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Do not treat a vendor demo, a high benchmark score, or a signed purchase order as proof that an AI system is safe for your workplace. Assess the specific system, task, data, users, affected people, and consequences in a documented lifecycle review. A practical structure is the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF): Govern, Map, Measure, and Manage. The functions are iterative and should continue throughout the system’s life, not end at launch.
Your assessment should answer five decision questions before anyone relies on the model: What exactly will it do? Who could be harmed if it fails? What evidence shows that it works in this setting? What human controls and technical safeguards limit the harm? What evidence will make you pause, restrict, or retire it?
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Start with the use case, not the model
AI risk belongs to a particular use and operating context. The same model might be a low-consequence drafting aid in one workflow and a high-consequence decision tool in another. A general reputation, public benchmark, or polished demonstration cannot establish suitability for your employer’s data, users, policies, or legal obligations.
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Define the proposed use in one sentence that names the action and its consequence. “Summarize internal meeting notes for the author’s review” is materially different from “rank candidates for interview,” “recommend who receives overtime,” or “score employee performance.” Record the intended benefit and the decision the output may influence. Also write prohibited uses so that a pilot cannot quietly expand into a more consequential role.
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Separate assistance from decisions
Classify whether the system merely assists a person or materially influences an outcome affecting a worker, applicant, customer, or member of the public. A human label on a workflow does not automatically make it low risk. Ask what authority the reviewer has, whether they can reject the output, whether they have enough time and information to do so, and whether organizational pressure makes rubber-stamping likely.
Define the boundaries
- State which users may access the system and for which tasks.
- List data that may be entered, data that is forbidden, and the required classification for each.
- Specify whether outputs are advisory, require verification, or may trigger an automated action.
- Identify affected groups, including people who never directly use the tool.
- Set an expiration or review date for the approval.
Use the NIST lifecycle: Govern, Map, Measure, Manage
NIST describes AI RMF as voluntary, adaptable guidance rather than a certification or guarantee. Its central instruction is that “Risk management should be continuous, timely, and performed throughout the AI system lifecycle dimensions.” Use the four functions as a repeating loop: governance sets authority, mapping describes the real context, measurement produces evidence, and management treats what remains.
1. Govern the decision
Assign a named business owner who is accountable for the use case and a decision-maker who can approve, restrict, pause, or reject deployment. Their names should appear in the assessment record, not only in an informal project channel.
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Governance should bring in the people who can see different failure modes. Depending on the use, include procurement, information security, privacy, legal, human resources, accessibility specialists, worker representatives, safety staff, and domain experts. Document conflicts of interest and give reviewers enough independence to challenge the sponsoring team.
- Maintain an inventory of AI systems and their versions.
- Record approval authority, review cadence, training requirements, and monitoring responsibilities.
- Define retention, access, audit, incident-reporting, and decommissioning rules.
- Train users on permitted uses, confidential-data handling, verification, and escalation.
- Plan how the organization will disable the system and preserve necessary records if it is withdrawn.
Governance is not a one-time committee meeting. Reopen approval when the model, vendor, data source, user population, workflow, or applicable rule changes.
2. Map the actual system and context
Map the complete system rather than evaluating a model in isolation. Include vendor-hosted components, retrieval databases, plug-ins, human reviewers, downstream software, and any rules that convert an output into an action.
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Your map should identify:
- The model, product, version, configuration, and vendor dependencies.
- The precise task, expected benefit, prohibited uses, and deployment environment.
- Input data sources, classifications, provenance, retention, and access rights.
- Output types, recipients, downstream decisions, and the people affected.
- Known limitations, operating assumptions, language or accessibility constraints, and expected failure behavior.
- Where a human checks the result, what evidence they see, and what authority they possess.
- Applicable jurisdictions, contractual commitments, security controls, and organizational policies.
Describe realistic operating conditions. A résumé-screening model may receive unusual formatting, incomplete work histories, multiple languages, or accessibility-related accommodations. A support assistant may encounter urgent requests, hostile inputs, or confidential records. Those conditions belong in the map because they shape both performance and harm.
3. Measure before launch
Measurement should test the system against criteria tied to its intended task, under representative conditions. A vendor’s generic benchmark can be one input, but it cannot substitute for deployment-specific evidence.
Define acceptance criteria before looking at results. Depending on the use, criteria may cover factual accuracy, completeness, false-positive and false-negative patterns, response time, escalation rates, calibration of confidence, or the percentage of outputs requiring correction. State what error rate is unacceptable and why; a tolerable typo rate is not a tolerable rate for a payroll or safety recommendation.
Test across relevant groups and conditions. Look for differences associated with language, disability, age, sex, race or ethnicity where lawful and appropriate to evaluate, job role, location, seniority, and other characteristics that may affect outcomes. Fairness testing is not a single score: inspect the pathway from data to output to decision and investigate material disparities.
