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The practical answer: manage AI risk as a repeating business process, not a one-time approval. Assign accountable owners, inventory every AI use, map who and what it can affect, test the system against its actual purpose, set human decision boundaries, monitor it in production, and keep a response plan for incidents and material changes. NIST’s voluntary AI Risk Management Framework (AI RMF) organizes that work into Govern, Map, Measure, and Manage.
This checklist is operational guidance, not a legal compliance certificate. AI obligations depend on where you operate, your sector, the people affected, and the decision being supported. Obtain jurisdiction- and sector-specific advice before deploying AI in consequential settings.
What AI risk management covers
AI risk management is the disciplined process of deciding whether and how an AI system may be used, reducing foreseeable harms, and checking whether controls continue to work. It applies to systems your organization builds, buys, embeds in another product, or accesses through a cloud service. It also applies when employees use general-purpose or generative AI outside a formal project.
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NIST released AI RMF 1.0 on January 26, 2023. NIST describes it as voluntary, rights-preserving, non-sector-specific, and use-case-agnostic guidance for organizations of different sizes. It provides a structure rather than a guarantee that a system is trustworthy or a statement that an organization satisfies every law, regulation, contract, or standard.
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NIST’s framework page currently says AI RMF 1.0 is being revised. Its Playbook page says the Playbook will be updated after that revision. NIST also reports that a concept note for a critical-infrastructure profile was released on April 7, 2026. Re-check the current NIST pages and applicable rules when you adopt or refresh your process.
Use the four-function cycle
Govern, Map, Measure, and Manage are connected activities, not a linear project that ends at launch. A material change in a model, data source, vendor, purpose, users, or affected population should send the use case back through the relevant functions.
| Function | Business question | Typical work product |
|---|---|---|
| Govern | Who is accountable, and what rules apply? | Approved-use policy, named owners, escalation route, decision authority, review records |
| Map | What is the system, where is it used, and who could be affected? | AI inventory entry, use-case description, data and dependency map, impact and misuse assessment |
| Measure | What evidence shows that risks are understood and controls work? | Test plan, representative scenarios, acceptance criteria, limitations, monitoring indicators |
| Manage | Which risks will be accepted, reduced, transferred, paused, or escalated? | Control decisions, remediation plan, incident playbook, rollback or shutdown procedure, review dates |
NIST’s Playbook groups suggested actions and references under these functions. The framework is intentionally flexible: it does not prescribe one universal control set or one approval workflow.
1. Govern: set ownership and rules before anyone deploys
Name an executive sponsor and an operational owner
An executive sponsor provides authority, resources, and a route for unresolved risk decisions. An operational owner is responsible for the system’s day-to-day purpose, users, controls, and performance. Record both names, their authority, and the conditions under which they must escalate.
Involve the functions that can identify different classes of harm: legal, privacy, security, procurement, human resources, compliance, accessibility, safety, and the business team that owns the process. The right participants vary by use case; a recruiting tool and a software-development assistant do not need identical reviews.
Publish an approved-use policy
State which tools and uses are approved, prohibited, or subject to additional review. Cover whether employees may enter confidential, personal, regulated, or customer information into an external service; whether generated code or content requires review; and which decisions may not be made or materially influenced by AI alone.
Give users a simple escalation route. A policy that says “use good judgment” without a named contact, response expectation, and stop-work authority is difficult to enforce.
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Identify who makes the final decision when an AI output is used in a workflow. The accountable person should be able to inspect relevant evidence, reject an output, request correction, and explain the decision to an affected person where required. Do not treat a vendor’s contract or a model’s output as a substitute for internal accountability.
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2. Build and maintain an AI inventory
You cannot manage systems you cannot see. Create an inventory that includes formal projects, embedded features in ordinary software, internally built models, vendor services, application programming interface calls, and employee-deployed generative AI. This is an implementation practice aligned with the Govern and Map functions, not a form prescribed by NIST.
For each entry, record:
- System or feature name, model or vendor, and version where available
- Intended purpose, business process, and accountable business owner
- Users, access groups, deployment environment, and external dependencies
- Data types entered, generated, retrieved, or used for training or tuning
- People or groups affected by outputs, including customers, employees, applicants, students, patients, or members of the public
- How an output influences a decision, action, ranking, recommendation, or communication
- Whether the use involves generative AI, an external service, automated action, or a high-consequence process
- Known limitations, required human review, approval status, and next review date
Revisit the entry when the model, vendor, data, purpose, user population, connected system, or level of automation changes. An apparently minor feature change can alter the risk profile if it expands who is affected or removes a human checkpoint.
3. Map context and possible harms before use
Describe the real decision or task
Write down what the system does in operational terms. “Uses AI for hiring” is too vague; distinguish screening, ranking, interview summarization, scheduling, or drafting communications. State whether the output is advisory, automatically acted on, or used by a person who may strongly rely on it.
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List direct users and people who may never interact with the system but can be affected by it. Consider language, disability, age, socioeconomic status, geography, and other characteristics relevant to the process. Prioritize deeper analysis where errors could restrict access to employment, credit, housing, healthcare, education, essential services, or safety.
