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An AI risk assessment should examine how a system could fail or cause harm in its actual setting, then assign people to manage those risks throughout its lifecycle. Cover the system’s intended use, performance and reliability, privacy, security, fairness and harmful bias, transparency, explainability, and accountability. The right tests and priorities depend on the system, the people affected, and the consequences of error—not on a one-size-fits-all scorecard.
Start with the system in its real context
Assess the complete socio-technical system in a specific deployment, not just the model. A model’s risks can change with its users, input data, supporting software, workflow, and the decisions people make from its outputs.
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First record what the system is meant to do, who will use it, who could be affected, and what decision or process it influences. Include foreseeable misuse and operating conditions. Define what a harmful failure would look like: for example, an incorrect recommendation, an inaccessible service, a privacy disclosure, or an outcome that is difficult to challenge.
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What to assess
Reliability and validity
Determine whether the system is fit for its intended task and continues to perform as expected. Evaluate accuracy, robustness to relevant variation, and generalization beyond development data. Test inputs and conditions that reflect actual use, and identify which errors could cause material harm.
Set out how performance will be monitored after deployment, how a failure will be detected, and when a person should intervene or escalate the issue. A successful one-time test does not establish that performance will remain reliable as data, users, or conditions change.
Privacy and data handling
Trace information through the system: what personal or sensitive data is collected, where it comes from, who can access it, how it is used and retained, and whether inputs or outputs could disclose or enable inference about an individual. Consider risks of identifying people or inferring private facts, including from information that was not initially treated as sensitive.
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Review data minimization and privacy-enhancing techniques where appropriate. These controls are not automatically cost-free: under some conditions, such as sparse data, privacy methods can reduce accuracy and affect fairness or other values. Record the evidence and tradeoffs for the specific system rather than assuming a control has no side effects. NIST’s trustworthiness guidance discusses privacy values including anonymity, confidentiality, and control.
Security and resilience
Assess threats to the confidentiality, integrity, and availability of the system and its data. Include training and output data, access controls, supporting software and hardware, external dependencies, and the system’s ability to recover from disruption. Consider the attack surface created by the particular deployment, not only the model itself; some AI security concerns overlap with conventional software and cybersecurity risks.
For a generative AI or foundation-model system, also consider risks that generation creates or makes worse. NIST’s Generative AI Profile, NIST AI 600-1, is a cross-sector companion to AI RMF 1.0 with suggested actions for those risks. Treat it as a supplement and determine which risks apply to the system under review.
Fairness and harmful bias
Check whether errors, access, or outcomes differ across affected groups and contexts. Identify groups that may be missing or misrepresented in the data, and examine how design and deployment choices could create or amplify harmful disparities. Evaluate the groups and outcomes that matter for the use case, and explain the metric, population, threshold, and consequences behind any disparity analysis; a single fairness score cannot settle every question.
Also assess what happens when an output is wrong: whether a person can obtain human review, challenge a decision, or seek a remedy. NIST treats fairness with harmful bias managed as a trustworthiness characteristic, while recognizing that trustworthiness dimensions can involve tradeoffs.
Accountability, transparency, and explainability
Name the people or teams accountable for decisions about the system, its operation, and residual risk. Keep records of intended use, limitations, evaluation evidence, changes, and approvals. Provide information that is useful to deployers and affected people in their respective roles.
Transparency and explainability help people oversee a system, but neither proves that it is accurate, fair, private, or secure. Treat them as distinct assessment dimensions, alongside accountability, rather than substitutes for testing and risk controls.
Use Govern, Map, Measure, and Manage to make the assessment actionable
Govern: assign responsibility
Establish policies, roles, decision rights, and escalation routes. Decide who can approve deployment, accept residual risk, pause use, and authorize changes. Keep accountability clear across the organization and any relevant system providers or operators.
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Document the system’s purpose, users, affected people, data, operating conditions, dependencies, and foreseeable misuse. Describe the workflow it influences and the potential harms of failure. This context determines which risks deserve attention and what evidence will be meaningful.
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Measure: gather evidence
Choose evaluations suited to the identified risks: performance tests, subgroup analyses, privacy and security reviews, and assessments of human oversight or recovery. Record methods, assumptions, limitations, results, and unresolved uncertainties. NIST’s AI Resource Center provides technical resources for testing, evaluation, verification, and validation.
Manage: decide, mitigate, and monitor
Prioritize risks, choose mitigations, assign owners and deadlines, and document which risks remain. Set monitoring and escalation procedures for operation, with clear conditions for reassessment or pausing use. NIST’s AI RMF Playbook offers suggested actions and documentation practices that organizations can tailor.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare systems on the same axes
If you are comparing systems for the same use, apply the same questions and evidence standards to each. These axes are a practical synthesis for comparison, not a NIST-mandated scoring scheme; tailor thresholds to context and risk tolerance.
| Assessment axis | Questions to compare |
|---|---|
| Intended use and affected people | What is the purpose, who uses or is affected by the system, and what are the consequences of error? |
| Performance and reliability | Is the system valid for the task? How does it handle relevant variation, and what monitoring or recovery is in place? |
| Privacy and data handling | What data is collected and retained, who can access it, what inference or disclosure risks exist, and what controls are used? |
| Security and resilience | What threats, dependencies, and confidentiality, integrity, or availability risks apply? How are incidents handled and service recovered? |
| Fairness and recourse | What evidence exists about disparities, and can people obtain review or challenge an outcome? |
| Governance and evidence | Who is accountable? What test methods, documentation, residual risks, and change controls support the decision? |
Repeat the assessment when the system changes
Risk management continues after launch. Reassess when the model, data, users, environment, supporting components, or intended use changes, and when monitoring reveals new failures or impacts. Keep the assessment tied to current evidence and operating conditions rather than treating approval as permanent.
NIST released AI RMF 1.0 on January 26, 2023, and its Generative AI Profile on July 26, 2024. As of October 4, 2026, NIST’s AI RMF page says the framework is being revised; check that page for the latest status before relying on a particular version.
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