of GAI security and resiliency risks. Information Security MS - 2.7 - 024 Verify vendor documentation for data and software security controls. Information Security, Value Chain and Component Integration MS - 2.7 - 025 Work with domain experts to capture stakeholder confidence in GAI system security and perceived effectiveness related to content provenance. Information Integrity, Information Security AI Actors: AI Deployment, AI Impact Assessment, Domain Experts, Operation and Monitoring, TEVV 1 MEASURE 2.8: Risks associated with transparency and accountability – as identified in the MAP function – are examined and documented. Action ID Action Risks MS - 2.8 - 001 Compile and communicate statistics on policy violations, take - down requests, intellectual property infringement, and information integrity for organizational GAI systems: Analyze transparency reports across demographic groups, languages groups, and other segments relevant to the deployment context. Information Integrity, Intellectual Property, Toxicity, Bias, and Homogenization MS - 2.8 - 002 Document the instructions given to data annotators or AI red - teamers. MS - 2.8 - 003 Document where in the data pipeline human labor is being used. MS - 2.8 - 004 Establish a mechanism for appealing usage policy violations. MS - 2.8 - 005 Maintain awareness of AI regulations and standards in relevant jurisdictions related to GAI systems and content provenance. Information Integrity MS - 2.8 - 006 Measure the effectiveness or accessibility of procedures to appeal adverse, harmful, or incorrect outcomes from GAI systems. Human AI Configuration, Toxicity, Bias, and Homogenization, Dangerous or Violent Recommendations

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44 MS - 2.8 - 007 Review and consider GAI system transparency artifacts such as impact assessments, system cards, model cards, and traditional risk management documentation as part of organizational decision making. MS - 2.8 - 008 Review licenses, patents, or other intellectual property rights pertaining to information in system training data. Intellectual Property MS - 2.8 - 009 Track AI actor decisions along the lifecycle to determine sources of systemic and cognitive bias and identify management and mitigation approaches. Human AI Configuration, Toxicity, Bias, and Homogenization MS - 2.8 - 010 Use interpretable machine learning techniques to make AI processes and outcomes more transparent, and easier to understand how decisions are made. MS - 2.8 - 011 Use technologies such as blockchain and digital signatures to enable the documentation of each instance where content is generated, modified, or shared to provide a tamper - proof history of the content, promote transparency, and enable traceability. Robust version control systems c an also be applied to track changes across the AI lifecycle over time. Information Integrity MS - 2.8 - 012 Verify adequacy of GAI system user instructions through user testing. Human AI Configuration MS - 2.8 - 013 Verify that accurate information about GAI capabilities, opportunities, risks, and potential negative impacts are available on websites, press releases, organizational reports, social media, and public communication channels. MS - 2.8 - 014 Verify the adequacy of feedback functionality in system user interfaces. Human AI Configuration MS - 2.8 - 015 Verify the adequacy of redress processes for severe GAI system impacts. AI Actors: AI Deployment, AI Impact Assessment, Domain Experts, Operation and Monitoring, TEVV 1 MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context – as identified in the MAP function – to inform responsible use and governance. Action ID Action Risks MS - 2.9 - 001 Apply and document ML explanation results such as: Analysis of embeddings, Counterfactual prompts, Gradient - based attributions, Model compression/surrogate models, Occlusion/term reduction. MS - 2.9 - 002 Apply transparency tools such as Datasheets, Data Nutrition Labels, and Model Cards to record explanatory and validation information.

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45 MS - 2.9 - 003 Document GAI model details including: Proposed use and organizational value; Assumptions and limitations, Data collection methodologies; Data provenance; Data quality; Model architecture (e.g., convolutional neural network, transformers, etc.); Optimization objectives; Training algorithms; RLHF approaches; Fine - tuning approaches; Evaluation data; Ethical considerations; Legal and regulatory requirements. Information Integrity, Toxicity, Bias, and Homogenization MS - 2.9 - 004 Measure and report: Comparisons to alternative approaches and benchmarks; Outcomes across demographic groups, languages groups, and other segments relevant to the deployment context; Reproducibility of outcomes or internal mechanisms; Sensitivity analysis and stress - testing results. Toxicity, Bias, and Homogenization MS - 2.9 - 005 Verify calibration and robustness of applied explanation techniques and document their assumptions and limitations. AI Actors: AI Deployment, AI Impact Assessment, Domain Experts, End - Users, Operation and Monitoring, TEVV 1 *MEASURE 2.10: Privacy risk of the AI system – as identified in the MAP function – is examined and documented. Action ID Action Risks MS - 2.10 - 001 Collaborate with other AI actors, domain experts, and legal advisors to evaluate the impact of GAI applications on privacy related to the GAI system and its content provenance, in domains such as healthcare, finance, and criminal justice. Data Privacy, Human AI Configuration, Information Integrity MS - 2.10 - 002 Conduct AI red - teaming to assess GAI system risks such as: Outputting of training data samples, and subsequent reverse engineering, model extraction, and membership inference risks; Revealing biometric, confidential, copyrighted, licensed, patented, personal, proprietary, sensitive, or trade - marked; Tracking or revealing location information of users or members of training datasets. Human AI Configuration, Intellectual Property MS - 2.10 - 003 Document collection, use, management, and disclosure of biometric, confidential, copyrighted, licensed, patented, personal, proprietary, sensitive, or trade - marked information in datasets, in accordance with privacy and data governance policies and data privacy laws. Data Privacy, Human AI Configuration, Intellectual Property MS - 2.10 - 004 Engage directly with end - users and other stakeholders to understand their expectations and concerns regarding content provenance. Use this feedback to guide the design of provenance - tracking mechanisms. Human AI Configuration, Information Integrity MS - 2.10 - 005 Establish and document protocols (authorization, duration, type) and access controls for training sets or production data containing biometric, confidential, copyrighted, licensed, patented, personal, proprietary, sensitive, or trade - marked information, in accordance with privacy and data governance policies and data privacy laws. Data Privacy, Intellectual Property

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46 MS - 2.10 - 006 Implement consent mechanisms that are demonstrated to allow users to understand and control how their data is used in the GAI system and its content provenance. Data Privacy, Human AI Configuration, Information Integrity MS - 2.10 - 007 Implement mechanisms to monitor, periodically review and document the provenance data to detect any inconsistencies or unauthorized modifications. Information Integrity, Information Security MS - 2.10 - 008 Implement zero - knowledge proofs to balance transparency with privacy and allow verification of claims about content without exposing the actual data. Data Privacy MS - 2.10 - 009 Leverage technologies such as blockchain to document the origin of, and any subsequent modifications to, generated content to enhance transparency and provide a secure method for provenance tracking. Information Integrity, Information Security MS - 2.10 - 010 Track training, input and output items that contains personally identifiable information. Data Privacy MS - 2.10 - 011 Verify compliance with data protection regulations. Data Privacy MS - 2.10 - 012 Verify deduplication of training data samples. Toxicity, Bias, and Homogenization MS - 2.10 - 013 Verify organizational policies, procedures, and processes for GAI systems address fundamental tenets of data privacy, e.g., Anonymization of private data; Consent to use data for targeted purposes or applications; Data collection and use in accordance with legal requirements and organizational policies; Reasonable data retention limits and requirements; User data deletion and rectification requests. Data Privacy, Human AI Configuration MS - 2.10 - 014 Verify that biometric, confidential, copyrighted, licensed, patented, personal, proprietary, sensitive, or trade - marked information are removed from GAI training data. Intellectual Property AI Actors: AI Deployment, AI Impact Assessment, Domain Experts, End - Users, Operation and Monitoring, TEVV 1 *MEASURE 2.11: Fairness and bias – as identified in the MAP function – are evaluated and results are documented. Action ID Action Risks MS - 2.11 - 001 Apply use - case appropriate benchmarks (e.g., Bias Benchmark Questions, Real Toxicity Prompts, Winogender) to quantify systemic bias, stereotyping, denigration, and toxicity in GAI system outputs; Document assumptions and limitations of benchmarks relative to in - context deployment environment. Toxicity, Bias, and Homogenization MS - 2.11 - 002 Assess content moderation and other output filtering technologies or processes for risks arising from human, systemic, and statistical/computational biases. Toxicity, Bias, and Homogenization

