An AI governance policy should set clear boundaries, assign accountable owners, and explain how an AI system is assessed, approved, monitored, and corrected throughout its lifecycle. Cover the organization’s AI uses—including third-party tools—and tailor controls to each system’s intended use, potential impact, and applicable law.
What an AI governance policy needs to do
A useful policy is more than a statement of principles: it establishes who may make decisions about AI and what evidence or safeguards are required before and after a system is used. NIST’s AI Risk Management Framework (AI RMF) treats governance as a continuing function across an AI system’s lifespan and organizational levels, rather than a one-time approval. Its framework is voluntary guidance, not a universal legal mandate. NIST AI Risk Management Framework
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Use the policy to connect organizational expectations to practical controls. A system that informs a low-impact internal task may need a lighter review than one that affects access to employment, education, healthcare, credit, or essential services. Applicable law and the organization’s role can change which duties apply.
Core sections to include
1. Purpose, scope, and definitions
State why the policy exists, who must follow it, and what counts as an AI system or AI use for internal purposes. Specify whether it covers systems built in-house, acquired from vendors, embedded in products, or accessed through general-purpose tools. Include relevant lifecycle stages—design, development, procurement, deployment, use, evaluation, and monitoring—so that responsibility does not stop at launch. NIST’s FAQ describes its framework as relevant to organizations that design, develop, deploy, or use AI. NIST AI RMF FAQs
2. Principles and restricted uses
Translate organizational commitments into expectations people can apply: respect for human rights, fairness, privacy, transparency, and proportionality to a legitimate aim. Identify prohibited uses and uses that require heightened approval or safeguards. UNESCO recommends that AI use not go beyond what is necessary to achieve a legitimate aim, and emphasizes risk assessment and human oversight. UNESCO Recommendation on the Ethics of Artificial Intelligence
3. Governance roles and decision rights
Name the accountable executive or governing body, policy owner, system owner, risk reviewers, and approvers. Set out who can authorize production use, accept residual risk, require remediation, or suspend or restrict a system. Give staff a clear escalation route for concerns. NIST’s AI RMF Core calls for defined roles and responsibilities, while recognizing that governance operates throughout the system lifecycle. NIST AI RMF Core
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4. Inventory, intake, and risk classification
Require proposed and existing AI uses to be recorded before they are put into operation. A practical record can capture the intended purpose, affected people, system owner, provider and deployer roles, data categories, key dependencies, and initial risk classification. The exact inventory format is an organizational choice; the point is to make uses visible enough to assign responsibility and apply proportionate review. NIST and OECD guidance supports lifecycle risk management, but neither prescribes one universal inventory form. NIST AI RMF Core OECD AI Principles
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5. Risk and impact assessment
Require an assessment before deployment and whenever a material change could alter the system’s effects—for example, a change in purpose, model, data, users, or operating context. Assess plausible effects on safety, rights, fairness, privacy, security, reliability, misuse, and downstream decisions. Record the evidence, assumptions, mitigations, residual risks, and decision-maker. UNESCO calls for risk assessment to prevent harm; OECD principles support systematic, ongoing risk management across lifecycle phases, taking actors’ roles and context into account. UNESCO Recommendation on the Ethics of Artificial Intelligence OECD AI Principles
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6. Data, privacy, and security
Set rules for lawful data use, data quality and representativeness, access controls, retention, security, and protection of personal information. Explain how teams should manage sensitive data and what review is required before data is used for training, validation, testing, or operation. For high-risk systems within its scope, the EU AI Act includes requirements for appropriate data governance and management practices for training, validation, and testing data; this is not a blanket rule for every AI system. Regulation (EU) 2024/1689, AI Act text
7. Fairness, transparency, and documentation
Specify how teams should test for unfair outcomes, address detected problems, communicate AI involvement where appropriate, and document a system’s purpose, limitations, and relevant operating conditions. Require records that allow reviewers to understand decisions and verify that controls were followed. NIST notes that documentation can support transparency, human review, and accountability; UNESCO identifies fairness and transparency as core ethical themes. NIST AI RMF Core UNESCO Recommendation on the Ethics of Artificial Intelligence
