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1. What AI systems and uses are in scope?
Define which systems, models, tools, and activities the policy covers. Include the contexts in which the organization develops, buys, deploys, or uses AI, and explain how employees identify an AI use and determine which review process applies.
NIST’s AI Risk Management Framework (AI RMF) is voluntary guidance for organizations that design, develop, deploy, or use AI systems. Its scope is not limited to organizations building their own models. NIST AI Risk Management Framework
2. Who is accountable, and what does each role do?
Name the executive sponsor and the people with authority to approve, restrict, or stop an AI use. Assign responsibility across the lifecycle so that accountability does not end with the technical team.
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- Who conducts or reviews risk assessments and decides whether residual risk is acceptable?
- Who is responsible for development or procurement, deployment, day-to-day operation, monitoring, and incident response?
- Who provides independent review, and which legal, privacy, security, compliance, or other functions must contribute?
- Which people oversee the system, and which people use or interact with it? What proficiency or training does each role require?
NIST’s AI RMF Playbook recommends policies that distinguish human roles and responsibilities, provide for relevant training and proficiency, and track risks associated with human-AI configurations. NIST AI RMF Playbook
3. Which laws, regulations, and standards apply?
Require someone to identify, document, and periodically revisit the legal and regulatory requirements that apply to each use. The policy should say who owns that assessment and how legal duties become operational controls, approvals, and records.
Applicability depends on jurisdiction, sector, organizational role, system classification, and actual use; a general policy cannot determine legal obligations for every deployment. For organizations and systems within its scope, the EU AI Act establishes a risk-based legal framework that includes prohibited practices, requirements for high-risk systems, and oversight arrangements. Its application requires case-specific analysis. EU AI Act (Regulation (EU) 2024/1689)
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NIST’s Govern function also calls for legal and regulatory requirements to be understood, managed, and documented. The AI RMF itself is voluntary guidance, not a substitute for applicable law. NIST AI RMF Playbook
4. How are AI uses classified, and who can accept the risk?
Set an intake and assessment process that determines the context and risk of a proposed use. Define risk tiers, criteria for escalation, who can approve each tier, and who may accept residual risk. Specify how much review is required at each level, based on the organization’s risk tolerance and the system’s context.
Risk assessment should account for multiple trustworthiness characteristics rather than reducing the decision to a single score. NIST identifies validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy; and fairness, with harmful bias managed. These are factors to consider in practice, not a guarantee that a system is trustworthy. NIST AI Risk Management Framework
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5. What controls apply across the AI lifecycle?
Specify what reviews and evidence are required when the organization selects or designs a system, develops or configures it, tests it, deploys it, uses it, and monitors it. Assign owners for each control and say what must be completed before a use can proceed.
Define what changes trigger reassessment. Examples include a material change to the model, data, purpose, users, or operating environment. NIST treats governance as ongoing and recommends considering trustworthiness from pre-design through testing and evaluation, rather than only at launch. NIST AI RMF Playbook
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State the conditions under which a person must review an output, intervene, override a result, or escalate a concern. Identify trained people who can carry out those tasks, the information they need, and the authority they have to act.
Document how the human and AI work together and how oversight outcomes or concerns are tracked. A person’s nominal presence is not enough: the policy should define the person’s responsibilities and what meaningful intervention looks like for that use.
7. What must be documented or disclosed?
Set minimum records for each system and use. At a minimum, consider documenting:
- The system’s purpose, owner, and approved use.
- Risk assessments, decisions, accepted residual risks, and required controls.
- Testing and evaluation, human-oversight arrangements, and material changes.
- Incidents, escalations, and actions taken.
Specify who can access these records and what information should be communicated to users or affected parties. NIST notes that documentation can support transparency, human review, and accountability, and recommends policies that enhance explanation and interpretation. Its guidance does not prescribe one universal documentation format. NIST AI RMF Playbook
8. How are incidents and exceptions handled?
Define how people report problems, how severity is assessed, and who decides on containment, escalation, or a pause or withdrawal of the AI use. Set expectations for follow-up review and records so that lessons from an incident can inform risk assessments and controls.
Also explain how exceptions to policy are requested, approved, time-limited, and recorded. Tailor reporting thresholds and response authority to the organization’s risks and legal obligations; a general framework does not set universal operational thresholds.
9. When and why is the policy reviewed?
Assign a policy owner and specify review triggers. These may include a material system change, an incident, a newly identified legal obligation, or a change in the organization’s risk tolerance. Set a review cadence that fits the organization; NIST describes governance as continual but does not prescribe a universal calendar schedule. NIST AI Risk Management Framework
How to make the policy actionable
For each answer, identify the responsible role, the decision or control required, the evidence to retain, and the route for escalation. A concise policy can establish the rules, while supporting procedures define how teams carry them out. This structure helps the organization review decisions and demonstrate accountability across both the organizational hierarchy and the AI lifecycle.
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