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Assess an AI system in the setting where it will actually be used—not just by checking model accuracy before launch. Define its purpose and users, identify who could benefit or be harmed, test the risks that matter for that use, assign people authority to act, and plan safeguards and monitoring. NIST’s voluntary AI Risk Management Framework (AI RMF) organizes this work into four connected functions: Govern, Map, Measure, and Manage.
What an AI risk assessment should establish
A predeployment assessment should give decision-makers enough evidence to decide whether to release a system as proposed, release it with restrictions, require more work, or not deploy it. It should also explain what the system is allowed to do, who is responsible for its operation, and what will happen when it fails or causes harm.
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The NIST AI RMF is a flexible, use-case-agnostic guide, not a safety certification, a guarantee of good outcomes, or proof of legal compliance. NIST describes it as voluntary. Applicable legal, sector-specific, contractual, and organizational requirements must be considered separately for the particular deployment.
How the four NIST functions fit together
| Function | What the team does | Predeployment question |
|---|---|---|
| Govern | Establishes policies, accountability, roles, and oversight. | Who owns the system and who can approve, restrict, or stop it? |
| Map | Documents context, intended use, affected parties, and potential impacts. | What could happen in this real operating environment, and to whom? |
| Measure | Evaluates risks and trustworthiness with appropriate evidence. | What tests or reviews would reveal the failures that matter here? |
| Manage | Prioritizes and responds to risks, including through ongoing monitoring. | Which risks can be reduced, accepted, restricted, or require the system not to launch? |
These functions complement one another; they are not a one-time sequence that ends at launch. Risk management applies across the AI lifecycle, including development, deployment, use, and testing and evaluation. Teams can revisit earlier decisions when the system, its users, or its operating conditions change.
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A practical predeployment workflow
1. Define the system, its purpose, and its boundaries
Write down what the system is intended to do and what it is not intended to do. Identify its users, the environment in which it will operate, the model or service components included in the assessment, and how its outputs may influence decisions or actions. Describe the human roles in the process, including whether a person reviews outputs and what authority that person has.
Make likely failure meaningful in context. For example, a wrong answer in an internal brainstorming tool is not necessarily equivalent to a wrong answer that shapes access to a service. Record what a consequential error, misuse, outage, or unexpected output would look like. A written scope is a practical way to apply NIST’s context-first approach; it is not a universal NIST-mandated form.
2. Identify affected people, benefits, and plausible harms
Map more than the people who operate or directly use the system. Include people who rely on its output, people whose data it processes, and people who may face consequences without knowing an AI system was involved. Consider plausible benefits as well as potential harms, and ask whether they may be distributed differently across groups.
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3. Choose the trustworthiness concerns that matter here
NIST identifies several characteristics to consider. Not every characteristic will have equal importance for every system, so connect each one to the intended use, affected parties, and plausible failure modes.
| Trustworthiness concern | Questions to ask |
|---|---|
| Validity and reliability | Does the system perform as intended in the conditions where it will be used, and how consistent is its performance? |
| Safety | Could outputs or system actions create harm, and are there controls to prevent or limit it? |
| Security and resilience | Can the system withstand foreseeable attacks, misuse, or disruption, and recover safely? |
| Accountability and transparency | Can responsible people explain who made relevant decisions and what information users need about the system? |
| Explainability and interpretability | Can people who need to review or act on outputs understand them well enough for their role? |
| Privacy enhancement | Is personal information handled and protected appropriately for the system’s purpose? |
| Fairness and harmful bias | Could performance or impacts differ unfairly across people or groups, and how will the team detect and address that? |
4. Assign owners, reviewers, and decision authority
Name the person or team accountable for the system and the people responsible for reviewing its risks. Specify who can approve deployment, impose restrictions, escalate an issue, or pause the system. Set approval criteria before reviewing test results, including what evidence is required and which unresolved risks are unacceptable for the intended use.
These roles and thresholds depend on the organization and use case. The useful test is whether responsibility is concrete: if a serious issue appears, people should know who must respond and who has authority to change or stop the deployment.
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5. Evaluate with evidence matched to the risks
Select tests and reviews based on the system’s purpose and the harms identified—not on the convenience of a generic benchmark. Depending on the application, an assessment may include representative performance checks, subgroup analysis, robustness and security testing, privacy review, human-factors evaluation, or checks of how the system handles failures.
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Record what was tested, the data and conditions used, observed failures, limitations, and remaining risks. No single test suite or universal threshold is established as sufficient for every AI system. A result from a narrow test should not be treated as evidence of reliable performance in settings, populations, or conditions it did not cover.
6. Select safeguards and connect each to a risk
Choose controls that reduce the specific risks identified and measured. Depending on the application, options may include human review for consequential decisions, limits on access or permitted uses, clear user disclosures, output validation, data minimization, security controls, appeal or correction routes, fallback procedures, and a safe way to stop the system.
For each safeguard, record the risk it addresses, its owner, how it will be used, and how the team will check whether it works. A human-review step, for example, is only meaningful if the reviewer has the information, time, training, and authority needed to challenge an output rather than simply pass it through.
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7. Plan monitoring, incident response, and reassessment
Define which signals will be monitored after release, who will review them, and how users or affected people can report a problem. Set out how incidents will be triaged, who is notified, and what conditions trigger restriction, rollback, or a fresh assessment. Reassessment triggers may include a change to the model, data, intended use, user population, connected systems, or operating environment.
Deployment approval is a decision based on evidence available at that point, not proof that future behavior cannot change. Continue to consider relevant trustworthiness concerns during deployment and use, and update controls when operational evidence changes the risk picture.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Additional guidance for generative AI
For systems that generate text, images, audio, video, or other synthetic content, NIST’s Generative AI Profile is a companion to AI RMF 1.0. Published July 26, 2024, it describes risks that are novel to or exacerbated by generative AI and suggests actions for managing them. Use it alongside the general framework when those systems are in scope; it does not remove the need to assess the particular application, users, and impacts.
How to decide whether the evidence is enough to deploy
There is no NIST-set risk score or universal pass mark for all AI deployments. A defensible decision ties the proposed use to the evidence and controls: the tests should address the important risks, known limitations should be understood, and the people accountable for the system should be able to explain why remaining risks are acceptable for that context.
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- Proceed with restrictions when risks can be contained by limiting users, settings, decisions, or system capabilities, and those limits can be enforced and monitored.
- Delay deployment when material evidence is missing, failures are not understood, or planned safeguards have not been shown to work well enough for the intended use.
- Do not deploy when the remaining risk is unacceptable or cannot be controlled in the proposed context.
Keep the rationale with the assessment so that later reviewers can see what use was approved, under what conditions, based on which evidence, and by whom.
NIST framework status and scope
NIST released AI RMF 1.0 on January 26, 2023, and describes it as voluntary and non-sector-specific. NIST’s framework page says AI RMF 1.0 is being revised; because that status can change, check NIST’s current framework materials before adopting a version or citing its status. The framework helps organize risk management, but it does not establish the legal requirements for a specific deployment or replace review of local law and sector rules.
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