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NIST’s AI Risk Management Framework (AI RMF) provides a voluntary structure for this work. In the European Union, AI Act Article 27 creates a more specific fundamental-rights assessment for certain deployers and certain high-risk systems. They can be used together, but they are not interchangeable legal instruments.
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What an AI risk assessment should accomplish
A useful assessment answers six operational questions:
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- What system and version are being deployed, for what purpose and in which workflow?
- Who will use it, who may be affected, and what action follows an output?
- What specific harms could occur, how serious and likely are they, and what evidence supports that judgment?
- Which controls, testing steps and human-review arrangements reduce those harms?
- Who owns each control, what residual risk remains, and who can stop or escalate the system?
- What decision was made, under which conditions, and what changes will trigger a new assessment?
The result should be an auditable decision record rather than a single unexplained risk score. Scores can help prioritize work, but the rationale, evidence, assumptions and limitations are what allow another person to understand and challenge the decision.
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Step 1: Set scope, ownership and the deployment boundary
Start with the concrete deployment. Record the system name, provider, model or application version, interfaces, data sources, deployment location and the date of the assessment. State the intended purpose in plain language and identify the deployer, accountable owner, operational team and approval stage.
Describe the system’s role in a decision or workflow. Is it generating a draft, ranking cases, recommending an action, making a classification, or triggering an automated action? Note where a person can review, correct or override an output. Include the boundaries of the deployment: business unit, geography, user population, hours of operation and connected systems.
This scope prevents a common error: approving a model in the abstract while overlooking a materially different use in production. The same model can create different risks when used for customer support, employment screening, benefits administration or safety-related decisions.
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Describe how often and for how long the system will be used. Include expected volume, peak periods, one-time or recurring use, and whether the deployment is a pilot, limited release or permanent process.
Map the path from input to output to action:
- Identify who supplies prompts, records or other inputs and what information enters the system.
- Describe the output, its format and any confidence, ranking or explanation shown to users.
- Identify the person or software that acts on the output, including escalation and override points.
- Record where outputs are stored, shared or incorporated into another decision.
List categories of natural persons and groups likely to be affected in this context. Consider both direct users and people who never interact with the interface but may be scored, screened, contacted, denied a service or otherwise affected by an output. Be specific about groups relevant to the workflow rather than relying on a generic statement that “all users” may be affected.
Step 3: Identify plausible harms and uncertainty
Write risk scenarios as concrete cause-and-effect statements. For example: “If the system ranks an applicant using incomplete records, qualified people in this group may be deprioritized, and staff may not notice because the ranking is presented as authoritative.” A scenario should identify the failure, the affected people, the resulting harm and the point in the workflow where the harm can occur.
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Use provider documentation, instructions and known limitations as inputs, but do not treat them as a complete assessment of your deployment. For a broader internal review, examine these areas:
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- Reliability: inaccurate, unstable, unavailable or non-reproducible outputs.
- Privacy: collection, inference, exposure, retention or inappropriate reuse of personal information.
- Security: prompt injection, data exfiltration, unauthorized access, abuse or manipulation.
- Fairness and rights: discriminatory outcomes, unequal access, loss of autonomy or other effects on fundamental rights.
- Operations: overreliance, workload shifts, unclear accountability, vendor dependency and failures in downstream systems.
This list is an internal checklist, not a claim that EU AI Act Article 27 enumerates each category. Article 27 focuses on the specific risks of harm to affected people in covered deployments.
Step 4: Assess each risk and preserve the evidence
For every scenario, record the likelihood and severity reasoning, the people or groups exposed, evidence reviewed, known limitations and unresolved assumptions. Evidence can include evaluation results, representative test cases, incident history, user research, provider technical documentation, process maps and relevant privacy or security analyses.
No universal scoring scale is mandated by the cited NIST or Article 27 materials. If your organization uses numerical scores, define the scale, decision thresholds and treatment of uncertainty before scoring cases, and apply them consistently. A score without its underlying rationale should not be presented as an objective measurement.
Preserve failed tests and contradictory evidence as well as favorable results. State where testing does not represent the deployment population, where data is sparse, which behaviors were not tested and which assumptions still require confirmation. Assign an owner and due date to each unresolved issue.
