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Build an AI governance policy by defining which AI uses it covers, assigning decision-makers, requiring teams to document each use, and setting risk-based review, approval, monitoring, and escalation rules. The NIST AI Risk Management Framework (AI RMF) offers a voluntary structure—Govern, Map, Measure, and Manage—but it is not a law or proof of legal compliance. The right policy depends on where your organization operates, its sector, and how it uses AI.
Start with a framework, not a one-size-fits-all checklist
NIST’s AI RMF is designed to help organizations manage risks that AI may pose to people, organizations, society, and the environment. Its four functions—Govern, Map, Measure, and Manage—organize the work. Governance is cross-cutting: ownership, decision rights, and accountability need to shape the other functions and continue throughout an AI system’s lifecycle.
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The framework is voluntary, not a legal requirement. NIST released AI RMF 1.0 on January 26, 2023 and says it is being revised; check NIST’s AI RMF page for current status. Its Playbook offers suggested actions and documentation practices, not a universal compliance checklist. Use these resources to structure your policy, then adapt its requirements to your organization’s circumstances.
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Build the policy in seven steps
1. Define what is covered
State which AI systems and activities fall under the policy: internally developed models, third-party services, embedded AI features, and business uses. Include acquisition and supplier dependencies, not only systems built by your technical teams. Explain any exclusions and who can approve them, and set a process to revisit scope as tools and uses change.
2. Assign owners and decision rights
Name an executive sponsor and a policy owner. Identify who owns each AI system or use, who reviews it, who can approve deployment or continued use, and where concerns are escalated. Involve legal, privacy, security, risk, procurement, technical, and affected business expertise as appropriate to the organization. Make clear who may accept residual risk and under what conditions.
3. Require teams to map each use
Before a system is approved, require its owner to record enough context to determine what review it needs. That record should cover:
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- The intended purpose, users, affected people, and deployment setting.
- Data used or produced, system components, and suppliers or service providers.
- Foreseeable changes in use, unexpected outputs, and plausible misuse.
This is the practical foundation for deciding whether a use needs routine review, specialist assessment, tighter controls, or should not proceed.
4. Set risk-based assessment and evidence requirements
Specify what teams must assess and what evidence they must keep, with depth proportionate to the use’s risks and impacts. NIST’s trustworthiness characteristics include validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy; and fairness, including management of harmful bias. Which characteristics matter most—and how to evaluate them—depends on the system and context. A generic checklist alone does not establish that a system is trustworthy.
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5. Establish review gates and responses
Define when review is required, who conducts it, and what conditions must be met before deployment or continued use. Set safeguards appropriate to the risk, a path for documenting and accepting residual risk, and criteria for escalation. Specify when an incident, new risk, or unmet condition requires a system to be paused, remediated, or retired. Calibrate review depth to organizational risk tolerance and applicable legal or regulatory obligations.
6. Monitor systems and reassess when they change
Governance should cover pre-design, design and development, deployment, use, and testing and evaluation—not stop at launch. Establish post-deployment monitoring, a way to handle incidents and complaints, and triggers for reassessment, such as a material change to the system, its data, supplier, purpose, or operating context. Set change-control and periodic policy-review processes so rules keep pace with systems and organizational needs.
7. Add specific rules for generative AI
Where generative AI is relevant, state which uses are permitted and what review they need. Address external services, sensitive data, verification of generated content, and other risks tied to your applications. NIST published its Generative AI Profile on July 26, 2024 as a cross-sector companion to AI RMF 1.0. It can inform generative-AI risk guidance, but does not settle every sector-specific or legal question.
Make the policy usable in day-to-day decisions
Translate policy principles into operational instructions: what information a system owner submits, which roles review it, what approvals are needed, how decisions and safeguards are recorded, and how a concern is escalated. The NIST AI RMF Core describes governance as continual and intrinsic to effective AI risk management throughout the AI system lifecycle and organizational hierarchy. In practice, that means assigning recurring responsibilities—not just publishing a document—and linking decisions to ongoing monitoring and reassessment.
Check legal obligations before finalizing
No single legal-control set can be prescribed without knowing the organization’s jurisdictions, sector, and AI use cases. Ask relevant legal or compliance staff to map current obligations for those circumstances before finalizing policy language. Treat the AI RMF as a risk-management aid, not a substitute for that review or a guarantee of compliance.
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