Think your AI governance program is ready for scrutiny? Research shows 74% of enterprise leaders believe they could pass an AI compliance audit today, yet only 27% have fully mature governance programs. This isn't a confidence problem, it's a documentation problem.
You need a governance policy that can withstand both auditor questions and regulatory review. Here's a template you can adapt immediately.
Purpose of the Template
This AI governance policy template establishes the formal framework auditors expect when evaluating your AI program maturity. It addresses five common gaps: funding allocation, agentic AI oversight, accountability structures, regulatory readiness, and measurable business outcomes.
Use this template to:
- Document your AI governance structure before your next SOC 2 Type II or ISO/IEC 27001 surveillance audit
- Respond to customer security questionnaires about AI controls
- Establish baseline requirements for third-party AI risk assessment
- Create the foundation for AI-specific incident response procedures
This isn't a strategic vision document. It's the operational policy proving your governance program exists beyond budget allocation.
Prerequisites
Before customizing this template, ensure you have:
Organizational Clarity:
- An identified executive owner for AI adoption decisions (not just "the CIO", the specific person)
- A defined scope of what constitutes "AI" in your environment (machine learning models, generative AI tools, autonomous agents)
- A list of AI systems currently in production or testing
Technical Inventory:
- Documentation of third-party tools with embedded AI capabilities
- Classification of AI agents by risk level (if you're running agentic AI in production)
- An existing incident response plan that you'll extend for AI-specific scenarios
Compliance Context:
- Current regulatory requirements applicable to your industry and geography
- Relevant controls from your ISO/IEC 27001 Statement of Applicability or SOC 2 trust services criteria
- Board-level approval to implement AI governance requirements
If you don't have the technical inventory yet, start there. You can't govern what you haven't identified.
The Template
AI GOVERNANCE POLICY
Version 1.0 | Effective Date: [DATE]
1. PURPOSE AND SCOPE
This policy establishes requirements for the governance of artificial intelligence systems operated by [ORGANIZATION NAME], including machine learning models, generative AI applications, and autonomous agents deployed in production or testing environments.
This policy applies to:
- All AI systems developed internally
- Third-party software products with embedded AI capabilities
- AI agents with decision-making authority
- Data sets used for AI training or fine-tuning
2. ROLES AND RESPONSIBILITIES
2.1 Executive Accountability
[TITLE, e.g., Chief Information Officer] serves as the designated AI Governance Executive, responsible for:
- Approving AI system deployments to production
- Reviewing AI-related incidents and risk assessments
- Reporting AI governance status to the Board quarterly
2.2 AI Risk Committee
The AI Risk Committee, comprising [ROLES, e.g., CIO, CISO, Legal Counsel, Data Protection Officer], meets [FREQUENCY] to:
- Evaluate new AI adoption requests
- Review third-party AI risk assessments
- Approve exceptions to this policy
2.3 System Owners
Each AI system must have a designated System Owner responsible for:
- Maintaining current documentation of AI capabilities and limitations
- Conducting risk assessments before deployment
- Monitoring performance metrics and bias indicators
3. AI SYSTEM CLASSIFICATION AND OVERSIGHT
3.1 Risk Tiers
AI systems are classified into four tiers based on decision impact:
Tier 1 (Human Review Required): AI agents that make decisions affecting customer data, financial transactions, or regulatory obligations must route recommendations to a human reviewer before execution.
Tier 2 (Human Oversight): AI systems with moderate business impact require human monitoring with the ability to intervene.
Tier 3 (Automated with Logging): Low-risk AI systems may operate autonomously with comprehensive audit logging.
Tier 4 (Informational): AI tools providing recommendations without automated action.