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Step 5: Choose controls and design human oversight
For each risk, specify the mitigation, responsible owner, expected effect and remaining risk. Controls may include restricting the purpose, narrowing the user population, filtering inputs, improving data quality, adding validation rules, requiring a second review, logging decisions, limiting retention, rate-limiting access or disabling automated actions.
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Describe human oversight as an operating procedure, not merely a statement that “a human is in the loop.” Identify:
- who reviews outputs and what training or authority they have;
- which cases require mandatory review or escalation;
- what information the reviewer receives, including relevant limitations;
- how a reviewer can override, correct or suspend the system;
- how overrides and disagreements are recorded; and
- who can stop use when a serious risk appears.
Also document what happens if a risk materializes. Include incident triage, containment, user notification where appropriate, correction or reversal of affected decisions, root-cause analysis and escalation to governance or legal teams. Provide a complaint route that affected people can actually use, with an owner and response process.
Step 6: Make and record the deployment decision
The approval record should state one of four outcomes: proceed, proceed with conditions, delay, or reject. List the conditions that must be met before launch, the approvers, open issues, accountable owners and the next review date.
Use a decision record that links each material risk to its control and residual-risk judgment. Explain why the remaining risk is acceptable for the stated purpose, or why the system is being limited until evidence improves. Keep the assessment, test results, approvals and change history together so an auditor or incident investigator can reconstruct the decision.
NIST’s four functions provide a practical way to organize this lifecycle: Govern establishes policies, roles and accountability; Map describes context and impacts; Measure gathers evidence and evaluates performance; and Manage prioritizes responses and tracks residual risk.
Step 7: Monitor operation and trigger reassessment
Define monitoring before launch. Choose signals that can reveal changing performance or harm, such as error patterns, complaints, override rates, distribution shifts, security events, unexpected use, downtime and changes in the affected population or workflow. Set thresholds and owners for investigation rather than collecting metrics with no response plan.
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List events that trigger a reassessment:
- a new model, provider, version, interface or connected data source;
- a change in purpose, user group, geography, frequency or decision authority;
- material changes in performance, incident patterns or security posture;
- new evidence that an affected group experiences different outcomes;
- a complaint, regulatory inquiry or serious near miss; or
- an update to applicable law or classification.
For an assessment covered by Article 27, the deployer must update the information when a listed element changes or is no longer current. Recital 96 likewise describes updating assessments when relevant factors change.
What to put in the assessment file
A practical file can contain these sections:
- System and use-case description: purpose, version, provider, users, workflow, deployment boundary and approval stage.
- Context and affected groups: frequency and duration of use, input-to-action map and categories of people or groups affected.
- Risk register: scenario, affected people, likelihood and severity rationale, evidence, limitations, assumptions and residual risk.
- Testing and evaluation: test design, population coverage, results, failed cases and unresolved gaps.
- Controls: technical and procedural mitigations, expected effect, owner, due date and escalation path.
- Oversight and complaints: reviewer responsibilities, override rules, incident response, governance and complaint handling.
- Decision record: outcome, conditions, approvers, open issues and review date.
- Monitoring plan: signals, thresholds, reporting cadence and reassessment triggers.
- Change log: version changes, context changes, incidents, updates and resulting decisions.
Treat this as a durable governance record, not a one-time form. Store the evidence that supports important judgments and control access to personal or security-sensitive material.
NIST AI RMF and EU AI Act Article 27: how they differ
| Comparison | NIST AI RMF 1.0 and Playbook | EU AI Act Article 27 |
|---|---|---|
| Who is covered | Organizations choosing to apply a broad, use-case-agnostic framework. | Specified public bodies, private entities providing public services, and deployers of certain high-risk systems identified in Annex III, subject to stated exceptions. |
| Legal status | Voluntary guidance. NIST states that the AI RMF and Playbook are intended for voluntary use. | A legal duty for covered deployers and systems. |
| Core structure | Govern, Map, Measure and Manage functions, with adaptable actions in the Playbook. | A fundamental-rights impact assessment describing specified use, affected groups, risks, oversight and measures if risks materialize. |
| Timing | Organizations choose timing and review cadence according to context. | Before first deployment of a covered system; information must be updated when listed elements change or become outdated. |
| External communication | No general submission requirement created by the framework. | The deployer notifies the market-surveillance authority using the specified template, subject to an exemption referenced in the Article. |
| Relationship to privacy records | Can be combined with existing privacy, security and governance processes. | Where certain data protection impact assessment duties already cover the relevant work, Article 27 permits cross-references or incorporation of relevant parts. |
These approaches complement one another but should not be described as equivalent. NIST’s framework page says AI RMF 1.0 is being revised, and the Playbook page says it will be updated after the framework is revised. Check the current versions when setting internal policy.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When Article 27 applies in the EU
Article 27 states: “Prior to deploying a high-risk AI system referred to in Article 6(2) … deployers … shall perform an assessment of the impact on fundamental rights that the use of such system may produce.” The provision is scoped; it does not make every AI deployment subject to the same assessment.
