If no one can clearly approve, constrain, pause, or retire an AI system, treat that as a governance gap—not as evidence that the risk belongs to nobody. Start by documenting the system and its uses, then assign an accountable decision-maker who has authority, resources, and a defined route for review and escalation. NIST’s AI Risk Management Framework (AI RMF) puts responsibility for AI risk decisions with executive leadership and calls for clear roles and communication lines.
1. Find the system and define its context
Begin with an inventory entry for the AI system. Record what it does, where and how it is used, who operates it, and which people or groups may be affected. Include enough information to distinguish the system from a vendor, model, or individual feature if those are only parts of a larger workflow.
This context matters because the same technology can create different risks in different uses. NIST’s AI RMF calls for mechanisms to inventory AI systems and prioritize resources according to organizational risk. Its Core treats governance as an ongoing function across an AI system’s lifespan.
2. Assign decision authority—not just a contact person
Name a person or role that can make and own decisions about the system’s risk. That authority should include the ability to approve use, set conditions, restrict deployment, pause operation, or retire the system, as appropriate. NIST states that executive leadership takes responsibility for decisions about risks associated with AI system development and deployment.
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That does not mean one executive must perform every technical review or make every operational choice. Identify supporting roles based on the system and organization, such as technical evaluation, operations, security, legal or compliance, and the business function using the system. Make clear how their advice reaches the accountable decision-maker and how concerns can be escalated.
A title by itself is not meaningful accountability. The assigned decision-maker needs suitable authority, access to relevant information, training, and resources. NIST’s Govern outcomes emphasize clear documented roles and communication lines, as well as responsible, empowered, and trained teams.
3. Make the assignment operational
Document who does what, when decisions are required, and what information must be available before those decisions are made. A practical responsibility record can include:
- The accountable decision-maker and the scope of their authority.
- Supporting owners for technical evaluation, operations, security, legal or compliance review, and affected business functions, as relevant.
- Escalation contacts and the route for unresolved concerns, including concerns raised by people affected by the system.
- The evidence needed for a decision, such as risk assessments, evaluation results, known limitations, and monitoring information.
- The review cadence, triggers for an out-of-cycle review, and the date of the next review.
Choose a reporting structure that fits the organization; the cited frameworks do not prescribe one universal org chart. Check whether authority is clear, the responsible person can obtain resources and escalate issues, relevant expertise and affected perspectives can be heard, and oversight continues as the system changes.
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Ownership should persist beyond initial approval. Revisit the risk assessment when the model, data source, system integration, intended use, or operating context changes. Also plan ongoing monitoring and periodic review so that problems arising in use can reach someone empowered to respond.
The OECD’s accountability work describes risk management and due diligence throughout the AI system lifecycle; its accountability guidance also says responsibility should reflect each actor’s role, context, and ability to act, with cooperation among relevant actors where appropriate. NIST likewise describes governance as a continual requirement rather than a one-time sign-off.
For practical actions across the AI RMF functions—Govern, Map, Measure, and Manage—NIST provides a Playbook. Use it to structure work, not as a substitute for deciding who has authority in your organization.
5. Record decisions and act when controls fail
Keep a decision record that identifies the assessed risks, the decision and its rationale, any conditions on use, the accountable owner, the review date, and how escalated concerns were resolved. NIST identifies documentation as a way to support transparency, human review, and accountability.
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If monitoring shows that controls are no longer effective, route the issue to the decision-maker and revise the controls, limit or pause use, or withdraw the system as appropriate. Include safe decommissioning and phase-out in the governance plan rather than leaving retirement responsibilities undefined.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the frameworks do—and do not—establish
The NIST AI RMF is intended for voluntary use. It offers a structure for organizing risk work; adopting it or naming an owner does not, by itself, establish that an organization has met legal obligations. Those obligations depend on the jurisdiction, sector, system, and use. The OECD Recommendation on Artificial Intelligence and its accountability work provide additional guidance, but neither settles the legal requirements for an unspecified organization or deployment.
For scope and status, see NIST’s AI Risk Management Framework overview, OECD’s Advancing accountability in AI (published 23 February 2023), and the OECD Recommendation on Artificial Intelligence.
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