Purpose of the Template
Your compliance program may not be equipped to manage systems that learn from data, evolve, and produce outputs that aren't always explainable. If your organization uses AI in hiring, credit decisions, customer service, or risk assessment, you're accountable for outcomes that traditional controls might miss.
This policy template integrates AI governance into your existing compliance framework, using ISO/IEC 42001 as its foundation. It establishes accountability, defines monitoring requirements, and creates audit trails for AI systems throughout their lifecycle. You can adapt it to align with the European Union's AI Act, GDPR enforcement patterns, or your organization's risk management structure.
The template addresses a key issue: AI-related compliance failures often arise gradually through data drift or model behavior changes, not through obvious control breakdowns. A hiring algorithm may produce discriminatory outcomes without intentional bias. A customer complaint prioritization system may systematically deprioritize certain categories while performance metrics appear stable. This policy ensures you're monitoring for those silent failures.
Prerequisites
Before implementing this template, confirm you have:
- Executive Sponsorship: AI governance requires collaboration between compliance, legal, and technical functions. You need authority to establish cross-functional accountability.
- AI System Inventory: Document all AI applications that influence decisions affecting customers, employees, or business operations.
- Existing Compliance Framework: This policy integrates with your current program. You should have established processes for risk assessment, control documentation, and audit preparation.
- Technical Liaison: Identify a counterpart in IT or data science who can translate model behavior into compliance language.
You don't need ISO/IEC 42001 certification to use this template. The standard provides structure; the policy creates enforceable requirements.
The Template
AI GOVERNANCE POLICY
Effective Date: [DATE]
Policy Owner: Chief Compliance Officer
Review Cycle: Annual
1. PURPOSE AND SCOPE
This policy establishes requirements for the governance, risk management, and compliance oversight of artificial intelligence systems deployed within [ORGANIZATION NAME]. It applies to all AI systems that influence decisions affecting customers, employees, suppliers, or regulatory obligations.
For this policy, an AI system is any application that uses machine learning, natural language processing, computer vision, or algorithmic decision-making to automate or augment business processes.
2. ROLES AND RESPONSIBILITIES
2.1 AI Governance Committee
The organization shall establish an AI Governance Committee comprising representatives from Compliance, Legal, IT, and relevant business units. The committee meets quarterly and is responsible for:
- Approving AI system deployments that meet high-risk criteria
- Reviewing incident reports and compliance findings
- Ensuring alignment with regulatory obligations
- Escalating material risks to the Board Risk Committee
2.2 AI System Owners
Each AI system must have a designated System Owner accountable for:
- Maintaining system documentation (see Section 4)
- Conducting quarterly risk assessments
- Implementing corrective actions identified through monitoring
- Reporting system changes that may affect compliance obligations
2.3 Compliance Function
The Compliance function is responsible for:
- Defining risk criteria and monitoring requirements
- Conducting independent reviews of high-risk systems
- Validating audit trails and documentation completeness
- Coordinating regulatory inquiries related to AI systems
3. RISK CLASSIFICATION
All AI systems shall be classified using the following risk tiers:
High-Risk Systems: AI applications that directly influence employment decisions, credit or insurance eligibility, legal obligations, or regulatory reporting. These systems require pre-deployment approval, continuous monitoring, and quarterly compliance reviews.
Medium-Risk Systems: AI applications affecting customer experience, operational efficiency, or internal resource allocation without direct legal or employment impact. These systems require documentation and annual compliance reviews.
Low-Risk Systems: AI applications with minimal decision-making authority or limited scope of impact. These systems require basic documentation only.
Classification must be reviewed whenever system functionality, data sources, or use cases change materially.
4. DOCUMENTATION REQUIREMENTS
For each AI system, the System Owner shall maintain:
- System Description: Purpose, business justification, and decision-making role
- Data Inventory: Sources, categories of data processed, data retention periods
- Model Logic: Algorithm type, training methodology, key variables influencing outputs
- Risk Assessment: Identified compliance, legal, and ethical risks with mitigation controls
- Change Log: Record of all material changes to data, model parameters, or deployment scope
- Monitoring Results: Evidence of ongoing performance validation and bias testing
Documentation must be sufficient to demonstrate compliance during regulatory inquiries or audits.