The consolidated text displayed by the European Commission AI Act Service Desk as at 27 July 2026 describes coverage for specified public bodies, private entities providing public services and deployers of certain Annex III high-risk systems, with exceptions. The assessment is performed before first use. It must describe:
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- the intended period and frequency of use;
- categories of affected people or groups;
- specific risks of harm to those people or groups, taking provider information into account;
- how human oversight is implemented; and
- measures to take if risks materialize, including internal governance and complaint mechanisms.
The deployer must notify the market-surveillance authority with the required template, unless the Article’s exemption applies. If a relevant data protection impact assessment already contains the information, the Article allows cross-references or incorporation of relevant parts. Confirm the current consolidated legal text, system classification and deployment circumstances before relying on this summary.
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Recital 96 says deployers may, where appropriate, involve representatives of affected groups, independent experts and civil-society organizations. It presents this as possible stakeholder participation, not a universal requirement.
Practical mistakes that weaken an assessment
- Assessing the model instead of the use: a general capability description does not show who is affected or what action follows an output.
- Using an unexplained score: a number without evidence, assumptions and uncertainty cannot support a defensible decision.
- Calling oversight “human in the loop” without procedures: reviewers need authority, information, time and a way to override the system.
- Ignoring indirect affected people: people subject to a ranking or recommendation may never see the interface.
- Leaving response measures until after launch: complaints, incident handling and suspension authority should be assigned in advance.
- Failing to track change: a new version, data source, purpose or population can invalidate the original assessment.
- Confusing guidance with law: voluntary NIST practices do not determine whether Article 27 applies.
Publication dates and maintenance
NIST published AI RMF 1.0 on 26 January 2023. Its Generative AI Profile was published on 26 July 2024. The official Playbook page was updated on 10 June 2026. These are publication dates, not evidence of a measured reduction in incidents or other outcome statistics. No standalone incident-rate or effectiveness statistics are established by these materials.
Because legal text and implementation guidance can change, record the version and date of every framework or legal source used in an assessment. Recheck the current EU consolidated text and NIST revision status at each material review.
A concise pre-deployment readiness check
- The intended purpose, users, workflow, version and deployment boundary are documented.
- Frequency, duration, input-to-action flow and affected groups are identified.
- Specific harm scenarios include evidence, assumptions and limitations.
- Controls, residual risks, owners and escalation paths are assigned.
- Human oversight, override, incident response and complaint routes are operational.
- The decision, conditions, approvers, open issues and review date are recorded.
- Monitoring signals and change-triggered reassessment rules are defined.
- EU applicability has been checked rather than assumed, and any Article 27 notification or cross-reference duty is addressed.
Frequently Asked Questions
Do all AI systems require a formal impact assessment before use?
No. NIST AI RMF is voluntary, and EU AI Act Article 27 applies to specified deployers and certain high-risk systems, subject to exceptions. Determine the system’s classification, deployer status and use context before deciding which obligations apply.
Can a privacy impact assessment replace an AI risk assessment?
Not automatically. Article 27 allows relevant parts of certain data protection impact assessments to be cross-referenced or incorporated, but the resulting record still needs the required information about affected groups, specific harms, oversight and measures if risks materialize.
What should happen when an AI system changes after approval?
Compare the change with the documented reassessment triggers. A new version, purpose, user group, data source, performance pattern, incident or legal requirement may require updated testing, controls and approval before continued use.
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