8. Human oversight and authority to intervene
For each use, define whether a qualified person must review outputs, intervene, override a recommendation, or stop the system. Give that person the information, authority, and training needed to do so. Avoid treating nominal human approval as meaningful oversight if the reviewer cannot understand the system’s role or change the outcome. The EU AI Act requires human oversight for high-risk systems within its scope, and UNESCO includes human oversight among its guiding principles. Regulation (EU) 2024/1689, AI Act text UNESCO Recommendation on the Ethics of Artificial Intelligence
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9. Procurement and third parties
Require teams to assess vendors, models, data, and other third-party components before adopting them. Clarify responsibilities between the organization and providers, identify available evidence and limitations, and make sure there is a route to raise incidents or request changes. NIST’s lifecycle framing includes third-party software, hardware, and data in relevant processes. NIST AI RMF Core
10. Testing, approval, and release
Define a risk-proportionate path to production: what testing is needed, what evidence must be retained, who approves release, and what conditions must be met before use. The policy should make clear that passing a test does not replace the risk assessment or ongoing monitoring. NIST and OECD describe lifecycle risk management, but the cited guidance does not establish one universal approval workflow. NIST AI RMF Core OECD AI Principles
11. Monitoring, incidents, and change management
Set monitoring responsibilities, review frequency, and the measures that matter for each system. Define how staff report incidents, how they are triaged and escalated, who authorizes corrective action, and when reassessment or suspension is required. Include triggers such as changes to the model, data, purpose, or operating environment. NIST’s AI RMF Core recommends planned ongoing monitoring and periodic review. Under the EU AI Act, providers of high-risk systems within scope must operate post-market monitoring systems, and deployers have monitoring and human-oversight duties applicable to their role. NIST AI RMF Core European Commission: AI Act Regulation (EU) 2024/1689, AI Act text
12. Training, exceptions, and enforcement
Require training suited to each person’s role, such as system owners, reviewers, operators, and procurement staff. Create a documented exception process that identifies an accountable approver and sets an expiry or review date. State how breaches are handled, including remediation or other consequences consistent with applicable employment, privacy, and other law.
How to use frameworks without confusing guidance with law
| Source | What it contributes | Legal status and scope |
|---|---|---|
| NIST AI RMF | A voluntary risk-management structure for integrating trustworthiness considerations into AI design, development, use, and evaluation. | Voluntary guidance; not itself a universal legal mandate. NIST AI RMF |
| EU AI Act | Risk-based legal requirements, including specific duties for relevant high-risk systems and the actors responsible for them. | Binding EU legislation where its provisions apply; duties depend on the system category, role, and scope. European Commission: AI Act |
| UNESCO Recommendation | Broad ethical principles including transparency, fairness, privacy, human oversight, risk assessment, and participation by diverse stakeholders. | Principles and recommendations; does not replace jurisdiction-specific legal analysis. UNESCO Recommendation |
| OECD AI Principles | Principles supporting ongoing lifecycle risk management that accounts for context and actors’ roles. | Principles and recommendations; does not replace jurisdiction-specific legal analysis. OECD AI Principles |
Use these materials for different purposes: operational risk structure, applicable legal duties, and ethical principles. Map the policy to laws that apply to the organization and each system rather than assuming that one framework covers every jurisdiction or use. The European Commission describes the AI Act as risk-based; its obligations depend on scope and on whether an organization is acting as a provider, deployer, or in another covered role. European Commission: AI Act
Quick Recap
Putting the policy into practice
- Record the use. Capture the system, intended purpose, owner, affected people, relevant data, and provider/deployer roles.
- Classify and assess risk. Review likely harms and applicable legal requirements before use, with additional scrutiny where consequences are significant.
- Set controls and approval conditions. Specify safeguards, human authority, testing evidence, and the person authorized to approve release or accept residual risk.
- Monitor and respond. Track relevant outcomes, provide reporting and escalation routes, and define when to correct, reassess, restrict, or suspend the system.
- Review the policy. Schedule periodic review and update it when law, technology, organizational responsibilities, or system uses change. NIST describes its AI RMF as “intended to be a living document.” NIST AI RMF FAQs
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