AI合规扫描

Inventory runtime AI use and static AI-related dependencies

air-blackbox discover --scan-path .

AI-BOM and Dependency Discovery

air-blackbox discover combines runtime-observed models, providers, and tools with static dependency scanning from your project:

air-blackbox discover --scan-path . --format table
air-blackbox discover --scan-path . --format cyclonedx
air-blackbox discover --scan-path . --format spdx

Formats:

• --format table is the human-readable inventory.
• --format json remains an alias for CycloneDX 1.6 JSON.
• --format cyclonedx emits CycloneDX 1.6 JSON.
• --format spdx emits SPDX 2.3 JSON.

Static scanning supports requirements.txt, pyproject.toml, package.json, and package-lock.json. requirements.txt and pyproject.toml provide declared direct Python dependencies only. package.json provides direct npm dependencies. package-lock.json v2/v3 can provide installed direct and transitive npm dependencies. Python transitive resolution is not performed, and discovery does not call the network, pip, npm, Poetry, or other package managers. devDependencies are currently excluded.

Each package in a reliable dependency graph is classified independently, so transitive AI libraries can be detected:

application
  -> wrapper-package
     -> openai

Custom AI-library rules can extend or override the built-in classifier:

air-blackbox discover \
  --scan-path . \
  --ai-libraries custom-ai-libraries.yaml \
  --format cyclonedx

version: 1
packages:
  python:
    my-ai-sdk:
      category: llm-sdk
      provider: Example AI
      reason: Internal AI SDK
  npm:
    "@example/ai-client":
      category: llm-sdk
      provider: Example AI
      reason: Internal AI client

Custom rules extend defaults; a rule with the same ecosystem and normalized package name overrides the default. Python names use PEP 503 normalization, and npm scoped package names such as @example/ai-client are supported. Invalid explicit classifier configuration exits with an error.

Use --output for machine-readable files:

air-blackbox discover \
  --scan-path . \
  --format spdx \
  --output sbom.spdx.json

Machine-readable output goes to the file, warnings go to stderr, JSON files are UTF-8 and end with a newline, and table output cannot be combined with --output.

Runtime model components include the model name, provider when observed, and explicit model version when available. AIR record or schema version is never treated as the model version.

Current limitations: no Python transitive resolution, no package-manager or network resolution, package-lock v2/v3 is the reliable transitive npm source, SPDX 2.3 represents models as packages plus annotations, and formal schema validation is not yet part of the test suite.

Full stack (Gateway + Episode Store + Policy Engine + observability):

git clone https://github.com/airblackbox/air-platform.git
cd air-platform
cp .env.example .env      # add OPENAI_API_KEY
make up                   # running in ~8 seconds

• Traces: localhost:16686 (Jaeger)
• Metrics: localhost:9091 (Prometheus)
• Episodes: localhost:8081 (Episode Store API)

Deploy on Kubernetes

helm install air deploy/helm/air-gateway \
  --set providerURL=https://api.openai.com \
  --set vault.existingSecret=air-vault-creds

Ships with 2 replicas, pod anti-affinity, health probes, and optional HPA. See deploy/HA.md for the high-availability story, including how per-replica audit chains stay independently verifiable.

Install

pip install air-blackbox[gate]   # includes Ed25519 via cryptography

Quickstart

```bash pip install air-blackbox

Verify the tamper-evident chain (zero config: reads the gateway's local key)

air-blackbox replay --verify

Generate an evidence package for audit or regulator review

air-blackbox export # JSON summary, HMAC attestation air-blackbox export --format evidence # self-verifying ZIP + standalone verify.py ```