An AI risk assessment should examine how a system could fail or cause harm in its real-world use, then assign owners and safeguards to manage those risks over time. Cover the system’s purpose and affected people, reliability, privacy, security, fairness and harmful bias, and the governance needed to monitor and respond to problems. The right tests and priorities depend on the specific system and context—not on a one-size-fits-all scorecard.
What should an AI risk assessment cover?
Assess the AI system as part of the workflow in which it will be used, not as a model in isolation. The assessment should identify the intended use, who operates or relies on the system, who may be affected by its outputs, and what happens when those outputs are wrong.
NIST groups key trustworthiness characteristics as validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy; and fairness, with harmful bias managed. These characteristics are related, but they are not a universal checklist with equal weight in every situation. The consequences of an error, the people affected, and tradeoffs among characteristics shape what matters most. See the NIST AI Risk Management Framework and its FAQ.
Start with the system’s use and impact
Before assigning risk ratings, describe what the system does and the decision or process it influences. Include the operating environment, inputs, data sources, users, affected people, and any human review. Consider intended use as well as foreseeable misuse. A tool used to draft a low-stakes internal summary has a different risk profile from one whose output can affect access to credit, employment, insurance, or another consequential service.
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Record what a harmful failure would look like, who could bear its costs, and how they could challenge or correct an outcome. This context makes later performance, privacy, security, and fairness tests meaningful.
Evaluate reliability and validity
Reliability asks whether the system performs consistently under expected conditions; validity asks whether it is fit for the purpose and context in which it is being used. Accuracy is only one part of that picture. A system may perform well on a test set yet fail on new inputs, under changing conditions, or for a population that was poorly represented during development.
- Test performance on inputs and scenarios representative of actual use, including meaningful variation and edge cases.
- Examine whether results generalize beyond development data and whether errors could cause material harm.
- Define what counts as failure, who reviews failures, and when use must be paused or escalated.
- Set out monitoring and human intervention appropriate to the consequences of an incorrect output.
A passing test at launch does not establish continuing reliability. Monitoring should detect changes in data, users, operating conditions, or outcomes that undermine the original evaluation.
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Assess privacy and data handling
Review personal and sensitive information throughout the system’s lifecycle. Identify what enters the system, where it comes from, who can access it, how long it is retained, and whether outputs could disclose or enable inference about an individual or private fact. NIST notes that AI can create privacy risks by making it possible to identify people or infer information that was previously private; see its trustworthiness guidance.
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- Consider whether prompts, training data, or outputs expose personal information or allow sensitive inferences.
- Evaluate data minimization and privacy-enhancing techniques where they fit the use case.
- Record tradeoffs and evidence: under some conditions, including sparse-data settings, privacy-enhancing methods can reduce accuracy and affect fairness or other values.
Privacy protections should be assessed alongside their effects on system performance and affected groups rather than treated as cost-free or interchangeable.
Assess security and resilience
Consider confidentiality, integrity, and availability risks involving the AI system and its data, as well as the software and hardware it depends on. Examine the deployment’s attack surface, dependencies, access controls, and ability to recover from an incident. Some AI security risks overlap with conventional software and cybersecurity risks, so include the surrounding systems and operating practices in scope. NIST’s AI Resource Center provides technical resources for testing, evaluation, verification, and validation.
For generative AI or foundation-model deployments, consider risks that generation introduces or makes more severe. NIST’s Generative AI Profile (NIST AI 600-1), released July 26, 2024, is a cross-sectoral companion to AI RMF 1.0 with suggested actions for managing such risks. Use it where relevant; its risk areas do not automatically apply to every AI system.
Check fairness and harmful bias
Examine whether access, errors, or outcomes differ across affected groups and contexts, and whether those differences could cause harm. Look for groups missing or misrepresented in data, design choices that shape outcomes, and differences in the consequences of mistakes. Ask what recourse a person has when an output is wrong.
- Choose evaluation populations and measures that match the system’s use and affected people.
- Investigate observed disparities and explain why the selected thresholds or metrics are appropriate.
- Specify how human review, correction, or challenge works for affected individuals.
A single fairness metric cannot settle the question without explaining the population, threshold, and consequences involved. NIST treats fairness with harmful bias managed as a trustworthiness characteristic and notes that trustworthiness goals can involve tradeoffs.
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Make accountability, transparency, and explanation operational
Name the people or teams accountable for approval, operation, monitoring, and incident response. Keep records of intended use, known limitations, evaluation evidence, system changes, and decisions to accept residual risk. Give deployers and affected users information suited to their roles so they can use, oversee, or contest outputs appropriately.
Transparency and explainability can support oversight, but they do not prove that a system is accurate, fair, private, or secure. NIST lists accountability and transparency separately from explainability and interpretability; each needs its own evidence and controls.
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NIST’s voluntary AI Risk Management Framework 1.0 organizes work into four functions: Govern, Map, Measure, and Manage. The AI RMF Playbook offers suggested actions and documentation practices that organizations can tailor; the framework is guidance, not a universal legal requirement.
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- Govern: Set policies, roles, accountability, and escalation paths for the system.
- Map: Describe the system, its intended use and operating context, stakeholders, affected people, and potential impacts.
- Measure: Evaluate risks with evidence appropriate to the use, including performance, privacy, security, and differential impacts.
- Manage: Prioritize findings, select mitigations, assign owners, document residual risks, and monitor their status.
Reassess when the model, data, users, environment, or intended use changes. NIST released AI RMF 1.0 on January 26, 2023. As of October 4, 2026, NIST’s framework page says the framework is being revised; check the page for current status.
Compare systems on the same assessment axes
If comparing two or more AI systems, use the same use context and axes for each. These comparison categories are a practical synthesis, not a NIST-mandated scoring rubric; tailor measures and thresholds to the system and its risk tolerance.
Quick Recap
| Assessment axis | What to compare |
|---|---|
| Intended use and affected people | Purpose, users, impacted groups, and consequences of error |
| Performance and reliability | Validity for the task, accuracy, robustness, monitoring, and recovery |
| Privacy and data handling | Data collected, 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 | Subgroup evidence, harmful disparities, human review, and routes to challenge outcomes |
| Governance and evidence | Accountable owners, documentation, test methods, residual risks, and change management |
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