To create an AI risk assessment, define what the complete product or workflow does and who it affects; identify plausible benefits and harms; evaluate each risk using evidence and stated assumptions; choose controls; assign accountable owners; and monitor for change. NIST’s voluntary AI Risk Management Framework (AI RMF) offers a useful structure—Govern, Map, Measure, and Manage—but a completed assessment is not proof that a system is safe or legally compliant.
What an AI risk assessment should cover
Assess the AI system in its actual setting, not just the model in isolation. A product may combine a model with software, data sources, user interfaces, human review, and external services. A workflow may also change who makes decisions, what information they see, or how quickly they act.
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For each material risk, record a specific scenario, who could be affected, the likelihood or uncertainty, the possible consequence, existing controls, proposed mitigation, an owner, and supporting evidence. NIST defines risk in terms of the likelihood of an event and the magnitude of its consequences. It does not prescribe one scoring scale for every organization or use case.
- Purpose and boundary: what the system is intended to do, what it is not intended to do, and which system components are included.
- People and setting: users, affected groups, deployment environment, and any human decisions or review points.
- Lifecycle and dependencies: whether the system is being designed, tested, deployed, or operated, and what data, models, services, or processes it depends on.
- Benefits and harms: intended benefits, foreseeable failures or misuse, and possible effects on individuals, organizations, society, or the environment where relevant.
- Evidence and accountability: how risks were evaluated, what remains uncertain, who will act, and who has authority to accept residual risk or pause deployment.
Step 1: Define the product or workflow boundary
Write a short description of the use case before scoring anything. State the intended purpose, users, affected groups, setting, inputs, outputs, human roles, lifecycle stage, and dependencies. Distinguish the model from the full system that people actually use.
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For example, an assessment of an AI assistant used to draft customer-support replies should cover more than the language model. Include the customer records or prompts it receives, the interface used by staff, whether a person reviews each draft, what can be sent automatically, and the systems that store or route the reply. This boundary helps reveal risks that arise from operation, inputs, outputs, or integration—not only from model design or training.
Step 2: Govern the assessment
Decide who is responsible for the assessment and who can make decisions based on it. Set roles for identifying risks, evaluating evidence, implementing controls, handling incidents, and approving or pausing deployment. Record who is authorized to accept residual risk and under what conditions.
NIST’s AI RMF Playbook is voluntary guidance with suggested actions and references for the framework’s four functions. Use it to organize work, not as a mandatory checklist or a substitute for decisions tailored to your system. Document escalation routes and ensure that an identified risk has an owner with the authority and resources to address it.
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Step 3: Map context, benefits, and harms
Describe how the AI changes the existing process: what it automates, recommends, summarizes, generates, or prioritizes, and how people may rely on its output. Identify intended benefits alongside foreseeable failure modes and misuse. Consider who receives the benefit, who bears the risk, and whether an error could be difficult to detect or reverse.
Use scenarios rather than broad labels. “Bias” or “hallucination” alone is not an actionable risk description. A more useful scenario names the setting, failure, and consequence—for example, an incorrect generated answer being sent to a customer without review, causing them to take an unsuitable next step. Adapt each scenario to the real workflow and affected people; do not assume that an example applies to every system.
Step 4: Measure the risks that matter
Choose a transparent method for assessing likelihood or uncertainty and consequence severity. The method can use qualitative categories or numeric scores, but define what each category means for this use case. A score is a prioritization aid, not objective certainty: record the evidence, assumptions, known limitations, and unresolved uncertainty behind it.
Consider which trustworthiness characteristics matter in context: validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness or harmful bias. These characteristics can involve tradeoffs, and their relative importance varies by setting. Do not treat a single aggregate score as proof that all relevant concerns have been addressed.
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For testing and evaluation, select evidence that fits the system’s purpose and potential impact. NIST’s AI Resource Center provides technical documents and resources for testing, evaluation, verification, and validation. Record what was tested, under what conditions, what the results show, and what the tests do not establish.
Step 5: Manage risks and document decisions
Prioritize risks based on the defined method and the consequences of getting the decision wrong. Choose controls or system changes, give each action an owner and due date, and record the evidence needed to verify that the action worked. If a risk remains, document the rationale for accepting it, the decision-maker, any conditions, and what would trigger reconsideration.
A practical register can use these fields:
| Field | What to record |
|---|---|
| Risk scenario | The context, event or failure, and possible consequence. |
| Affected parties | Users and other people or groups who could experience the outcome. |
| Likelihood or uncertainty | The assessment using your defined scale, with assumptions and evidence. |
| Severity | The potential magnitude of the consequence, with the reason for the rating. |
| Existing controls | Measures already in place and evidence of their operation. |
| Mitigation and verification | The planned control or change, how it will be checked, and its due date. |
| Owner and decision | The accountable person, residual-risk decision, and any deployment conditions. |
| Monitoring and triggers | Signals to watch and events that prompt reassessment or escalation. |
Step 6: Evaluate before and after deployment
Plan evaluation around the actual use, including the inputs, outputs, and human roles that shape outcomes. Use tests suited to the potential impact, and keep records that allow reviewers to understand the conditions and limits of the evidence. For generative AI, assess risks associated with generated outputs as well as how prompts, inputs, operation, and system scope affect them.
NIST’s Generative AI Profile, NIST-AI-600-1, was published on July 26, 2024, as a cross-sectoral companion to AI RMF 1.0. It addresses generative AI risks that can arise at different lifecycle stages, including from model design, training, operation, inputs, or outputs.
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Reassess when the system or its context changes in a way that could alter risk: for example, a change in purpose, users, data, model, integrations, degree of automation, or deployment setting. Also review after relevant incidents or evidence that controls are not working as intended. Define monitoring and triggers in advance. NIST supports lifecycle risk management but does not set one fixed review cadence for every organization.
What the NIST framework does—and does not—establish
NIST released AI RMF 1.0 on January 26, 2023. It is voluntary and use-case agnostic, and its framework page says it is being revised. The Playbook is based on AI RMF 1.0; NIST says it will update the Playbook after revising the framework. Check NIST’s current framework materials when applying them because revision status and resources can change.
Using the framework does not, by itself, establish compliance with every legal or sector-specific obligation. Determine applicable requirements separately based on geography, sector, use, and affected people. An AI risk assessment helps organize decisions and evidence; it is not a universal certification of safety or compliance.
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