You can evaluate AI risk by examining a specific system, what it is used for, the people and decisions it affects, and what evidence shows about its behavior. That practical assessment does not require assumptions about superintelligence: it focuses on foreseeable harms from systems being developed or deployed today, and changes as the system and its context change.
What does AI risk assessment actually assess?
“AI” is not one uniform risk category. A system’s risks depend on its capabilities, intended task, how it is deployed, and who may be affected. A model that drafts low-stakes text presents a different set of concerns from a workflow that influences access to employment, services, or other consequential decisions.
Be clear about the unit you are assessing: the model itself, a product that incorporates it, or the full deployed workflow—including its users, data, interfaces, human review, and downstream decisions. The NIST AI Risk Management Framework (AI RMF) treats risk management as a way to address potential impacts on individuals, organizations, and society. It is voluntary guidance, not a guarantee that any system is trustworthy.
How to evaluate an AI system’s risks
1. Define the system and its intended use
Write down what the system does, its main components, who will use it, what it is intended to do, and what it is not meant to do. Note whether the assessment covers a model, a software product, or an entire operational process. Include relevant versions or configurations so the findings can be tied to the system actually examined.
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2. Map the context and affected people
Identify who operates the system, who receives or relies on its outputs, and who could be affected without directly using it. Ask what decisions the output influences, what happens if it is wrong or unavailable, and what human oversight exists in practice. A review process matters only to the extent that reviewers can understand and challenge outputs and have the authority to act.
Consider the setting as well as the technology: the same system can have different consequences depending on the task, users, available alternatives, and how much people rely on its recommendations.
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3. Identify risks across relevant dimensions
Do not reduce the assessment to accuracy or a single overall score. NIST’s trustworthiness guidance identifies dimensions that include validity and reliability, safety, security and resilience, accountability and transparency, explainability, privacy, and harmful bias. Which dimensions matter most depends on the system and use, but a weakness in one can remain important even when performance looks strong on another.
- Validity and reliability: Does the system perform the intended task, and how consistently does it do so under relevant conditions?
- Safety: Could outputs or system behavior cause harm, including when the system is misused or fails?
- Security and resilience: Can the system withstand attacks, unauthorized access, or disruptions, and recover appropriately?
- Privacy: Could personal or sensitive information be exposed, inferred, or handled inappropriately?
- Fairness and harmful bias: Could performance or impacts differ in ways that disadvantage people or groups?
- Transparency, explainability, and accountability: Can relevant people understand the system’s role, question its outputs, and identify who is responsible for decisions and remedies?
4. Match tests to the risks and setting
Use more than one kind of evidence where the stakes and use warrant it. NIST’s AI Risk and Impacts Assessment (ARIA) describes model testing, red-teaming, and field testing. Its approach considers technical and contextual robustness as well as performance and accuracy.
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- Model testing examines a model under defined conditions. Record the test data, tasks, assumptions, and limitations; a result applies to what was tested, not automatically to every use.
- Red-teaming probes for failures or vulnerabilities through adversarial or challenging scenarios. The scenarios should be relevant to plausible misuse and failure, rather than treated as an exhaustive search.
- Field testing examines behavior in a real or representative use context. It can reveal issues that controlled tests miss, but findings still depend on the people, setting, and conditions observed.
For each test, report what was measured, under which conditions, and how closely those conditions match deployment. A benchmark pass is bounded evidence; it does not establish that a system is safe in every context. NIST’s AI Resource Center provides materials to help organizations operationalize the framework, including resources for testing, evaluation, verification, and validation.
5. Monitor incidents and reassess when things change
Risk assessment should continue after launch. Keep records of failures, unexpected impacts, complaints, and near misses, along with changes to the model, data, users, or deployment setting. Use those records to revisit assumptions, add tests, and adjust safeguards when needed.
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The OECD’s 2025 common framework for reporting AI incidents provides 29 criteria for capturing and comparing incidents across contexts. Those criteria describe a reporting structure; they are not a count of incidents or a measure of how common AI harms are.
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NIST’s AI RMF 1.0 is voluntary guidance for managing AI risks. NIST released it on January 26, 2023, and says it is being revised; check NIST’s current status before relying on version-specific details.
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For generative AI, NIST released its Generative AI Profile on July 26, 2024. It is intended to help organizations identify risks specific to generative AI and consider management actions aligned with their goals. These resources can structure an assessment, but neither a framework nor completion of its steps guarantees a trustworthy result. NIST cautions that considering trustworthiness characteristics cannot by itself ensure trustworthiness.
What this approach can—and cannot—tell you
A grounded assessment can clarify plausible harms, identify where evidence is thin, and guide testing, safeguards, and monitoring for a particular system and use. It cannot prove that an AI system is risk-free, settle every value judgment about acceptable risk, or answer speculative questions about future superintelligence. Those are separate questions from evaluating the behavior and impacts of a system in a defined context.
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