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47 MS - 2.11 - 003 Conduct fairness assessments to measure systemic bias. Measure GAI system performance across demographic groups and subgroups, addressing both quality of service and any allocation of services and resources. Identify types of harms, including harms in resource allocation, representational, quality of service, stereotyping, or erasure, Identify across, within, and intersecting groups that might be harmed; Quantify harms usi ng: field testing with sub - group populations to determine likelihood of exposure to generated content exhibiting harmful bias, AI red - teaming with counterfactual and low - context (e.g., “leader,” “bad guys”) prompts. For ML pipelines or business processes w ith categorical or numeric outcomes that rely on GAI, apply general fairness metrics (e.g., demographic parity, equalized odds, equal opportunity, statistical hypothesis tests), to the pipeline or business outcome where appropriate; Custom, context - specifi c metrics developed in collaboration with domain experts and affected communities; Measurements of the prevalence of denigration in generated content in deployment (e.g., sub - sampling a fraction of traffic and manually annotating denigrating content); Anal yze quantified harms for contextually significant differences across groups, within groups, and among intersecting groups; Refine identification of within - group and intersectional group disparities, Evaluate underlying data distributions and employ sensiti vity analysis during the analysis of quantified harms, Evaluate quality metrics including differential output across groups, Consider biases affecting small groups, within - group or intersectional communities, or single individuals. Toxicity, Bias, and Homogenization, Dangerous or Violent Recommendations MS - 2.11 - 004 Evaluate practices along the lifecycle to identify potential sources of human - cognitive bias such as availability, observational, groupthink, funding, and confirmation bias, and to make implicit decision - making processes more explicit and open to investigation. Toxicity, Bias, and Homogenization MS - 2.11 - 005 Identify the classes of individuals, groups, or environmental ecosystems which might be impacted by GAI systems through direct engagement with potentially impacted communities. Environmental, Toxicity, Bias, and Homogenization MS - 2.11 - 006 Monitor for representational, financial, or other harms after GAI systems are deployed. Toxicity, Bias, and Homogenization, Dangerous or Violent Recommendations MS - 2.11 - 007 Review, document, and measure sources of bias in training and TEVV data: Differences in distributions of outcomes across and within groups, including intersecting groups; Completeness, representativeness, and balance of data sources; demographic group and subgroup coverage in GAI system training data; Forms of latent systemic bias in images, text, audio, embeddings, or other complex or unstructured data ; Input data features that may serve as proxies for demographic group membership (i.e., image metadata, language dialect) or otherwise give rise to emergent bias within GAI systems; The extent to which the digital divide may negatively impact representativ eness in GAI system training and TEVV data; Filtering of hate speech and toxicity in GAI system training data; Prevalence of GAI - generated data in GAI system training data. Toxicity, Bias, and Homogenization, Unknowns

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48 MS - 2.11 - 008 Track and document AI actor credentials and qualifications. Human AI Configuration MS - 2.11 - 009 Verify accessibility functionality; verify functionality and timeliness of accommodations and opt - out functionality or processes. Human AI Configuration, Toxicity, Bias, and Homogenization MS - 2.11 - 010 Verify bias management in periodic model updates; test and recalibrate with updated and more representative data to manage bias within acceptable tolerances. Toxicity, Bias, and Homogenization MS - 2.11 - 011 Verify training is not homogenous GAI - produced data in order to mitigate concerns of model collapse. Toxicity, Bias, and Homogenization AI Actors: AI Deployment, AI Impact Assessment, Affected Individuals and Communities, Domain Experts, End - Users, Operation and Monitoring, TEVV 1 MEASURE 2.12: Environmental impact and sustainability of AI model training and management activities – as identified in the MAP function – are assessed and documented. Action ID Action Risks MS - 2.12 - 001 Assess safety to physical environments when deploying GAI systems. Dangerous or Violent Recommendations MS - 2.12 - 002 Document anticipated environmental impacts of model development, maintenance, and deployment in product design decisions. Environmental MS - 2.12 - 003 Measure or estimate environmental impacts (e.g., energy and water consumption) for training, fine tuning, and deploying models: Verify tradeoffs between resources used at inference time versus additional resources required at training time. Environmental MS - 2.12 - 004 Track and document continuous improvement processes that enhance effectiveness of risk measurement for GAI environmental impacts and sustainability. Environmental MS - 2.12 - 005 Verify effectiveness of carbon capture or offset programs, and address green - washing risks. Environmental AI Actors: AI Deployment, AI Impact Assessment, Domain Experts, Operation and Monitoring, TEVV 2

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49 MEASURE 2.13: Effectiveness of the employed TEVV metrics and processes in the MEASURE function are evaluated and documented. Action ID Action Risks MS - 2.13 - 001 Create measurement error models for pre - deployment metrics to demonstrate construct validity for each metric (i.e., does the metric effectively operationalize the desired concept): Measure or estimate, and document, biases or statistical variance in applied metrics or structured human feedback processes; Adhere to applicable laws and regulations when opera tionalizing models in high - volume settings (e.g., toxicity classifiers and automated content filters); Leverage domain expertise when modeling complex societal constructs such as toxicity. Confabulation, Information Integrity, Toxicity, Bias, and Homogenization MS - 2.13 - 002 Document measurement and structured public feedback processes applied to organizational GAI systems in a centralized repository (i.e., organizational AI inventory). MS - 2.13 - 003 Review GAI system metrics and associated pre - deployment processes to determine their ability to sustain system improvements, including the identification and removal of errors, harms, and negative impacts. Confabulation, Information Integrity, Dangerous or Violent Recommendations AI Actors: AI Deployment, Operation and Monitoring, TEVV 1 *MEASURE 3.1: Approaches, personnel, and documentation are in place to regularly identify and track existing, unanticipated, and emergent AI risks based on factors such as intended and actual performance in deployed contexts. Action ID Action Risks MS - 3.1 - 001 Assess completeness of known use cases and expected performance of inputs, such as third - party data or upstream AI systems, or the performance of downstream systems which use the outputs of the GAI system, directly or indirectly, through engagement and out reach with AI Actors. Human AI Configuration, Value Chain and Component Integration, Toxicity, Bias, and Homogenization MS - 3.1 - 002 Compare intended use and expected performance of GAI systems across all relevant contexts.

