Core Components of the AI Governance Program

1. AI Policy Framework

Defines the rules, expectations, and boundaries for AI usage.

• Acceptable use guidelines

• Ethical principles (fairness, transparency, accountability)

• Data handling and privacy requirements

• Model lifecycle management standards
   

2. AI Risk Management

A structured approach to identifying, assessing, and mitigating AI-related risks.

• Risk classification (low/medium/high impact models)

• Bias detection and mitigation

• Model validation and testing

• Continuous monitoring for drift and anomalies
   

3. Compliance & Regulatory Alignment

Ensures AI systems meet legal and industry requirements.

• GDPR, HIPAA, SOC 2, PCI, and emerging AI-specific regulations

• Documentation and audit readiness

• Third-party AI vendor assessments
   

4. AI Lifecycle Governance

Controls and checkpoints across the entire AI lifecycle.

• Model design and approval

• Data sourcing and quality validation

• Deployment controls

• Ongoing monitoring and retirement procedures
   

5. Human Oversight & Accountability

Defines roles and responsibilities for safe AI operations.

• RACI models for AI decision-making

• Human-in-the-loop (HITL) requirements

• Escalation paths for AI failures or anomalies
   

6. Transparency & Explainability

Ensures AI decisions can be understood and communicated.

• Explainability standards

• Documentation templates

• User-facing disclosures