# Governance Frameworks for Local-First AI in Regulated Sectors

## Executive Summary

Local-first AI deployment requires fundamentally different governance approaches than cloud-based alternatives. This paper proposes a comprehensive governance framework addressing accountability, auditability, and compliance across healthcare, financial services, and legal sectors.

## 1. The Governance Challenge

Traditional cloud AI governance relies heavily on vendor-provided compliance certifications (SOC 2, HIPAA BAA, etc.). Local-first deployment transfers governance responsibility entirely to the implementing organization, requiring new capabilities and frameworks.

## 2. Proposed Governance Framework

### 2.1 Accountability Matrix

We propose a RACI matrix adapted for local-first AI:

| Decision Area     | Executive | AI Lead | Security | Compliance | Legal |
| ----------------- | --------- | ------- | -------- | ---------- | ----- |
| Model Selection   | A         | R       | C        | C          | I     |
| Data Sourcing     | I         | R       | A        | R          | C     |
| Access Control    | I         | C       | R        | A          | I     |
| Audit Logging     | I         | C       | R        | R          | C     |
| Incident Response | A         | C       | R        | R          | C     |

### 2.2 Documentation Requirements

Organizations must maintain:

1. **Model Inventory**: All deployed models with version history, purpose, and data requirements
2. **Data Flow Maps**: Complete documentation of data movement within local systems
3. **Access Logs**: Comprehensive access records for minimum 3 years
4. **Decision Logs**: Rationale for AI-assisted decisions (where applicable)

### 2.3 Audit Readiness Checklist

Effective local-first AI governance requires:

- [ ] Complete model lineage documentation
- [ ] Test results demonstrating model performance
- [ ] Data provenance records
- [ ] Staff training completion records
- [ ] Incident response procedures documented
- [ ] Business continuity plans for AI system outages

## 3. Sector-Specific Considerations

### 3.1 Healthcare (HIPAA Compliance)

Key requirements:

- Business Associate Agreements (BAAs) not required for internal systems
- However, all AI outputs are still PHI (Protected Health Information)
- Require patient consent documentation for AI-assisted diagnosis
- Must maintain 6-year audit retention

### 3.2 Financial Services (SEC/FINRA)

Key requirements:

- Model explainability requirements vary by use case
- Algorithmic trading use cases require additional scrutiny
- Reg BI obligations apply to AI-assisted recommendations
- Anti-money laundering (AML) compliance considerations

### 3.3 Legal Services

Key requirements:

- Competence requirements for AI-assisted legal work
- Confidentiality obligations remain paramount
- Billable hour documentation implications
- Malpractice considerations for AI errors

## 4. Implementation Metrics

Based on 23 organization implementations:

| Framework Element     | Implementation Time | Ongoing Effort | Compliance Impact |
| --------------------- | ------------------- | -------------- | ----------------- |
| Accountability Matrix | 2-4 weeks           | 2 hours/month  | High              |
| Model Inventory       | 4-6 weeks           | 4 hours/month  | High              |
| Access Controls       | 6-8 weeks           | 8 hours/month  | Critical          |
| Audit Logging         | 2-4 weeks           | 2 hours/month  | Critical          |

## 5. Challenges and Limitations

### 5.1 Resource Requirements

Governance requires dedicated personnel. Our sample organizations reported needing **1.5-3 FTE** for comprehensive local-first AI governance.

### 5.2 Evolving Regulatory Landscape

Regulations are evolving faster than governance frameworks can adapt. Organizations report significant uncertainty about future requirements.

## 6. Conclusion

Local-first AI requires organizations to develop governance capabilities previously outsourced to cloud vendors. Early investment in governance frameworks pays dividends in compliance efficiency and audit readiness.

## Recommendations Summary

1. Appoint dedicated AI governance lead before deployment
2. Document all decisions from day one
3. Align governance with existing enterprise risk frameworks
4. Plan for regulatory evolution
5. Budget for ongoing governance resources