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50 MS - 3.1 - 003 Elicit and track feedback for previously unknown uses of the GAI systems. AI Actors: AI Impact Assessment, Operation and Monitoring, TEVV 1 MEASURE 3.2: Risk tracking approaches are considered for settings where AI risks are difficult to assess using currently available measurement techniques or where metrics are not yet available. Action ID Action Risks MS - 3.2 - 001 Determine if available GAI system risk measurement approaches are applicable to the GAI system use contexts. MS - 3.2 - 002 Document the rate of occurrence and severity of GAI harms to the organization and to external AI actors. Human AI Configuration MS - 3.2 - 003 Establish processes for identifying emergent GAI system risks with external AI actors. Human AI Configuration, Unknowns MS - 3.2 - 004 Identify measurement approaches for tracking GAI system risks if none exist. AI Actors: AI Impact Assessment, Domain Experts, Operation and Monitoring, TEVV 2 *MEASURE 3.3: Feedback processes for end users and impacted communities to report problems and appeal system outcomes are established and integrated into AI system evaluation metrics. Action ID Action Risks MS - 3.3 - 001 Conduct impact assessments on how AI - generated content might affect different social, economic, and cultural groups. Toxicity, Bias, and Homogenization MS - 3.3 - 002 Conduct studies to understand how end users perceive and interact with GAI content related to content provenance within context of use. Assess whether the content aligns with their expectations and how they may act upon the information presented. Human AI Configuration, Information Integrity MS - 3.3 - 003 Design evaluation metrics that include parameters for content provenance quality, validity, reliability, authenticity or origin, and integrity of content. Information Integrity MS - 3.3 - 004 Evaluate GAI system evaluation metrics based on feedback from relevant AI actors. Human AI Configuration

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51 MS - 3.3 - 005 Evaluate potential biases and stereotypes that could emerge from the AI - generated content using appropriate methodologies including computational testing methods as well as evaluating structured feedback input. Toxicity, Bias, and Homogenization MS - 3.3 - 006 Implement continuous monitoring of AI - generated content and provenance after system deployment for various types of drift. Verify GAI systems are adaptive and able to iteratively improve models and algorithms over time. Information Integrity MS - 3.3 - 007 Integrate human evaluators to assess content quality and relevance. Human AI Configuration MS - 3.3 - 008 Provide input for training materials about the capabilities and limitations of GAI systems related to content provenance for AI actors, other professionals, and the public about the societal impacts of AI and the role of diverse and inclusive content generation. Human AI Configuration, Information Integrity, Toxicity, Bias, and Homogenization MS - 3.3 - 009 Record and integrate structured feedback about content provenance from operators, users, and potentially impacted communities through the use of methods such as user research studies, focus groups, or community forums. Actively seek feedback on generated content quality and po tential biases. Assess the general awareness among end users and impacted communities about the availability of these feedback channels. Human AI Configuration, Information Integrity, Toxicity, Bias, and Homogenization MS - 3.3 - 010 Regularly review structured human feedback and GAI system sensors and update based on the evolving needs and concerns of the impacted communities. MS - 3.3 - 011 Utilize independent evaluations to assess content quality and types of potential biases and related negative impacts. Toxicity, Bias, and Homogenization MS - 3.3 - 012 Verify AI actors engaged in GAI TEVV tasks for content provenance reflect diverse demographic and interdisciplinary backgrounds. Human AI Configuration, Information Integrity, Toxicity, Bias, and Homogenization AI Actors: AI Deployment, Affected Individuals and Communities, End - Users, Operation and Monitoring, TEVV 1 MEASURE 4.2: Measurement results regarding AI system trustworthiness in deployment context(s) and across the AI lifecycle are informed by input from domain experts and relevant AI actors to validate whether the system is performing consistently as intended. Results ar e documented. Action ID Action Risks MS - 4.2 - 001 Conduct adversarial testing to assess the GAI system’s response to inputs intended to deceive or manipulate its content provenance and understand potential misuse scenarios and unintended outputs. Information Integrity, Information Security

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52 MS - 4.2 - 002 Ensure both positive and negative feedback on GAI system functionality is assessed. MS - 4.2 - 003 Ensure visible mechanisms to collect users’ feedback are in place, including systems to report harmful and low quality content. Human AI Configuration, Dangerous or Violent Recommendations MS - 4.2 - 004 Evaluate GAI system content provenance in real - world scenarios to observe its behavior in practical environments and reveal issues that might not surface in controlled and optimized testing environments. Information Integrity MS - 4.2 - 005 Evaluate GAI system performance related to content provenance against predefined metrics and update the evaluation criteria as necessary to adapt to changing contexts and requirements. Information Integrity MS - 4.2 - 006 Implement interpretability and explainability methods to evaluate GAI system decisions related to content provenance and verify alignment with intended purpose. Information Integrity, Toxicity, Bias, and Homogenization MS - 4.2 - 007 Integrate structured human feedback results into calibration and update processes for traditional measurement approaches (e.g., benchmarks, performance assessments, data quality measurements). MS - 4.2 - 008 Measure GAI system inputs and outputs to account for content provenance, data provenance, source reliability, contextual relevance and coherence, and security implications. Information Integrity, Information Security MS - 4.2 - 009 Monitor and document instances where human operators or other systems override the GAI's decisions. Evaluate these cases to understand if the overrides are linked to issues related to content provenance. Information Integrity MS - 4.2 - 010 Verify and document the incorporation of structured human feedback results into design, implementation, deployment approval (“go”/“no - go” decisions), monitoring, and decommission decisions. MS - 4.2 - 011 Verify that GAI system development and deployment related to content provenance integrates trustworthiness characteristics. Information Integrity MS - 4.2 - 012 Verify the performance of user feedback and recourse mechanisms, including analyses across various sub - groups. Human AI Configuration, Toxicity, Bias, and Homogenization MS - 4.2 - 013 Work with domain experts to integrate insights from stakeholder feedback analysis into TEVV metrics and associated actions, and continuous improvement processes. MS - 4.2 - 014 Work with domain experts to review feedback from end users, operators, and potentially impacted individuals and communities — enumerated in the Map function. Human AI Configuration

