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An AI risk assessment should examine how a system could fail or cause harm in its real deployment, then document how those risks will be reduced, monitored, and revisited. Cover reliability and validity, safety, security and resilience, privacy, fairness and harmful bias, transparency, explainability, and accountability. The right priorities depend on the system’s purpose, operating conditions, and the people affected—not on a one-size-fits-all scorecard.
Start with the AI system’s actual use
Assess the complete socio-technical system in a specific deployment, not just the model in isolation. The same model can create different risks when used by different people, with different data, or to influence different decisions. NIST’s AI Risk Management Framework (AI RMF) frames trustworthy AI around context and interrelated characteristics rather than a universal checklist with fixed weights.
Describe the system and the workflow it affects before scoring risks. Include:
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- The intended purpose, users, and decisions or processes the system influences.
- The people or communities affected, including people who do not directly use the system.
- Inputs and data sources, operating conditions, and important dependencies.
- Foreseeable misuse, unexpected inputs, and ways people may rely on or override outputs.
- What a harmful failure would look like and who would bear its consequences.
This scope gives each later test a meaningful context: an accuracy result matters only in relation to the task, population, and consequences of error.
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Use a lifecycle workflow to turn findings into action
NIST’s voluntary AI RMF 1.0 organizes risk work into four functions: Govern, Map, Measure, and Manage. Its AI RMF Playbook offers suggested actions and documentation practices that organizations can tailor; it is guidance, not a universal legal requirement.
- Govern: Set policies, assign accountable owners, define approval and escalation routes, and establish how risk decisions are recorded.
- Map: Document the system, use context, affected people, expected benefits, foreseeable harms, and relevant constraints.
- Measure: Evaluate risks using evidence appropriate to the use, such as performance tests, privacy and security reviews, and analysis of differential impacts.
- Manage: Prioritize findings, choose mitigations, assign owners and deadlines, record residual risks, and monitor whether controls work in deployment.
For each material risk, record its cause, who may be affected, potential consequences, supporting evidence, mitigation, responsible owner, escalation path, and remaining risk after mitigation. Reassess when the model, data, users, operating environment, or intended use changes. Monitoring after launch matters because a one-time evaluation cannot establish that performance and risk remain acceptable over time.
Assess reliability, validity, and safety
Determine whether the system is fit for its intended task and behaves acceptably under expected conditions. Reliability and validity are related but not identical: validity concerns whether the approach is suitable for the intended purpose, while reliability concerns consistency of performance. Accuracy is one useful measure, but it does not by itself establish that a system is robust, safe, or appropriate for a deployment.
- Test with inputs and populations representative of the intended setting, not only development data.
- Examine errors, edge cases, and sensitivity to meaningful changes in inputs or operating conditions.
- Identify which failures could cause material harm, how likely they are to be detected, and what happens next.
- Set deployment monitoring, escalation, fallback, and human-intervention procedures where needed.
Specify the evidence behind a performance claim and the conditions under which it applies. A system that performs well in a controlled evaluation may behave differently after deployment or when the population, inputs, or workflow changes.
Review privacy and data handling
Trace personal and sensitive information through the system: what enters it, where the data comes from, how it is used, who can access it, how long it is retained, and what outputs disclose. Consider whether the system can identify a person or infer private facts from information that might otherwise appear non-sensitive. NIST’s trustworthiness material describes identification and inference as potential AI privacy risks, and points to values such as anonymity, confidentiality, and individual control as relevant design considerations.
Evaluate whether data minimization or privacy-enhancing techniques fit the use, and document their effects rather than assuming they are cost-free. NIST notes that, in some conditions such as data sparsity, privacy-enhancing methods can reduce accuracy, with possible implications for fairness and other values. Explain the observed tradeoff and its evidence for this system.
Assess security and resilience
Consider confidentiality, integrity, and availability risks for the system, its data, and supporting software and hardware. Review the deployment’s attack surface, dependencies, access controls, and recovery behavior, including how an incident could affect outputs or interrupt the service. Some AI security concerns overlap with ordinary software and cybersecurity risks; the assessment should reflect the actual architecture and use context. NIST’s AI Resource Center provides technical resources for AI testing and evaluation as well as other implementation guidance.
For a generative AI system, consider risks that arise from or are made worse by generation and foundation-model use. NIST AI 600-1, the Generative AI Profile, was released on July 26, 2024, as a cross-sectoral companion to AI RMF 1.0, with suggested actions for managing relevant generative AI risks. Use it as a supplement where applicable; not every risk in a profile will apply to every system.
Check fairness and harmful bias
Look for differences in errors, access, or outcomes across the groups and contexts affected by the system. Ask which populations may be missing or misrepresented in data or evaluation, whether design choices could create harmful disparities, and how an affected person can seek review when an output is wrong.
- Define which groups and outcomes are relevant to the deployment and why.
- Use evidence suited to those groups and conditions; report where data is insufficient to draw a reliable conclusion.
- Explain the metric, population, threshold, and consequences behind any disparity finding.
- Provide appropriate human review or a route to challenge consequential outputs.
Do not treat one fairness metric as a complete answer. NIST includes fairness with harmful bias managed among trustworthiness characteristics, while recognizing that characteristics can involve tradeoffs. State what the chosen evaluation captures and what it cannot establish.
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Name the people or teams accountable for the system and its risk decisions. Keep records of intended use, limitations, evaluation evidence, changes, approvals, mitigations, and decisions to accept residual risk. Provide information appropriate to the audience: operators need enough to use and supervise the system, while affected people may need understandable notice and a practical way to raise concerns.
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Compare systems on the same axes
When evaluating alternatives, use the same deployment context and comparison dimensions for each. This is a practical comparison structure, not a NIST-mandated scoring rubric; tailor measures and thresholds to the system and the organization’s risk tolerance.
| Assessment axis | What to compare |
|---|---|
| Intended use and affected people | Purpose, users, impacted groups, operating conditions, and consequences of error. |
| Performance and reliability | Fitness for the task, accuracy, robustness, monitoring, escalation, and recovery. |
| Privacy and data handling | Data collected and used, retention and access, inference or disclosure risks, and privacy controls. |
| Security and resilience | Threats, confidentiality, integrity and availability, dependencies, incident response, and recovery. |
| Fairness and recourse | Evidence across relevant groups, harmful disparities, human review, and ways to challenge outcomes. |
| Governance and evidence | Accountable owners, documentation, test methods, residual risks, and change management. |
Check which NIST guidance applies
NIST released AI RMF 1.0 as a voluntary framework on January 26, 2023. It released the Generative AI Profile on July 26, 2024. As of October 4, 2026, NIST’s AI RMF page says version 1.0 is being revised; check the current framework page for status before relying on a version-specific detail. The framework can organize an assessment, but organizations still need to determine which legal, regulatory, contractual, and sector-specific obligations apply to their own deployment.
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