The NIST AI Risk Management Framework 1.0 gives organizations a structured way to manage AI risks, but translating its four functions — Govern, Map, Measure, and Manage — into action requires a concrete plan. This checklist breaks each function into steps you can execute, track, and evidence.
Govern — establish the foundation
Start by writing an AI risk management policy that defines scope, roles, and decision rights. Name an accountable executive and identify who can approve AI deployments, exceptions, and risk acceptance. Make sure the policy references your existing enterprise risk, security, and privacy programs so AI risk does not live in a silo.
Set a review cadence — at least annually, and ad hoc when regulations or use cases shift. Document everything: policy versions, approval chains, and the rationale for risk tolerance levels. Governance is what makes the rest of the framework auditable.
Map — understand the system and its context
Document the system's purpose, intended users, and expected operating environment. Catalog the data sources, training data, and any external content the model ingests. Identify stakeholders who will be affected by the system's outputs, including end users, downstream decision makers, and affected communities.
Map known risks and failure modes for this specific use case: what could go wrong with accuracy, security, fairness, privacy, or safety? Record assumptions and dependencies — third-party APIs, model providers, data pipelines — because each is an extension of your attack surface and a source of supply-chain risk.
Measure — test and evaluate risks
Define measurable risk metrics tied to the failure modes you mapped. For accuracy and robustness, run benchmark tests and adversarial evaluations against expected inputs and edge cases. For security, test for prompt injection, jailbreaks, training-data extraction, and unsafe tool use in the model's actual deployment configuration.
Evaluate bias, fairness, and explainability against the populations the system serves. Document the methodology, test data, results, and any gaps. The goal is not a pass-fail score — it is evidence that you understand the risks and can quantify them for decision makers.
Manage — treat and monitor risks
Prioritize risks by impact and likelihood, then develop a treatment plan for each significant finding. Some risks you will mitigate with controls: input validation, output filtering, human-in-the-loop reviews, or least-privilege tool permissions. Some you will transfer, accept, or avoid based on the business context.
Implement a continuous monitoring plan: scheduled re-testing, logging of model inputs and outputs, drift detection, and incident response procedures. Maintain a plan of action and milestones (POA&M) for open findings with owners and target dates. The framework is a cycle, not a project — revisit Map and Measure as the system and threat landscape evolve.
Build the evidence package
An effective AI RMF implementation is only as credible as the evidence behind it. Assemble an artifact set that includes the governance policy, system context documentation, measurement reports, risk treatment decisions, and the POA&M. Make sure every control and treatment maps back to a specific risk identified in the Map phase.
This package becomes the basis for internal reviews, customer assurances, and future regulatory conversations. It also makes the next cycle easier — you are not starting from scratch, you are updating a living risk record.
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