5. MONITORING AND VALIDATION
5.1 Continuous Monitoring
High-risk systems require automated monitoring for:
- Output consistency and unexpected distribution shifts
- Adverse impact across protected categories (where applicable)
- Data quality degradation or source changes
- Model performance against established benchmarks
Monitoring results must be reviewed monthly by the System Owner and quarterly by the Compliance function.
5.2 Human Oversight
Systems classified as high-risk must include human review mechanisms for:
- Decisions with material adverse impact on individuals
- Outputs that deviate from expected ranges or historical patterns
- Situations where the system cannot provide adequate explanation for its recommendation
5.3 Bias Testing
Where AI systems influence decisions affecting individuals, the organization shall conduct periodic bias testing to identify disparate impact across protected categories. Testing frequency depends on risk classification and regulatory requirements.
6. INCIDENT RESPONSE
AI-related incidents include:
- System outputs that violate legal or regulatory obligations
- Confirmed bias or discriminatory outcomes
- Data breaches or unauthorized access to training data
- Material performance degradation affecting compliance obligations
Incidents must be reported to the Compliance function within 24 hours. The AI Governance Committee shall review all incidents and determine whether regulatory notification is required.
7. VENDOR MANAGEMENT
Third-party AI systems and services are subject to this policy. Contracts with AI vendors must include:
- Documentation access rights
- Audit and inspection provisions
- Incident notification requirements
- Termination rights if the vendor cannot meet compliance obligations
8. TRAINING
Personnel involved in AI system deployment, operation, or oversight must complete annual training covering:
- AI-related compliance risks
- Documentation and monitoring requirements
- Incident identification and escalation procedures
9. POLICY REVIEW
This policy shall be reviewed annually or following material regulatory developments, enforcement actions, or changes to the organization's AI deployment strategy.
Customization Options
Risk Classification Criteria: Adjust Section 3 based on your regulatory environment. If you're subject to the EU AI Act, align your high-risk definition with the Act's prohibited and high-risk categories. In highly regulated sectors like financial services or healthcare, consider adding sector-specific risk factors.
Monitoring Frequency: The template specifies monthly and quarterly reviews for high-risk systems. Increase frequency if you're in a jurisdiction with active AI enforcement. Decrease frequency for lower-risk environments, but never eliminate monitoring entirely.
Committee Structure: If your organization already has a Model Risk Management Committee or Technology Risk Committee, integrate AI governance into that structure rather than creating a standalone committee. The key requirement is cross-functional representation.
Documentation Depth: Section 4 establishes baseline documentation. If you're pursuing ISO/IEC 42001 certification, add specific references to the standard's control objectives. If you're focused purely on regulatory compliance, ensure your documentation can answer regulator questions about how the system works and how you're managing risks.
Vendor Language: Section 7 provides basic vendor management requirements. If you rely heavily on third-party AI services, expand this section to address model transparency, data processing agreements, and liability allocation.
Validation Steps
After implementing this policy, validate its effectiveness through these steps:
1. Inventory Reconciliation: Compare your AI system inventory against actual deployments. Shadow IT is common with AI tools. Ensure nothing is operating outside your governance framework.
2. Documentation Audit: Select three high-risk systems and verify that all required documentation exists and is current. If you can't answer basic questions about how a system works or what data it uses, your documentation is insufficient.
3. Monitoring Test: Review monitoring results for one high-risk system over the past quarter. Confirm that someone actually reviewed the results and that anomalies triggered investigation. Monitoring without review creates audit risk, not compliance.
4. Committee Effectiveness: Attend one AI Governance Committee meeting. Verify that the committee is making decisions, not just receiving updates. If the committee has never rejected or modified an AI deployment, it's not functioning as a control.
5. Incident Response Drill: Simulate an AI-related incident (a hiring algorithm produces discriminatory outcomes; a customer service bot provides inaccurate information that violates disclosure rules). Verify that your incident response procedures work and that escalation paths are clear.
6. Regulatory Alignment: Map your policy requirements to applicable regulations. If you're subject to the EU AI Act, confirm your high-risk classification aligns with the Act's risk categories. If you're under GDPR, verify that your data inventory and processing documentation meet GDPR standards.
This policy won't prevent every AI-related compliance failure. However, it ensures you're monitoring for the risks that matter, documenting the decisions you're making, and establishing accountability before a regulator asks why you didn't.