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53 MS - 4.2 - 015 Work with domain experts who understand the GAI system context of use to evaluate the content’s validity, relevance, and potential biases. Toxicity, Bias, and Homogenization AI Actors: AI Deployment, Domain Experts, End - Users, Operation and Monitoring, TEVV 1 * MANAGE 1.3: Responses to the AI risks deemed high priority, as identified by the MAP function, are developed, planned, and documented. Risk response options can include mitigating, transferring, avoiding, or accepting. Action ID Action Risks MG - 1.3 - 001 Allocate resources and time for GAI risk management activities, including planning for incident response and other mitigation activities. MG - 1.3 - 002 Document residual GAI system risks that persist after risk mitigation or transfer. MG - 1.3 - 003 Document trade - offs, decision processes, and relevant measurement and feedback results for risks that do not surpass organizational risk tolerance. MG - 1.3 - 004 Mitigate, transfer, or avoid risks that surpass organizational risk tolerances. MG - 1.3 - 005 Monitor the effectiveness of risk controls (e.g., via field testing, participatory engagements, performance assessments, user feedback mechanisms). Human AI Configuration AI Actors: AI Deployment, AI Impact Assessment, Operation and Monitoring 2 MANAGE 2.2: Mechanisms are in place and applied to sustain the value of deployed AI systems. Action ID Action Risks MG - 2.2 - 001 Compare GAI system outputs against pre - defined organization risk tolerance, guidelines , and principles , and review and audit AI - generated content against these guidelines. MG - 2.2 - 002 Document training data sources to trace the origin and provenance of AI - generated content. Information Integrity MG - 2.2 - 003 Evaluate feedback loops between GAI system content provenance and human reviewers, and update make updates where needed. Implement real - time monitoring systems to detect GAI systems and content provenance drift as it happens. Information Integrity

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54 MG - 2.2 - 004 Evaluate GAI content and data for representational biases and employ techniques such as re - sampling, re - ranking, or adversarial training to mitigate biases in the generated content. Information Security, Toxicity, Bias, and Homogenization MG - 2.2 - 005 Filter GAI output for harmful or biased content, potential misinformation , and CBRN - related or NCII content . CBRN Information, Obscene, Degrading, and/or Abusive Content, Toxicity, Bias, and Homogenization , Dangerous or Violent Recommendations MG - 2.2 - 006 Implement version control for models and datasets to track changes and facilitate rollback if necessary. MG - 2.2 - 007 Incorporate feedback from users, external experts, and the public to adapt the GAI system and monitoring processes. Human AI Configuration MG - 2.2 - 008 Incorporate human review processes to assess and filter content in accordance with the socio - cultural knowledge and values of the context of use and to identify limitations and nuances that automated processes might miss; verify that human reviewers are tr ained on content guidelines and potential biases of GAI system and its content provenance. Information Integrity, Toxicity, Bias, and Homogenization MG - 2.2 - 009 Integrate information from data management and machine learning security countermeasures like red teaming, and differential privacy, and authentication protocols to ensure data and models are protected from potential risks. CBRN Information, Data Privacy, Information Security MG - 2.2 - 010 Use feedback from internal and external AI actors, users, individuals, and communities, to assess impact of AI - generated content. Human AI Configuration MG - 2.2 - 011 Use real - time auditing tools such as distributed ledger technology to track and validate the lineage and authenticity of AI - generated data. Information Integrity MG - 2.2 - 012 Use structured feedback mechanisms to solicit and capture user input about AI - generated content to detect subtle shifts in quality or alignment with community and societal values. Human AI Configuration, Toxicity, Bias, and Homogenization AI Actors: AI Deployment, AI Impact Assessment, Governance and Oversight, Operation and Monitoring 1

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55 * MANAGE 2.3: Procedures are followed to respond to and recover from a previously unknown risk when it is identified. Action ID Action Risks MG - 2.3 - 001 Develop and update GAI system incident response and recovery plans and procedures to address the following: Review and maintenance of policies and procedures to account for newly encountered uses; Review and maintenance of policies and procedures for detection of unanticipated uses; Verify response and recovery plans account for the GAI system supply chain; Verify response and recovery plans are updated for and include necessary details to communicate with downstream GAI system Actors: Points - of - Contact (POC), Contact information, notification format. Value Chain and Component Integration MG - 2.3 - 002 Maintain protocols to log changes made to GAI systems during incident response and recovery. MG - 2.3 - 003 Review, update and maintain incident response and recovery plans to integrate insights from GAI system use cases and contexts and needs of relevant AI actors. Human AI Configuration MG - 2.3 - 004 Verify and maintain measurements that GAI systems are operating within organizational risk tolerances post incident. AI Actors: AI Deployment, Operation and Monitoring 1 * MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use. Action ID Action Risks MG - 2.4 - 001 Enforce change management processes, and risk and impact assessments across all intended uses and contexts before deploying GAI system updates. MG - 2.4 - 002 Establish and maintain communication plans to inform AI stakeholders as part of the deactivation or disengagement process of a specific GAI system or context of use, including reasons, workarounds, user access removal, alternative processes, contact information, etc. Human AI Configuration MG - 2.4 - 003 Establish and maintain procedures for escalating GAI system incidents to the organizational risk authority when specific criteria for deactivation or disengagement is met for a particular context of use or for the GAI system as a whole.

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56 MG - 2.4 - 004 Establish and maintain procedures for the remediation of issues which trigger incident response processes for the use of a GAI system, and provide stakeholders timelines associated with the remediation plan. MG - 2.4 - 005 Establish and regularly review specific criteria that warrants the deactivation of GAI systems in accordance with set risk tolerances and appetites. AI Actors: AI Deployment, Governance and Oversight, Operation and Monitoring 1 * MANAGE 3.1: AI risks and benefits from third - party resources are regularly monitored, and risk controls are applied and documented. Action ID Action Risks MG - 3.1 - 001 Apply organizational risk tolerances and controls (e.g., acquisition and procurement processes; assessing personnel credentials and qualifications, performing background checks; filtering GAI input and outputs, grounding, fine tuning) to third - party GAI resources: Apply organizational risk tolerance to the utilization of third - party datase ts and other GAI resources; Apply organizational risk tolerances to fine - tuned third - party models; Apply organizational risk tolerance to existing third - party models adapted to a new domain; Reassess risk measurements after fine - tuning third - party GAI mode ls. Value Chain and Component Integration MG - 3.1 - 002 Audit GAI system supply chain risks (e.g., data poisoning, malware, other software and hardware vulnerabilities; labor practices; data privacy and localization compliance; geopolitical alignment). Data Privacy, Information Security, Value Chain and Component Integration , Toxicity, Bias, and Homogenization MG - 3.1 - 003 Decommission third - party systems that exceed organizational risk tolerances. Value Chain and Component Integration MG - 3.1 - 004 Identify and maintain documentation for third - party AI systems, and components, in organizational AI inventories. Value Chain and Component Integration MG - 3.1 - 005 Initiate review of third - party organizations/developers prior to their use of GAI models, and during their use of GAI models for their own applications, to monitor for abuse and policy violations. Value Chain and Component Integration , Toxicity, Bias, and Homogenization , Dangerous or Violent Recommendations MG - 3.1 - 006 Re - assess model risks after fine - tuning and for any third - party GAI models deployed for applications and/or use cases that were not evaluated in initial testing. Value Chain and Component Integration

