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Assess an AI system in the context where people will actually use it, not as an abstract model. Before approval, document its purpose, users, workflow, affected people, possible harms, evidence, mitigations, human oversight, owners, and conditions for stopping or changing the deployment. Keep that record current as the system or its context changes.
A practical process can use the voluntary NIST AI Risk Management Framework (AI RMF) 1.0 to organize governance, mapping, measurement, and management. If the deployment falls within the scope of European Union AI Act Article 27, you may also have a specific legal duty to complete a fundamental-rights impact assessment before first use. These resources support each other but are not interchangeable.
What an AI risk assessment must accomplish
An assessment should let an accountable decision-maker answer five questions:
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- What exactly will the system do, for whom, where, and under which version?
- Who could be helped, disadvantaged, exposed, or otherwise affected?
- What harmful outcomes are plausible, how serious could they be, and what evidence supports that judgment?
- Which controls, human checks, response plans, and complaint routes reduce those risks?
- Who approved the deployment, under what conditions, and when will the assessment be revisited?
The object of assessment is the complete sociotechnical use: the model or service, its inputs and data, interfaces, operators, downstream decisions, and operating environment. The same model can have very different risks in a low-stakes drafting tool and a system that influences access to employment, housing, education, healthcare, benefits, or public services.
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Do not present a number such as “low risk” as an objective fact unless you can explain the scale, assumptions, evidence, and decision rule behind it. The cited frameworks do not prescribe one universal scoring method. A consistent internal scale can be useful, but the reasoning and supporting evidence matter more than a score alone.
Step 1: Set scope, ownership, and the approval boundary
Open the record before technical testing. Establish what is being approved and who is accountable for the decision.
Record the system and deployment identity
- System name, provider, model or service version, and relevant configuration.
- Intended purpose and prohibited or out-of-scope uses.
- Deployment location, business unit, environment, and interfaces with other systems.
- Whether outputs inform a person, trigger an automated action, or do both.
- Expected launch date, approval stage, and assessment owner.
Capture version identifiers and deployment boundaries precisely enough to distinguish the assessed configuration from a later update. A provider change, fine-tuning change, new data source, or new integration can alter the risk profile.
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Assign a business owner who can accept or reject residual risk, a technical owner responsible for operation, and named reviewers for privacy, security, legal, safety, or domain controls as appropriate. Record who can pause the system and who receives escalations. “The AI team” is not an accountable owner.
Define the decision gate
State whether this file supports a pilot, limited production, full production, or a material change. Include the decision date and the choices available: proceed, proceed with conditions, delay pending evidence, or reject. Conditions should be testable, owned, and dated.
Step 2: Map the real use and the people affected
Describe the path from input to output to action. This is where abstract model risk becomes a concrete assessment.
Describe workflow and human roles
- Identify who supplies inputs, what data is used, and what preprocessing occurs.
- Describe the output shown to each user, including confidence indicators, warnings, or missing-context messages.
- State what a person or downstream system does with the output.
- Specify whether a human can override, correct, or ignore the output and how that action is recorded.
- Document fallback procedures when the system is unavailable, uncertain, or withdrawn.
Record intended frequency and duration of use: for example, continuous triage during business hours, a monthly batch, or a two-week pilot. Include the geographic and organizational context because legal scope, language, population, and available human support can change the consequences.
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List direct users and people who may be evaluated, ranked, contacted, monitored, denied a service, or otherwise affected without interacting with the system. Include relevant categories of natural persons and groups in the specific context. Consider children, people with disabilities, language minorities, workers, customers, applicants, patients, students, residents, and people represented in training or reference data where relevant.
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For each group, explain the route of impact. A customer may be affected by a recommendation; an applicant may be screened; a worker may be scheduled; a resident may be subject to a public-service decision. Do not assume that only the operator is a user.