Measure trustworthiness characteristics that fit the use:
| Dimension | Questions to answer | Evidence to retain |
|---|---|---|
| Validity and reliability | Does it perform the intended task consistently in the target workflow? | Task-specific test set, error analysis, repeatability results, and known limits. |
| Safety | Can an output create physical, financial, psychological, or employment harm? | Hazard analysis, unsafe-output tests, safeguards, and escalation results. |
| Security and resilience | Can inputs manipulate the system, expose data, or cause service failure? | Adversarial testing, access review, abuse cases, recovery and continuity tests. |
| Accountability and transparency | Can a responsible person explain who approved the system and how incidents are handled? | Ownership records, policies, audit logs, notices, and incident procedures. |
| Explainability and interpretability | Can users understand enough about an output to challenge it appropriately? | Explanations, user testing, uncertainty displays, and override records. |
| Privacy enhancement | Are collection, use, retention, sharing, and deletion appropriate? | Data-flow map, privacy review, contract terms, retention settings, and access logs. |
| Fairness with harmful bias managed | Does the system create unjustified differences or amplify existing inequities? | Disaggregated results, qualitative review, mitigation decisions, and unresolved limitations. |
| Accessibility | Can people with relevant disabilities use the interface and review its outputs? | Accessibility testing, alternative processes, and accommodation procedures. |
Use quantitative, qualitative, or mixed methods as appropriate. Keep the test protocol, data description, limitations, reviewers, dates, and raw results. Independent review is proportionate when consequences, complexity, or uncertainty are high. Testing must continue after deployment because data, users, prompts, vendor components, and model behavior can change.
4. Manage residual risk
Measurement rarely eliminates all risk. Decide what remains and whether it is within the tolerance set during governance. If it is not, narrow the task, add controls, choose another system, or do not deploy.
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Controls should address the observed failure, not merely create paperwork. Examples include restricting inputs to approved fields, masking sensitive data, limiting access by role, requiring a second reviewer, forcing verification against an authoritative source, blocking automatic actions, adding rate limits, and preserving an appeal route for affected people.
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Build a deployment risk record
A concise record makes the decision auditable and gives operators a shared reference. Treat the following as a practical checklist derived from NIST outcomes, not as a mandatory NIST form.
| Record section | What to capture |
|---|---|
| Identity | System and vendor name, model or product version, configuration, dependencies, and deployment date. |
| Purpose | Intended task, expected benefit, prohibited uses, output recipients, and downstream actions. |
| People | Business owner, approver, operators, affected workers or customers, worker representatives where relevant, and incident owner. |
| Data | Sources, classification, provenance, permissions, retention, transfers, training or vendor reuse, and deletion process. |
| Evidence | Evaluation protocol, representative conditions, results, error patterns, subgroup analysis, security and privacy findings, and limitations. |
| Oversight | Human review point, reviewer competence, authority to override, required verification, notices, and appeal or correction route. |
| Operations | Monitoring metrics, alert thresholds, complaint handling, audit access, change-control process, and review schedule. |
| Exit | Pause triggers, rollback method, communications, record retention, and decommissioning criteria. |
Test the workflow, not just the model
A model can be technically accurate yet unsafe in a workplace because users misunderstand confidence, cannot challenge a recommendation, or apply it to a population unlike the test set. Run end-to-end scenarios with the people who will operate the process.
Use representative and adverse cases
- Include ordinary cases, borderline cases, missing information, unusual formats, and multiple languages used in the workplace.
- Include deliberately misleading, ambiguous, or malicious inputs to observe failure behavior.
- Test what happens when the model is unavailable, slow, overconfident, or produces no answer.
- Check whether a reviewer can detect a wrong output without privileged technical knowledge.
- Record corrections, overrides, time pressure, and whether users follow the required procedure.
Examine human factors
Interview or observe users after realistic exercises. Ask which outputs they trust, what they would do when the system disagrees with a supervisor, and whether they understand the limits shown in the interface. Measure whether review is meaningful or merely a click-through step. If users cannot explain when to escalate, training and interface changes are controls to implement before approval, not optional enhancements.
Document uncertainty honestly
State where evidence is weak: small or non-representative test data, untested languages, changing vendor behavior, unavailable incident history, or unknown downstream effects. An uncertainty statement should lead to a control, a narrower scope, additional testing, or a no-deployment decision. Do not convert an absence of evidence into an assurance of safety.
Compare alternatives on identical conditions
When several systems or a non-AI workflow could meet the need, compare them using the same task definition, data conditions, acceptance criteria, and reviewers. A lower-performing model may still be preferable if it exposes less data, supports stronger oversight, or fails more safely.