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57 MG - 3.1 - 007 Review GAI training data for CBRN information and intellectual property; scan output for plagiarized, trademarked, patented, licensed, or trade secret material. Intellectual Property, CBRN Information MG - 3.1 - 008 Update acquisition and procurement policies, procedures, and processes to address GAI risks and failure modes. MG - 3.1 - 009 Use, review, update, and share various transparency artifacts (e.g., system cards and model cards) for third - party models. Document or retain documentation for: Training data content and provenance, methodology, testing, validation, and clear instructions for use from GAI vendors and suppliers, Information related to third - party information security policies, procedures, and processes. Information Integrity, Information Security, Value Chain and Component Integration AI Actors: AI Deployment, Operation and Monitoring, Third - party entities 1 MANAGE 3.2: Pre - trained models which are used for development are monitored as part of AI system regular monitoring and maintenance. Action ID Action Risks MG - 3.2 - 001 Apply explainable AI (XAI) techniques (e.g., analysis of embeddings, model compression/distillation, gradient - based attributions, occlusion/term reduction, counterfactual prompts, word clouds) as part of ongoing continuous improvement processes to mitigate risks related to unexplainable GAI systems. MG - 3.2 - 002 Document how pre - trained models have been adapted (fine - tuned) for the specific generative task, including any data augmentations, parameter adjustments, or other modifications. Access to un - tuned (baseline) models must be available to support debugging the relative in fluence of the pre - trained weights compared to the fine - tuned model weights. MG - 3.2 - 003 Document sources and types of training data and their origins, potential biases present in the data related to the GAI application and its content provenance, architecture, training process of the pre - trained model including information on hyperparameters, training duration, and any fine - tuning processes applied. Information Integrity, Toxicity, Bias, and Homogenization MG - 3.2 - 004 Evaluate user reported problematic content and integrate feedback into system updates. Human AI Configuration , Dangerous or Violent Recommendations

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58 MG - 3.2 - 005 Implement content filters to prevent the generation of inappropriate, harmful, toxic, false, illegal, or violent content related to the GAI application, including for CSAM and NCII. These filters can be rule - based or leverage additional machine learning models to flag problematic inputs and outputs. Information Integrity, Toxicity, Bias, and Homogenization , Dangerous or Violent Recommendations , Obscene, Degrading, and/or Abusive Content MG - 3.2 - 006 Implement real - time monitoring processes for analyzing generated content performance and trustworthiness characteristics related to content provenance to identify deviations from the desired standards and trigger alerts for human intervention. Information Integrity MG - 3.2 - 007 Leverage feedback and recommendations from organizational boards or committees related to the deployment of GAI applications and content provenance when using third - party pre - trained models. Information Integrity, Value Chain and Component Integration MG - 3.2 - 008 Maintain awareness of relevant laws and regulations related to content generation, data privacy, and user protections and work in conjunction with legal experts to review and assess the potential liabilities associated with AI - generated content. Data Privacy, Intellectual Property Information Integrity MG - 3.2 - 009 Provide use case examples as material for training employees and stakeholders about the trustworthiness implications of GAI applications and content provenance and to raise awareness about potential risks in fostering a risk management culture. Information Integrity MG - 3.2 - 010 Use human moderation systems to review generated content in accordance with human - AI configuration policies established in the Govern function, aligned with socio - cultural norms in the context of use, and for settings where AI models are demonstrated to pe rform poorly. Human AI Configuration MG - 3.2 - 011 Use organizational risk tolerance to evaluate acceptable risks and performance metrics and decommission or retrain pre - trained models that perform outside of defined limits. CBRN Information, Confabulation AI Actors: AI Deployment, Operation and Monitoring, Third - party entities 1

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59 * MANAGE 4.1: Post - deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management. Action ID Action Risks MG - 4.1 - 001 Collaborate with external researchers, industry experts, and community representatives to maintain awareness of emerging best practices and technologies in content provenance. Information Integrity, Toxicity, Bias, and Homogenization MG - 4.1 - 002 Conduct adversarial testing at a regular cadence; test against various adversarial inputs and scenarios; identify vulnerabilities and assess the AI system’s resilience to content provenance attacks. Information Integrity, Information Security MG - 4.1 - 003 Conduct red - teaming exercises to surface failure modes of content provenance mechanisms. Evaluate the effectiveness of red - teaming approaches for uncovering potential vulnerabilities and improving overall content provenance. Information Integrity, Information Security MG - 4.1 - 004 Employ user - friendly channels such as feedback forms, e - mails, or hotlines for users to report issues, concerns, or unexpected GAI outputs to feed into monitoring practices. Human AI Configuration MG - 4.1 - 005 Establish, maintain, and evaluate effectiveness of organizational processes and procedures to monitor GAI systems within context of use. MG - 4.1 - 006 Evaluate the use of sentiment analysis to gauge user sentiment regarding GAI content performance and impact, and work in collaboration with AI actors experienced in user research and experience. Human AI Configuration MG - 4.1 - 007 Implement active learning techniques to identify instances where the model fails or produces unexpected outputs. Confabulation MG - 4.1 - 008 Integrate digital watermarks, blockchain technology, cryptographic hash functions, metadata embedding, or other content provenance techniques within AI - generated content to track its source and manipulation history. Information Integrity MG - 4.1 - 009 Measure system outputs related to content provenance at a regular cadence and integrate insights into monitoring processes. Information Integrity MG - 4.1 - 010 Monitor GAI training data for representation of different user groups. Human AI Configuration, Toxicity, Bias, and Homogenization MG - 4.1 - 011 Perform periodic review of organizational adherence to GAI system monitoring plans across all contexts of use.

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60 MG - 4.1 - 012 Share transparency reports with internal and external stakeholders that detail steps taken to update the AI system to enhance transparency and accountability. MG - 4.1 - 013 Track dataset modifications for content provenance by monitoring data deletions, rectification requests, and other changes that may impact the verifiability of content origins. Information Integrity MG - 4.1 - 014 Verify risks associated with gaps in GAI system monitoring plans are accepted at the appropriate organizational level. MG - 4.1 - 015 Verify that AI actors responsible for monitoring reported issues can effectively evaluate GAI system performance and its content provenance, and promptly escalate issues for response. Human AI Configuration, Information Integrity AI Actors: AI Deployment, Affected Individuals and Communities, Domain Experts, End - Users, Human Factors, Operation and Monitoring 1 MANAGE 4.2: Measurable activities for continual improvements are integrated into AI system updates and include regular engagement with interested parties, including relevant AI actors. Action ID Action Risks MG - 4.2 - 001 Adopt agile development methodologies, and iterative development and feedback loops to allow for rapid adjustments based on external input related to content provenance. Information Integrity MG - 4.2 - 002 Conduct regular audits of GAI systems and publish reports detailing the performance, feedback received, and improvements made. MG - 4.2 - 003 Employ explainable AI methods to enhance transparency and interpretability of GAI content provenance to help AI actors and stakeholders understand how and why specific content is generated. Human AI Configuration, Information Integrity MG - 4.2 - 004 Employ stakeholder feedback captured in the Map function to understand user experiences and perceptions about AI - generated content and its provenance; include user interactions and feedback from real - world scenarios. Human AI Configuration, Information Integrity MG - 4.2 - 005 Form cross - functional teams leveraging expertise from across the AI lifecycle including AI designers and developers, socio - technical experts, and experts in the context of use and identify mechanisms to include end users in consultations. Human AI Configuration