Step 3: Identify plausible harms and uncertainty
Write risks as concrete scenarios rather than broad labels such as “bias” or “hallucination.” A useful scenario states the failure, the context, the affected people, and the consequence.
Use a risk-scenario format
“If [condition], the system may [failure], causing [harm] to [affected group], because [known mechanism or uncertainty].” Examples include:
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- If historical records underrepresent a language group, the system may misclassify requests, delaying access to a service.
- If an operator treats a generated summary as complete, an omitted warning may influence a high-impact decision.
- If an attacker manipulates an input or retrieved document, the system may disclose information or recommend an unsafe action.
- If a model update changes output patterns, an approved workflow may produce different error rates without a new review.
Cover multiple risk dimensions
For an internal assessment, examine safety, reliability, privacy, security, fairness, explainability, accessibility, compliance, and operational effects as relevant to the use case. This is an adaptable checklist, not a claim that EU AI Act Article 27 enumerates every category. Include harms from incorrect, incomplete, inconsistent, discriminatory, unavailable, manipulated, or unauthorized outputs.
Document uncertainty and assumptions
State what you do not know: limited representative test data, untested languages, unknown downstream behavior, unavailable provider documentation, or assumptions about operator attention. An uncertainty can itself be a launch condition. Record the evidence reviewed, its date, scope, and limitations instead of converting a gap into a reassuring conclusion.
Step 4: Assess likelihood, severity, and evidence
Choose a method your organization can apply consistently. The frameworks do not mandate a universal scale, so define your own terms before rating individual risks.
Define a transparent rating method
One simple approach rates likelihood and severity on five levels, each with written definitions. For example, likelihood can range from rare to frequent based on observed or reasonably foreseeable occurrence; severity can range from minimal inconvenience to serious rights, safety, financial, or societal harm. If you calculate an overall level, publish the rule and explain when a high-severity scenario requires action regardless of likelihood.
Do not hide disagreement. Record who supplied the estimate, what data or tests informed it, and why the estimate may be wrong. A provider’s benchmark can inform the assessment but does not establish performance in your population or workflow.
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Preserve supporting evidence
- Provider instructions, limitations, model cards, safety notices, and version notes.
- Representative test sets, evaluation protocols, error analyses, and results.
- Security reviews, privacy analyses, red-team findings, and accessibility checks.
- Interviews with operators, domain experts, and potentially affected groups.
- Incident, complaint, correction, and override data from pilots or comparable systems.
Keep a link or identifier to each artifact in the assessment repository, with its owner and date. If a test is not representative of production, say so next to the result.
Step 5: Choose controls and human oversight
Every material risk needs a control, an owner, and a way to verify whether the control works.
Design controls at several layers
- Purpose and data: narrow the intended use, minimize inputs, validate data quality, and restrict sensitive fields.
- Model and configuration: select an appropriate version, constrain prompts or outputs, set thresholds, and test updates before release.
- Workflow: require human review for defined cases, prohibit automatic action where judgment is required, and provide a clear override path.
- Access and security: enforce least privilege, protect logs, separate environments, and address prompt injection or data exfiltration threats.
- User experience: label generated content, expose uncertainty and limitations, support accessibility, and provide instructions for correction.
- Organizational response: define incident triage, suspension authority, notification duties, remediation, and complaint handling.
Specify meaningful human oversight
“Human in the loop” is not enough. Identify who reviews what, at which point, with what information and time, and what authority they have. Train reviewers to recognize automation bias, allow them to override the output, and monitor whether review is actually occurring. If volume makes real review impossible, redesign the workflow instead of keeping a nominal approval step.
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For each scenario, state the expected effect of the control, the remaining risk, the owner, and the trigger for escalation. Explain why residual risk is acceptable for the stated purpose or why deployment must wait. Include a stop or rollback condition that can be executed by an identified person.
Step 6: Build an assessment file and risk register
A single, versioned file makes the reasoning auditable. It can be a controlled document linked to a structured risk register and evidence repository.
