AI Governance Policy Template

The AI governance policy is the top-level document that establishes how the organization governs the development, deployment, and use of AI systems. It is the foundation for all subsequent procedures, controls, and decisions.

Policy structure:

1. Purpose: Why the organization needs an AI governance policy
2. Scope: Which AI systems, organizational units, and lifecycle stages are covered
3. Principles: High-level commitments (fairness, accountability, transparency, privacy, safety, human oversight)
4. Roles and responsibilities: Who does what across the AI lifecycle
5. Lifecycle controls: What governance activities happen at each stage
6. Approval and review: Decision authority, exception process, periodic review

The policy should be 5-15 pages. Longer policies become unread. Shorter ones lack the detail needed for operational use. Approved by senior leadership, reviewed annually.

Use the AI governance learning module for implementation patterns and reference the ISO 42001 framework for control alignment.

Scope must be specific. Vague scope ("all AI systems") fails operational use. Effective scope definitions:

• By AI capability type: Generative AI, predictive analytics, computer vision, recommendation systems, natural language processing, automated decision-making
• By risk level: High-stakes (hiring, credit, healthcare), medium-stakes (productivity, internal operations), low-stakes (spell-check, autocomplete)
• By deployment context: Customer-facing, internal, third-party services, embedded in products
• By data sensitivity: Personal data, financial data, health data, business confidential

Common policy scope: "This policy applies to all AI systems developed by, procured by, or used within the organization that process organizational or customer data, make decisions affecting individuals, or are integrated into products or services."

Out of scope: typically things like spell-check, basic search ranking, and other low-risk AI utilities. Exclusions should be explicit and justified.

Principles articulate the organization's commitments. They guide decisions when specific procedures do not exist. Common principles in AI governance policies:

• Fairness: AI systems are designed and deployed to avoid unjust impact on individuals or groups
• Accountability: Clear ownership exists for every AI system, with named individuals accountable for outcomes
• Transparency: AI systems are documented; affected individuals are informed about AI involvement in decisions when material
• Privacy: AI systems comply with applicable privacy laws and follow data minimization principles
• Safety and reliability: AI systems are tested for robustness, monitored in production, and have fallback procedures
• Human oversight: High-stakes AI decisions involve meaningful human review
• Continuous improvement: AI systems are monitored, evaluated, and improved over time

Principles should be aspirational but actionable. "We strive for fairness" is not actionable. "We test for disparate impact across protected demographic groups before deployment" is.

The policy defines required activities at each AI lifecycle stage. Sample structure:

• Plan and design: Use case approval, risk and impact assessment, regulatory analysis, stakeholder consultation
• Data collection and preparation: Data sourcing approval, privacy review, bias assessment, quality validation
• Model development: Architecture review, training methodology documentation, validation plan
• Validation and testing: Performance testing, bias testing, robustness testing, security testing, human oversight design
• Deployment: Pre-deployment review and approval, rollout plan, rollback procedures, customer communication
• Operation and monitoring: Performance monitoring, drift detection, incident response, periodic revalidation
• Retirement: Decommissioning procedures, data retention, model artifact management

Each stage cites the procedures that operationalize the controls. The policy stays high-level; procedures contain the details. This separation makes the policy stable while procedures evolve.

Effective AI governance requires defined roles across the lifecycle:

• AI System Owner: Accountable for the AI system across its lifecycle. Typically a product or engineering leader.
• AI Risk Officer: Coordinates risk and impact assessments, tracks risk treatment
• AI Ethics Lead: Reviews fairness, transparency, and societal impact concerns
• AI Governance Committee: Cross-functional body that approves new AI use cases, reviews high-risk decisions, oversees policy compliance
• Data Protection Officer: Reviews AI systems for privacy compliance (GDPR, CCPA, DPDPA)
• Security Lead: Reviews AI systems for security risks (adversarial attacks, data poisoning, prompt injection)

For smaller organizations, these roles may be held by the same person. For larger organizations, dedicated roles emerge. The policy should name the roles, describe responsibilities, and reference role assignments (which can change without re-approving the policy).