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61 MG - 4.2 - 006 Practice and follow incident response plans for addressing the generation of inappropriate or harmful content and adapt processes based on findings to prevent future occurrences. Conduct post - mortem analyses of incidents with relevant AI actors, to understand the root causes and implement preventive measures. Human AI Configuration, Dangerous or Violent Recommendations MG - 4.2 - 007 Provide external stakeholders with regular updates about the progress, challenges, and improvements made based on their feedback through the use of public venues such as online platforms and communities, and open - source initiatives. Intellectual Property MG - 4.2 - 008 Simulate various scenarios to test GAI system responses and verify intended performance across different situations. MG - 4.2 - 009 Use visualizations to represent the GAI model behavior to ease non - technical stakeholders understanding of GAI system functionality. Human - AI Configuration AI Actors: AI Deployment, AI Design, AI Development, Affected Individuals and Communities, End - Users, Operation and Monitoring, TEVV 1 * MANAGE 4.3: Incidents and errors are communicated to relevant AI actors, including affected communities. Processes for tracking, responding to, and recovering from incidents and errors are followed and documented. Action ID Action Risks MG - 4.3 - 001 Conduct after - action assessments for GAI system incidents to verify incident response and recovery processes are followed and effective. MG - 4.3 - 002 Establish and maintain change management records and procedures for GAI systems, including the reasons for each change, how the change could impact each intended context of use, and step - by - step details of how changes were planned, tested, and deployed. MG - 4.3 - 003 Establish and maintain policies and procedures to record and track GAI system reported errors, near - misses, incidents, and negative impacts. Confabulation, Information Integrity MG - 4.3 - 004 Establish processes and procedures for regular sharing of information about errors, incidents, and negative impacts for each and across contexts, sectors, and AI actors, including the date reported, the context of use, the number of reports for each issue, and assessments of impact and severity. Confabulation, Human AI Configuration, Information Integrity AI Actors: AI Deployment, Affected Individuals and Communities, Domain Experts, End - Users, Human Factors, Operation and Monitoring 2

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62 Appendix A. Primary GAI Considerations 1 The following primary considerations were derived as overarching themes from the GAI PWG 2 consultation process. These considerations (Governance, Pre - Deployment Testing, Content Provenance, 3 and Incident Disclosure) are relevant to any organization designing, developing, and using GAI and also 4 inform the Actions to Manage GAI risks. Information included about the primary considerations is not 5 exhaustive , but highlights the most relevant topics derived from the GAI PWG. 6 Acknowledgments: These considerations could not have been surfaced without the helpful analysis and 7 contributions from the community and NIST staff GAI PWG leads: George Awad, Luca Belli, Mat Heyman, 8 Yoo y oung Lee, Reva Schwartz, and Kyra Yee. 9 Governance 10 A.1.1. Overview 11 Like any other technology system, governance principles and techniques can be used to manage risks 12 related to generative AI models, capabilities, and applications. Organizations may choose to apply their 13 existing risk tiering to GAI systems, or they may opt to revise or update AI system risk levels to address 14 these unique GAI risks. This section describes how organizational governance regimes may be re - 15 evaluated and adjusted for GAI contexts. It also addresses third - party considerations for governing across 16 the AI value chain. 17 A.1.2. Organizational Governance 18 GAI opportunities, risks and long - term performance characteristics are typically less well - understood 19 than non - generative AI tools. and may be perceived and acted upon by humans in ways that vary greatly. 20 Accordingly, GAI may call for different levels of oversight from AI actors or different human - AI 21 configurations in order to manage their risks effectively. Organizations’ use of GAI systems may also 22 warrant additional human review, tracking and documentation, and greater management oversight. 23 AI technology can produce varied outputs in multiple modalities and present many classes of user 24 interfaces. This leads to a broader set of AI actors interacting with GAI systems for widely differing 25 applications and contexts of use. These can include data labeling and preparation, development of GAI 26 models, content moderation, code generation and review, text generation and editing, image and video 27 generation, summarization, search, and chat. These activities can take place within organizational 28 settings or in the public domain. 29 Organizations can restrict AI applications that cause harm, exceed stated risk tolerances, or that conflict 30 with their tolerances or values. Governance tools and protocols that are applied to other types of AI 31 systems can be applied to GAI systems. These plans and actions include: 32 • Accessibility and reasonable 33 accommodations 34 • AI actor credentials and qualifications 35 • Alignment to organizational values 36 • Auditing and assessment 37 • Change - management controls 38 • Commercial use 39 • Data provenance 40

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63 • Data protection 41 • Data retention 42 • Consistency in use of defining key terms 43 • Decommissioning 44 • Discouraging anonymous use 45 • Education 46 • Impact assessments 47 • Incident response 48 • Monitoring 49 • Opt - outs 50 • Risk - based controls 51 • Risk mapping and measurement 52 • Science - backed TEVV practices 53 • Secure software development practices 54 • Stakeholder engagement 55 • Synthetic content detection and 56 labeling tools and techniques 57 • Whistleblower protections 58 • Workforce diversity and 59 interdisciplinary teams 60 61 Establishing acceptable use policies and guidance for the use of GAI in formal human - AI teaming settings 62 as well as different levels of human - AI configurations can help to decrease risks arising from misuse, 63 abuse, inappropriate repurpose, and misalignment between systems and users. These practices are just 64 one example of adapting existing governance protocols for GAI contexts. 65 A.1.3. Third - Party Considerations 66 Organizations may seek to acquire, embed, incorporate, or use open source or proprietary third - party 67 GAI models, systems, or generated data for various applications across an enterprise. Use of these GAI 68 tools and inputs has implications for all functions of the organization – including but not limited to 69 acquisition, human resources, legal, compliance, and IT services – regardless of whether they are carried 70 out by employees or third parties. Many of the actions cited above are relevant and options for 71 addressing third - party considerations. 72 Third party GAI integrations may give rise to increased intellectual property, data privacy, or information 73 security risks, pointing to the need for clear guidelines for transparency and risk management regarding 74 the collection and use of third - party data for model inputs. Organizations may consider varying risk 75 controls for foundation models, fine - tuned models, and embedded tools, enhanced processes for 76 interacting with external GAI technologies or service providers. Organizations can apply standard or 77 existing risk controls and processes to proprietary or open - source GAI technologies, data, and third - party 78 service providers, including acquisition and procurement due diligence, requests for software bills of 79 materials (SBOMs), application of service level agreements (SLAs), and statement on standards for 80 attestation engagement (SSAE) reports to help with third - party transparency and risk management for 81 GAI systems. 82 A.1.4. Pre - Deployment Testing 83 Appendix B. Overview 84 The diverse ways and contexts in which GAI systems may be developed, used, and repurposed 85 complicates risk mapping and pre - deployment measurement efforts. Robust test, evaluation, validation, 86 and verification (TEVV) processes can be iteratively applied – and documented – in early stages of the AI 87