Recommended assessment-file contents
- System, provider, version, purpose, users, location, workflow, and deployment boundary.
- Intended frequency and duration of use.
- Affected categories of people and groups, with the path of impact.
- Risk scenarios, likelihood and severity rationale, evidence, limitations, and assumptions.
- Evaluation results, known failure modes, and unresolved questions.
- Mitigations, human-oversight design, residual risks, owners, and due dates.
- Incident response, suspension, notification, correction, and complaint mechanisms.
- Decision, conditions, approvers, open actions, and next review date.
- Monitoring signals, change triggers, and a dated change log.
Example risk-register fields
| Field | What to record |
|---|---|
| Risk ID and scenario | A unique identifier and a specific failure-and-harm statement |
| Affected people | Groups exposed, including indirect or non-user groups |
| Likelihood and severity | Defined ratings with the rationale, evidence, and uncertainty |
| Existing controls | Technical, workflow, organizational, and oversight measures already operating |
| Residual risk | Remaining exposure after controls and the acceptance rationale |
| Owner and action | Named person, required action, due date, and escalation path |
| Monitoring trigger | Signal, threshold, incident, complaint, or change that starts reassessment |
The table is a practical design, not a universal statutory checklist. Tailor fields to the system and retain the source evidence behind every material judgment.
Step 7: Make the deployment decision and prepare operations
Convert the assessment into an operational decision, not a filing exercise.
Use explicit decision outcomes
- Proceed: controls are operating, evidence is sufficient for the stated scope, and residual risk is accepted by the accountable owner.
- Proceed with conditions: launch is limited by population, volume, features, geography, or mandatory human review until named actions are complete.
- Delay: a critical evidence gap, control failure, or unresolved legal question prevents safe approval.
- Reject: the purpose cannot be achieved with acceptable residual risk or the system is unsuitable for the context.
List each condition, owner, due date, and verification method. Require approval from the people who own the affected process, not only from the technical team.
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Prepare incident and complaint handling
Define how users and affected people report an error or harm, who acknowledges the complaint, how records are preserved, and how corrections or decisions are reconsidered. Set criteria for pausing the system, notifying internal governance or authorities, and communicating with affected people. Test the escalation route before launch.
Monitor in production
Track signals that match the identified risks: error patterns, override rates, drift, uptime, access anomalies, privacy events, disparate outcomes where measurable, complaints, and near misses. Assign a review cadence and a person who examines the signals. Monitoring should be capable of detecting a control that exists on paper but fails in practice.
When to reassess and update the record
Reassessment is required whenever the system or context can change the original reasoning. Define triggers in advance:
- Model, provider, prompt, retrieval source, threshold, or integration changes.
- New users, affected groups, geography, language, purpose, or decision authority.
- Higher volume, longer duration, expanded automation, or removal of human review.
- Material performance drift, incidents, near misses, security events, or complaints.
- New evidence about limitations, rights impacts, or applicable law.
Version the assessment, record what changed and why, and link the change to a new approval decision. Do not overwrite the prior record; a history shows which information was available when each decision was made.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.NIST AI RMF and EU AI Act Article 27: related but different
NIST AI RMF 1.0 is a voluntary, rights-preserving, non-sector-specific, use-case-agnostic framework published January 26, 2023. Its Playbook is also voluntary and organizes suggested actions under four functions: Govern, Map, Measure, and Manage. NIST says the Playbook is based on AI RMF 1.0 and will be updated after the framework is revised; the framework page states that AI RMF 1.0 is being revised. NIST also lists a Generative AI Profile published July 26, 2024. These dates identify publications, not measured reductions in incidents.
The official NIST Playbook page states: “The AI RMF and the Playbook are intended for voluntary use.” Use it to structure internal governance whether or not a law applies, and adapt the suggested actions to your sector and risk.
