# AI Governance and Compliance

> **Entry point:** `/faion-net` - invoke for automatic routing.

Comprehensive framework for AI governance, model risk management, and regulatory compliance in MLOps.

## Overview

AI governance ensures AI systems are safe, compliant, ethical, explainable, and properly documented. It sits above MLOps to provide oversight and traceability across the entire AI lifecycle.

## Market Context (2025-2026)

| Metric | Value |
|--------|-------|
| AI Governance Market CAGR | 45.3% (2024-2029) |
| Projected Market Value | $5.8B by 2029 |
| AI Industry CAGR | 30.6% (2025-2032) |
| EU AI Act Full Enforcement | 2026 |

## Regulatory Landscape

### EU AI Act (Effective 2026)

| Risk Level | Requirements | Examples |
|------------|--------------|----------|
| Unacceptable | Prohibited | Social scoring, manipulative AI |
| High-Risk | Full compliance | Healthcare, HR, credit scoring |
| Limited | Transparency only | Chatbots, deepfakes |
| Minimal | No requirements | Spam filters, games |

### Key Compliance Frameworks

| Framework | Focus | Applicability |
|-----------|-------|---------------|
| EU AI Act | Risk-based regulation | EU market access |
| NIST AI RMF | Risk management | US federal, voluntary |
| ISO/IEC 42001 | AI management system | Global certification |
| ISO/IEC 38507 | AI governance | Board-level oversight |
| SOC 2 Type II | Security controls | SaaS providers |

## Core Governance Pillars

### 1. Model Governance

- Model registration and inventory
- Version control and lineage tracking
- Approval workflows for deployment
- Model retirement procedures

### 2. Audit Trails

- Immutable logs for all decisions
- Training data provenance
- Parameter and hyperparameter tracking
- Retraining and decommissioning events

### 3. Responsible AI

- Bias detection and mitigation
- Fairness metrics monitoring
- Explainability (SHAP, LIME)
- Human oversight mechanisms

### 4. Data Governance

- Consent management
- Data retention policies
- Right to deletion (GDPR)
- Data quality monitoring

## Governance Tools Ecosystem

### Enterprise Platforms

| Platform | Strengths | Best For |
|----------|-----------|----------|
| DataRobot | Unified governance, auto compliance reports | Enterprise MLOps |
| Domino | Audit trails, reproducibility | Regulated industries |
| Credo AI | Policy workflows, EU AI Act alignment | Compliance-first orgs |
| Weights & Biases | Experiment tracking, lineage | ML teams |

### Open Source Tools

| Tool | Purpose |
|------|---------|
| Fairlearn | Bias assessment and mitigation |
| AI Fairness 360 | Comprehensive fairness toolkit |
| SHAP | Model explainability |
| LIME | Local interpretable explanations |
| Captum | PyTorch model interpretability |
| MLflow | Model tracking and registry |

## Implementation Approach

```
Phase 1: Assessment
├── Inventory all AI systems
├── Classify by risk level
├── Gap analysis vs. requirements
└── Prioritize remediation

Phase 2: Infrastructure
├── Deploy governance platform
├── Configure audit logging
├── Set up model registry
└── Implement access controls

Phase 3: Processes
├── Define approval workflows
├── Create documentation templates
├── Train teams
└── Establish review cadence

Phase 4: Continuous
├── Monitor bias and drift
├── Regular compliance audits
├── Update for new regulations
└── Report to stakeholders
```

## Files in This Folder

| File | Purpose |
|------|---------|
| [README.md](README.md) | This overview |
| [checklist.md](checklist.md) | Compliance verification checklist |
| [examples.md](examples.md) | Real-world implementation examples |
| [templates.md](templates.md) | Documentation templates |
| [llm-prompts.md](llm-prompts.md) | Prompts for governance tasks |

## Key Metrics

### Compliance Metrics

| Metric | Target |
|--------|--------|
| Model documentation coverage | 100% |
| Audit trail completeness | 100% |
| Bias assessment frequency | Per deployment |
| Human review rate (high-risk) | 100% |

### Operational Metrics

| Metric | Target |
|--------|--------|
| Time to compliance report | < 1 day |
| Model approval cycle time | < 5 days |
| Incident response time | < 4 hours |
| Audit finding resolution | < 30 days |

## Risk Categories

### Technical Risks

- Model drift degrading performance
- Data quality issues
- Security vulnerabilities
- Infrastructure failures

### Compliance Risks

- Regulatory non-compliance
- Documentation gaps
- Audit failures
- Unauthorized deployments

### Ethical Risks

- Algorithmic bias
- Privacy violations
- Lack of transparency
- Uncontrolled AI behavior

## Integration Points

| System | Integration Type |
|--------|-----------------|
| MLOps Pipeline | Pre-deployment gates |
| CI/CD | Compliance checks |
| Data Catalog | Lineage tracking |
| SIEM | Security monitoring |
| GRC Platform | Risk reporting |

## Related Resources

- [EU AI Act Compliance](../eu-ai-act-compliance/README.md) - Detailed EU AI Act guide
- [LLM Observability](../llm-observability/README.md) - Monitoring and tracing
- [Model Evaluation](../model-evaluation/README.md) - Testing and benchmarks


## Agent Selection

| Task | Model | Rationale |
|------|-------|----------|
| Governance framework | opus | Strategic planning |
| Policy development | sonnet | Document creation |
| Audit readiness | sonnet | Compliance checking |

## Sources

- [Keyrus: AI in 2026 - Building Trustworthy AI Systems](https://keyrus.com/us/en/insights/ai-in-2026-how-to-build-trustworthy-safe-and-governed-ai-systems-noram)
- [Governance Intelligence: AI Redefining Compliance 2026](https://www.governance-intelligence.com/regulatory-compliance/how-ai-will-redefine-compliance-risk-and-governance-2026)
- [Atlan: AI Model Governance Guide](https://atlan.com/know/ai-readiness/ai-model-governance/)
- [Digicrome: Responsible AI and Governance 2026](https://www.digicrome.com/blog/responsible-artificial-intelligence-and-governance-2026)
- [Splunk: AI Governance Platforms 2026](https://www.splunk.com/en_us/blog/learn/ai-governance-platforms.html)