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64 lifecycle and informed by representative AI actors ( see Figure 3 of the AI RMF ). Until new and rigorous 88 early lifecycle TEVV approaches are developed and matured for GAI, organizations may use 89 recommended “pre - deployment testing” practices to measure performance, capabilities, limits, risks, 90 and impacts. This section describes risk measurement and estimation as part of pre - deployment TEVV, 91 and examines the state of play for pre - deployment testing methodologies. 92 Appendix C. Limitations of Current Pre - deployment Test Approaches 93 Currently available pre - deployment TEVV processes used for GAI applications may be inadequate, non - 94 systematically applied, or fail to reflect or mismatched to deployment contexts. For example, the 95 anecdotal testing of GAI system capabilities through video games or standardized tests designed for 96 humans (e.g., intelligence tests, professional licensing exams) does not guarantee GAI system validity or 97 reliability in those domains. Similarly, jailbreaking or prompt - engineering tests may not systematically 98 assess validity or reliability risks. 99 Measurement gaps can arise from mismatches between laboratory and real - world settings. Current 100 testing approaches often remain focused on laboratory conditions or restricted to benchmark test 101 datasets and in silico techniques that may not extrapolate well to — or directly assess GAI impacts in – 102 real world conditions. For example, current measurement gaps for GAI make it difficult to precisely 103 estimate its potential ecosystem - level or longitudinal risks and related political, social, and economic 104 impacts. Gaps between benchmarks and real - world use of GAI systems may likely be exacerbated due to 105 prompt sensitivity and broad heterogeneity of contexts of use. 106 A.1.5. Structured Public Feedback 107 Structured public feedback can be used to evaluate whether GAI systems are performing as intended 108 and to calibrate and verify traditional measurement methods. Examples of structured feedback include, 109 but are not limited to: 110 • Participatory Engagement Methods : Methods used to solicit feedback from civil society groups, 111 affected communities, and users, including focus groups, small user studies, and surveys. 112 • Field Testing : Methods used to determine how people interact with, consume, use, and make 113 sense of AI - generated information, and subsequent actions and effects, including UX, usability, 114 and other structured, randomized experiments. 115 • AI Red - teaming: A structured testing exercise used to probe an AI system to find flaws and 116 vulnerabilities such as inaccurate, harmful, or discriminatory outputs, often in a controlled 117 environment and in collaboration with system developers. 118 Information gathered from structured public feedback can inform design, implementation, deployment 119 approval , maintenance, or decommissioning decisions. Results and insights gleaned from these exercises 120 can serve multiple purposes, including improving data quality and preprocessing, bolstering governance 121 decision making, and enhancing system documentation and debugging practices. When implementing 122 feedback activities, organizations should follow human subjects research requirements and best 123 practices such as informed consent and subject compensation. 124

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65 C.1.1.1. Participatory Engagement Methods 125 On an ad hoc or more structured basis, organizations can design and use a variety of channels to engage 126 external stakeholders in product development or review. Focus groups with select experts can provide 127 feedback on a range of issues. Small user studies can provide feedback from representative groups or 128 populations. Anonymous surveys can be used to poll or gauge reactions to specific features. Participatory 129 engagement methods are often less structured than field testing or red teaming, and are more 130 commonly used in early stages of AI or product development. 131 Appendix D. Field Testing 132 Field testing involves structured settings to evaluate risks and impacts and to simulate the conditions 133 under which the GAI system will be deployed. Field style tests can be adapted from a focus on user 134 preferences and experiences towards AI risks and impacts – both negative and positive. When carried 135 out with large groups of users, these tests can provide estimations of the likelihood of risks and impacts 136 in real world interactions. 137 Organizations may also collect feedback on outcomes, harms, and user experience directly from users in 138 the production environment after a model has been released, in accordance with human subject 139 standards such as informed consent and compensation. Organizations should follow applicable human 140 subjects research requirements, and best practices such as informed consent and subject compensation, 141 when implementing feedback activities. 142 Appendix E. AI Red - teaming 143 AI red - teaming exercises are often conducted in a controlled environment and in collaboration with AI 144 developers building AI models. AI red - teaming can be performed before or after AI models or systems 145 are made available to the broader public; this section focuses on red - teaming in pre - deployment 146 contexts. 147 The quality of AI red - teaming outputs is related to the background and expertise of the AI red - team 148 itself. Demographically and interdisciplinarily diverse AI red - teams can be used to identify flaws in the 149 varying contexts where GAI will be used. For best results, AI red - teams should demonstrate domain 150 expertise, and awareness of socio - cultural aspects within the deployment context. AI red - teaming results 151 should be given additional analysis before they are incorporated into organizational governance and 152 decision making, policy and procedural updates, and AI risk management efforts. 153 Various types of AI red - teaming may be appropriate, depending on the use case: 154 • General P ublic: Performed by general users (not necessarily AI or technical experts) who are 155 expected to use the model or interact with its outputs, and who bring their own lived 156 experiences and perspectives to the task of AI red - teaming . These individuals may have been 157 provided instructions and material to complete tasks which may elicit harmful model behaviors. 158 This type of exercise can be more effective with large groups of AI - teamers. 159 • Expert: Performed by specialists with expertise in the domain or specific AI red - teaming context 160 of use (e.g., medicine, biotech, cybersecurity). 161

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66 • Combination: In scenarios when it is difficult to identify and recruit specialists with sufficient 162 domain and contextual expertise, AI red - teaming exercises may leverage both expert and 163 general public participants. For example, expert AI red - teamers could modify or verify the 164 prompts written by general public AI red - teamers. These approaches may also expand coverage 165 of the AI risk attack surface. 166 • Human / AI : Performed by GAI in combinatio n with specialist or non - specialist human teams. 167 GAI - led red - teaming can be more cost effective than human red teamers alone . Human or GAI - 168 led AI red - teaming may be better suited for eliciting different types of harms. 169 A.1.6. Content Provenance 170 Appendix F. Overview 171 GAI technologies can be leveraged for many applications such as content generation and synthetic data. 172 Some aspects of GAI output, such as the production of deepfake content, can challenge our ability to 173 distinguish human - generated content from AI - generated content. To help manage and mitigate these 174 risks, digital transparency mechanisms like provenance data tracking can trace the origin and history of 175 content. Provenance data tracking and synthetic content detection can help provide greater information 176 about both authentic and synthetic content to users, enabling trustworthiness in AI systems. When 177 combined with other organizational accountability mechanisms, digital content transparency can enable 178 processes to trace negative outcomes back to their source, improve information integrity, and uphold 179 public trust. Provenance data tracking and synthetic content detection mechanisms provide information 180 about the origin of content and its history to assist in GAI risk management efforts. 181 Provenance data can include information about generated content’s creators, date/time of creation, 182 location, modifications, and sources, including metadata information. Metadata can be tracked for text, 183 images, videos, audio, and underlying datasets. Provenance data tracking employs various methods and 184 metrics to assess the authenticity, integrity, credibility, intellectual property rights , and potential 185 manipulations in GAI output. Some well - known techniques for provenance data tracking include 186 watermarking , metadata tracking, digital fingerprinting, and human authentication, among others . 187 Appendix G. Provenance Data Tracking Approaches 188 Provenance data tracking techniques for GAI systems can be used to track the lineage and integrity of 189 data inputs, metadata, and AI - generated content. Provenance data tracking records the origin and 190 history for digital content, allowing its authenticity to be determined. It consists of techniques to record 191 metadata as well as perceptible and imperceptible digital watermarks on digital content. Data 192 provenance refers to tracking the origin and history of input data through metadata and digital 193 watermarking techniques. Provenance data tracking processes can include and assist AI actors across the 194 lifecycle who may not have full visibility or control over the various trade - offs and cascading impacts of 195 early - stage model decisions on downstream performance and synthetic outputs. For example, by 196 selecting a given model to prioritize computational efficiency over accuracy, an AI actor may 197 inadvertently affect provenance tracking reliability. Organizational risk management efforts for 198 enhancing content provenance include: 199