EU AI Act Article 27 is a scoped legal provision. The consolidated text displayed by the European Commission AI Act Service Desk as at July 27, 2026 requires, before deployment, a fundamental-rights impact assessment for specified public bodies, private entities providing public services, and deployers of certain high-risk systems identified in Annex III, subject to the Article’s exceptions. It applies to first use. Covered deployers must update the information when a listed element changes or is no longer current, and must notify the market-surveillance authority using the specified template, subject to an exemption referenced in the Article. Where certain data protection impact assessments already meet the relevant requirements, Article 27 permits cross-references or incorporation of relevant parts.
Recital 96 describes the purpose as identifying specific risks to the rights of affected people and measures to take if those risks materialize. It says deployers could, where appropriate, involve representatives of affected groups, independent experts, and civil-society organizations; that is a possible participation measure, not a blanket requirement.
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| Question | NIST AI RMF 1.0 | EU AI Act Article 27 |
|---|---|---|
| Who is covered? | Any organization choosing to use the framework; it is use-case-agnostic. | Specified deployers and certain high-risk systems within the Article’s scope and exceptions. |
| Is it mandatory? | No. NIST describes the framework and Playbook as voluntary. | Yes, where the legal scope applies, before first deployment. |
| What does the record contain? | Flexible actions organized through Govern, Map, Measure, and Manage. | Processes and intended use; period and frequency; affected people or groups; specific risks of harm; human oversight; and measures if risks materialize, including internal governance and complaint mechanisms. |
| When is it updated? | According to the organization’s lifecycle and change controls. | When listed information changes or is no longer current; Recital 96 also points to updates when relevant factors change. |
| Who receives results? | Internal stakeholders or others chosen by the organization. | The deployer must notify the market-surveillance authority with the required template, subject to the Article’s exemption. |
| How does it relate to privacy records? | Can be integrated with privacy, security, safety, and other governance records. | Relevant parts may be cross-referenced or incorporated from certain data protection impact assessments where the Article allows it. |
Do not assume that using NIST satisfies Article 27, or that Article 27 is a complete AI governance program. Confirm the current consolidated legal text, the system’s classification, the deployer’s role, and the applicable exceptions before relying on this summary.
Common documentation failures and fixes
Assessing the model instead of the use
Failure: a generic model description with no workflow, users, or affected groups. Fix: map the input-to-action path and state the decision role.
Using unsupported risk scores
Failure: a color or number with no definitions or evidence. Fix: publish the scale, rationale, uncertainty, and decision rule.
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Calling a nominal reviewer “human oversight”
Failure: a reviewer lacks time, context, training, or authority to override. Fix: specify review points, information, authority, workload, and override logging.
Ignoring indirect impacts
Failure: only system operators are listed as affected people. Fix: include applicants, customers, workers, residents, patients, students, or others subject to downstream action.
Freezing the file at launch
Failure: no change log, monitoring owner, or reassessment trigger. Fix: define signals and versioned review events before production.
Confusing voluntary guidance with legal compliance
Failure: an organization treats an AI RMF checklist as proof that Article 27 does not apply. Fix: perform a separate applicability analysis and complete any required legal assessment.
Quick Recap
A launch checklist
- System, version, provider, purpose, location, workflow, and accountable owner are recorded.
- Intended users, frequency, duration, and automation level are explicit.
- Direct and indirect affected people and groups are identified.
- Specific harm scenarios, assumptions, evidence, and limitations are documented.
- Likelihood and severity definitions are consistent and reviewable.
- Controls, human oversight, residual risk, escalation, and stop conditions have owners.
- Testing, privacy, security, accessibility, and domain reviews are attached where relevant.
- Complaint, correction, incident, and notification routes are operational.
- Decision, conditions, approvers, open actions, and review date are recorded.
- Monitoring signals, change triggers, and a versioned change log are in place.
- EU AI Act Article 27 applicability has been checked separately where the EU is relevant.
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