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| Comparison area | What to compare |
|---|---|
| Performance | Task accuracy, error types, uncertainty, reliability, latency, and correction burden. |
| Robustness and security | Behavior under unusual inputs, prompt manipulation, outages, abuse, and attempted data extraction. |
| Data practices | Provenance, collection, retention, deletion, transfers, access controls, and whether inputs are reused. |
| Impact | Results across affected groups, accessibility, foreseeable harms, and available remedies. |
| Oversight | Quality of explanations, reviewer authority, auditability, notices, appeals, and override support. |
| Operations | Monitoring, incident reporting, change notifications, logging, rollback, vendor dependency, contract terms, and total operational effort. |
Make trade-offs explicit in the approval record. “Best model” is not a meaningful conclusion without stating which risks and costs were prioritized.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Generative AI needs additional checks
NIST’s July 2024 Generative AI Profile is a cross-sector companion to AI RMF 1.0. It focuses on risks that are novel to or intensified by generative systems and highlights governance, content provenance, pre-deployment testing, and incident disclosure.
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For generated text, images, summaries, code, or recommendations, define the verification step for the task. A reviewer should know which claims require checking, what source is authoritative, and whether generated content must be labeled or accompanied by provenance information. Test for fabricated citations, omitted qualifications, unsafe instructions, disclosure of confidential material, and persuasive but incorrect explanations. Record how incidents will be reported to affected users, internal leaders, and any party required by contract or law.
Legal and jurisdictional review
NIST AI RMF is voluntary guidance. It does not replace legal advice, regulatory analysis, collective-bargaining obligations, employment law, privacy duties, accessibility requirements, or sector-specific rules. Obtain jurisdiction-specific review before relying on an approval decision.
EU employment uses
The EU AI Act lists certain employment and worker-management systems as high-risk. Examples include systems intended for recruitment or selection and certain systems that affect work-related decisions, task allocation, monitoring, or evaluation. Classification depends on the precise intended purpose, the Act’s terms and exceptions, and whether profiling is involved. Do not label every workplace AI application high-risk without checking those details.
For a covered high-risk system used at the workplace, an employer acting as deployer must inform workers’ representatives and affected workers before use. Deployers must monitor the system in line with its instructions and keep automatically generated logs under their control for an appropriate period of at least six months, subject to applicable Union or national law.
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Set approval gates and stop conditions
Turn the assessment into a decision with explicit gates. A simple record can use four outcomes:
- Approve for the defined scope: Evidence meets acceptance criteria, controls are operating, owners are trained, and residual risk is within tolerance.
- Approve with restrictions: Limit users, data, geography, output authority, or duration; require extra review and a near-term reassessment.
- Hold for remediation: A specific gap must be fixed and retested before any production use.
- Reject or retire: Evidence is inadequate, harms exceed tolerance, controls are ineffective, or a safer alternative is available.
Predefine stop conditions such as a serious privacy or security incident, a material discriminatory pattern, repeated unsafe outputs, unexplained performance deterioration, inability to complete required human review, a vendor change outside approval, or a legal determination that the use is not permitted. Assign someone with authority to act immediately, without waiting for the next routine meeting.
Operate the assessment after launch
Monitoring should connect to the risks you identified. Track the measures that can reveal deterioration or harm, such as error and correction rates, subgroup differences, override frequency, user complaints, security events, data-access anomalies, response refusals, and changes in input distribution. Set thresholds and an owner for each signal; a dashboard without an action rule is not monitoring.
Review incidents and near misses, not only confirmed harm. Preserve relevant prompts, inputs, outputs, reviewer actions, timestamps, model versions, and communications according to your retention rules. Protect personal information while retaining enough evidence to investigate.
Reassess after a model update, prompt or policy change, new data source, vendor change, altered user population, new integration, material incident, or change in law. If the organization cannot explain what changed and whether prior evidence still applies, pause expansion until the gap is resolved.
A worked example: an internal résumé-screening assistant
Suppose an employer proposes an assistant that summarizes applications and suggests which candidates merit recruiter review. The first map would identify applicants as affected people, résumé and accommodation data as sensitive inputs, recruiters as reviewers, and interview selection as the downstream consequence. A prohibited-use rule might forbid automatic rejection.
Measurement would use a representative, permissioned historical or synthetic set and compare summaries and recommendations with expert review. The team would inspect omission of relevant experience, errors caused by formatting or language, and differences across groups. It would test whether recruiters can spot an incorrect recommendation and whether time pressure turns review into rubber-stamping.
Management controls could restrict the assistant to summarization, require recruiters to consult the original application, block automatic ranking, log overrides, and provide a correction route. Governance would name the HR owner, legal and privacy reviewers, an incident lead, and a date for reassessment. If evidence showed material unexplained disparities or ineffective human review, the correct outcome would be remediation, a narrower use, or no deployment—not a claim that the model is safe because it performed well on a general benchmark.
The practical decision rule
Deploy only when the organization can describe the use precisely, identify accountable owners, show task-specific evidence, operate effective human and technical controls, meet applicable obligations, and respond quickly when reality differs from the plan. If any of those conditions is missing, the responsible action is to narrow the use, gather better evidence, add controls, select a different approach, or stop.
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