3.2 Deployment Requirements
Before production deployment, System Owners must document:
- Risk tier classification and justification
- Data sources and training methodology
- Known limitations and failure modes
- Monitoring and alerting thresholds
- Rollback procedures
4. THIRD-PARTY AI RISK MANAGEMENT
4.1 Vendor Assessment
Before procuring software with embedded AI capabilities, the organization must:
- Obtain vendor documentation of AI functionality and data handling
- Assess AI-related risks using the standard vendor risk assessment process
- Document contractual provisions for AI transparency and incident notification
4.2 Ongoing Monitoring
System Owners of third-party AI tools must:
- Track vendor AI capability changes through release notes
- Re-assess risk when vendors add new AI features
- Maintain inventory of all third-party AI dependencies
5. INCIDENT RESPONSE FOR AI SYSTEMS
AI-specific incidents include:
- Unintended automated actions affecting customer data or business operations
- Detection of bias or discriminatory outputs
- AI system behavior outside documented parameters
- Third-party AI vendor security incidents
Incident response procedures extend the organization's standard incident response plan with AI-specific investigation steps:
- Preserve AI decision logs and model state
- Document inputs that triggered unintended behavior
- Assess whether model retraining is required
- Notify affected parties per regulatory requirements
6. REGULATORY COMPLIANCE
The AI Risk Committee monitors regulatory developments in [APPLICABLE JURISDICTIONS] and updates this policy to address new requirements.
Current regulatory obligations include:
[LIST SPECIFIC REQUIREMENTS, e.g., EU AI Act provisions, industry-specific regulations]
7. [PERFORMANCE EVALUATION](/glossary/performance-evaluation)
The AI Governance Executive reports the following metrics to the Board quarterly:
- Count of AI systems by risk tier
- AI-related incidents and resolution status
- Third-party AI vendor risk assessment completion rate
- Regulatory compliance status
8. POLICY REVIEW
This policy is reviewed annually or when regulatory requirements change.
APPROVED BY:
[NAME, TITLE] | [DATE]
How to Customize It
Section 1 (Purpose and Scope): Replace bracketed placeholders with your organization's name and specific AI technologies. If you're not running agentic AI yet, remove "autonomous agents" but keep the third-party clause, most SaaS tools now embed AI features.
Section 2 (Roles): Name the actual person, not just the title. If you're placing both adoption authority and accountability with one executive, document the escalation path to the Board for high-risk decisions. If your organization has split these responsibilities, add a second executive role here.
Section 3 (Classification): The four-tier framework comes from governance-mature organizations running AI agents in production. If you're earlier in AI adoption, you can collapse this to two tiers (human review required vs. automated with logging). The key is documenting when human oversight is mandatory.
Section 4 (Third-Party Risk): This section addresses what research identifies as the biggest blind spot in enterprise AI governance. Customize the vendor assessment requirements to match your existing third-party risk management process. Don't create a separate workflow, extend what you already do for SaaS vendors.
Section 5 (Incident Response): Reference your existing incident response plan by name and document number. This section extends it, not replaces it.
Section 6 (Regulatory Compliance): List the specific regulations that apply to your organization. If you operate in the EU, name the EU AI Act requirements relevant to your AI use cases. If you're in a regulated industry, cite the agency guidance (FDA, FTC, FINRA, etc.).
Section 7 (Metrics): Choose metrics you can actually measure today. If you can't track third-party AI vendor assessments yet, replace that metric with "percentage of AI systems with documented risk classifications."
Validation Steps
After customizing the template, validate it against audit readiness:
Control of Documented Information Check:
- Assign a document number consistent with your ISO/IEC 27001 control of documented information process
- Add this policy to your next management review agenda
- Upload to your compliance documentation repository where auditors can access it
Operational Test:
- Walk through the policy with the next AI tool purchase request
- Verify that the roles you've named can actually perform the responsibilities you've assigned
- Confirm that your incident response team knows how to preserve AI decision logs
Stakeholder Review:
- Have your Lead Auditor review the policy during your next surveillance audit planning meeting
- Present the risk tier framework to your AI Risk Committee (or equivalent) and get their approval on the classification criteria
- Confirm with Legal that the regulatory compliance section covers your actual obligations
Gap Identification:
- Compare your current AI inventory against the requirements in Section 3.2
- Identify which systems lack the required deployment documentation
- Create a Risk Treatment Plan for closing those gaps before your next audit
The gap between governance confidence and maturity shows up when auditors ask for evidence. This template gives you the documented framework they expect to see, but only if you can prove you're following it. Your next step is generating that proof.