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67 • Tracking provenance of training data and metadata for GAI systems; 200 • Documenting provenance data limitations within GAI systems; 201 • Monitoring system capabilities and limitations in deployment through rigorous TEVV processes; 202 • Evaluati ng how humans engage, interact with, or adapt to GAI content (especially in decision 203 making tasks informed by GAI content), and how they react to applied provenance techniques 204 such as perceptible disclosures. 205 Organizations can document and delineate GAI system objectives and limitations to identify gaps where 206 provenance data may be most useful. For instance, GAI systems used for content creation may require 207 watermarking techniques to identify the source of content or metadata management to trace content 208 origins and modifications. Further narrowing of GAI task definitions to include provenance data can 209 enable organizations to maximize the utility of provenance data and risk management efforts. 210 A.1.7. Enhancing Content Provenance through Structured Public Feedback 211 While indirect feedback methods such as automated error collection systems are useful, they often lack 212 the context and depth that direct input from end users can provide. Organizations can leverage feedback 213 approaches described in the Pre - Deployment Testing section to capture input from external sources such 214 as through AI red - teaming. 215 Integrating pre - and post - deployment external feedback into the monitoring process of applications 216 involving AI - generated content can help enhance awareness of performance changes and mitigate 217 potential risks and harms. There are many ways to capture and make use of user feedback – before and 218 after GAI systems are deployed – to gain insights about authentication efficacy and vulnerabilities, 219 impacts of adversarial threats, unintended consequences resulting from the utilization of content 220 provenance approaches, and other unanticipated behavior associated with content manipulation. 221 Organizations can track and document the provenance of datasets to identify instances in which AI - 222 generated data is a potential root cause of performance issues with the GAI system. 223 A.1.8. Incident Disclosure 224 Appendix H. Overview 225 AI incidents can be defined as an event, circumstance, or series of events in which the development, use, 226 or malfunction of one or more AI systems directly or indirectly contribute s to identified harm s . These 227 harms include injury or damage to the health of an individual or group of people; disruption of the 228 management and operation of critical infrastructure; violations of human rights or a breach of 229 obligations under applicable law intended to protect legal and labor rights; or damage to property, 230 communities, or the environment. AI incidents can occur in the aggregate (i.e., for systemic 231 discrimination) or acutely (i.e., for one individual). 232

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68 Appendix I. State of AI Incident Tracking and Disclosure 233 Formal channels do not currently exist to report and document AI incidents. However, a number of 234 publicly - available databases have been created to document their occurrence. These reporting channels 235 make decisions on an ad hoc basis about what kinds of incidents to track. Some, for example, track by 236 amount of media coverage . 237 Documenting, reporting, and sharing information about GAI incidents can help mitigate and prevent 238 harmful outcomes by assisting relevant AI actors in tracing impacts to their source . Greater awareness 239 and standardization of GAI incident reporting could promote this transparency and improve GAI risk 240 management across the AI ecosystem. 241 Appendix J. Documentation and Involvement of AI Actors 242 AI actors should be aware of their roles in reporting AI incidents. To better understand previous incidents 243 and implement measures to prevent similar ones in the future, organizations could consider developing 244 guidelines for publicly available incident reporting which include information about AI actor 245 responsibilities. These guidelines would help AI system operators identify GAI incidents across the AI 246 lifecycle and with AI actors regardless of role . Documentation and review of third party inputs and 247 plugins for GAI systems is especially important for AI actors in the context of incident disclosure; LLM 248 inputs and content delivered through these plugins is often distributed, with inconsistent or insufficient 249 access control. 250 Documentation practices including logging, recording, and analyzing GAI incidents can facilitate 251 smoother sharing of information with relevant AI actors. Regular information sharing, change 252 management records, version history and metadata can also empower AI actors responding to and 253 managing AI incidents. 254

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69 Appendix K. References 255 AI Risks and Trustworthiness, NIST Trustworthy & Responsible AI Resource Center. National Institute of 256 Standards and Technology . 257 https://airc.nist.gov/AI_RMF_Knowledge_Base/AI_RMF/Foundational_Information/3 - sec - characteristics . 258 AI RMF Playbook. National Institute of Standards and Technology . 259 https://airc.nist.gov/AI_RMF_Knowledge_Base/Playbook . 260 AI RMF Profiles. National Institute of Standards and Technology . 261 https://airc.nist.gov/AI_RMF_Knowledge_Base/AI_RMF/Core_And_Profiles/6 - sec - profile . 262 AI Incident Database. https://incidentdatabase.ai/ . 263 AI Risk Management Framework. National Institute of Standards and Technology . 264 https://www.nist.gov/itl/ai - risk - management - framework . 265 AI Risk Management Framework. National Institute of Standards and Technology . Appendix A: 266 Descriptions of AI Actor Tasks, NIST Trustworthy & Responsible AI Resource Center. National Institute of 267 Standards and Technology. 268 https://airc.nist.gov/AI_RMF_Knowledge_Base/AI_RMF/Appendices/Appendix_A#:~:text=AI%20actors% 269 20in%20this%20category,data%20providers%2C%20system%20funders%2C%20product . 270 AI Risk Management Framework. National Institute of Standards and Technology . Appendix B: How AI 271 Risks Differ from Traditional Software Risks. National Institute of Standards and Technology . 272 https://airc.nist.gov/AI_RMF_Knowledge_Base/AI_RMF/Appendices/Appendix_B . 273 Alba, D., (2023) How Fake AI Photo of a Pentagon Blast Went Viral and Briefly Spooked Stocks. 274 Bloomberg . https://www.bloomberg.com/news/articles/2023 - 05 - 22/fake - ai - photo - of - pentagon - blast - 275 goes - viral - trips - stocks - briefly . 276 Atherton, D. (2024) Deepfakes and Child Safety: A Survey and Analysis of 2023 Incidents and Responses. 277 AI Incident Database. https://incidentdatabase.ai/blog/deepfakes - and - child - safety/ . 278 Authenticating AI - Generated Content (2024). Information Technology Industry Council . 279 https://www.itic.org/policy/ITI_AIContentAuthorizationPolicy_122123.pdf . 280 Badyal, N. et al., (2023) Intentional Biases in LLM Responses. arXiv . https://arxiv.org/pdf/2311.07611 . 281 Bing Chat: Data Exfiltration Exploit Explained. Embrace The Red . 282 https://embracethered.com/blog/posts/2023/bing - chat - data - exfiltration - poc - and - fix/ . 283 Bommasani, R. et al., (2022) Picking on the Same Person: Does Algorithmic Monoculture lead to 284 Outcome Homogenization? arXiv . https://arxiv.org/pdf/2211.13972 . 285 Boyarskaya, M. et al., (2020) Overcoming Failures of Imagination in AI Infused System Development and 286 Deployment. arXiv . https://arxiv.org/pdf/2011.13416 . 287 Browne, D. et al., (2023) Securing the AI Pipeline. Mandiant . 288 https://www.mandiant.com/resources/blog/securing - ai - pipeline . 289

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