Map foreseeable misuse and downstream effects
Ask how an authorized user, an attacker, or an accidental recipient could misuse the system. Trace outputs into downstream databases, decisions, notifications, or models. Check whether an incorrect answer can be amplified by automation, copied without context, or treated as an authoritative record.
Trace data provenance and sensitivity
Document where input, retrieval, and evaluation data come from, what permissions govern it, and whether it contains personal, confidential, proprietary, or regulated information. Record assumptions about data quality, representativeness, freshness, and consent or other lawful basis where relevant. Confirm legal requirements separately; this checklist does not determine them.
4. Review trustworthiness characteristics
Use NIST’s trustworthiness characteristics as prompts for a use-case-specific review. NIST identifies validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy enhancement; and fairness with harmful bias managed. These are risk lenses, not a guarantee that a system is trustworthy.
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Define what “correct enough” means for the actual task. Check performance on representative cases, edge cases, changed conditions, and the languages or formats users will submit. A model that performs well on an average benchmark may still be unsuitable for a narrow business decision.
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Safety
Identify outputs or actions that could cause physical, financial, operational, or psychological harm. Establish refusal, confirmation, and human-intervention conditions for high-impact actions. Test what happens when inputs are incomplete, contradictory, adversarial, or outside the system’s intended scope.
Security and resilience
Consider prompt injection, data exfiltration, unauthorized use, model or dependency compromise, denial of service, and unsafe connected actions. Limit privileges, separate environments where appropriate, protect credentials, and define how the service will operate or fail when a dependency is unavailable.
Accountability and transparency
Keep enough documentation to identify the owner, purpose, data sources, model or service, review history, limitations, and decision path. Tell users when AI is involved where that information is material to their choices or rights, subject to applicable requirements.
Explainability and interpretability
Decide what explanation a user or affected person needs to challenge or understand an outcome. If the system cannot provide a meaningful explanation for a consequential use, restrict its role or require a stronger human decision process.
Privacy enhancement
Minimize collected data, restrict access, set retention and deletion expectations, and prevent secondary use that was not approved. Verify how an external service processes prompts, outputs, logs, and administrator data before deployment.
Fairness and harmful bias management
Identify groups that could experience different error rates or disparate effects. Use appropriate disaggregated tests and qualitative review, investigate material differences, and document mitigation or a decision not to deploy. Fairness questions depend on the context; a single aggregate score cannot answer them.
5. Control data, access, and vendors
Write a plain-language data rule for each use case:
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- What users may enter, upload, retrieve, or generate
- Where processing occurs and which parties can access the information
- Whether prompts and outputs are retained, used for training, or visible to administrators
- How retention, deletion, export, and account closure are handled
- Which roles may use the system and what actions each role can trigger
Review vendor terms, security information, service dependencies, change-notification practices, and incident contacts before approval. Ask the vendor for evidence supporting performance, security, privacy, and fairness claims; do not treat marketing assertions as test results. For a purchased or embedded capability, record what your organization can configure, monitor, pause, or replace.
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6. Set human oversight and decision boundaries
Match oversight to impact and risk rather than applying the same workflow to every tool. Define three boundaries explicitly:
- Review: when a qualified person must inspect an output before it is used.
- Approval: when a person must authorize an action, decision, or communication.
- Prohibition: when the system must not make or materially influence a decision by itself.
Specify the reviewer’s competence, available evidence, time to review, and authority to override the system. Avoid “rubber-stamp” review in which workload or interface design makes rejection impractical. Give users and affected people a route to challenge, correct, or appeal an outcome, with a record of what was reviewed and changed.
7. Measure before launch
Establish a baseline and acceptance criteria
Define the intended population, task, operating conditions, and acceptable failure modes. Select tests that resemble real inputs and include boundary cases, known risks, and situations in which the model should abstain. Record the test date, model or service version, data or prompts used, evaluator, results, and limitations.
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Probe for inaccurate, discriminatory, unsafe, privacy-invasive, or overconfident outputs. For generative systems, include prompt injection, instruction conflicts, sensitive-data extraction attempts, fabricated citations, and inappropriate content relevant to your use. Test connected actions separately from text generation: an incorrect draft is different from an incorrect payment, deletion, access change, or customer notification.
Document the release decision
Record which criteria were met, which risks remain, who accepted them, what controls are required, and when the system must be reassessed. If evidence is insufficient, narrow the use, add safeguards, run a limited pilot with appropriate protections, or do not deploy.
8. Monitor after deployment and reassess after change
Production behavior can differ from pre-launch tests. Monitor performance, error patterns, complaints, incidents, appeals, security events, data drift, model or prompt changes, vendor changes, and shifts in how users rely on outputs. Set thresholds that trigger investigation, additional review, a restricted mode, or a pause.
Define what counts as a material change. Examples include a new model or version, a new data source, a different geographic or demographic population, a new downstream action, removal of human review, a changed vendor term, or evidence of a new failure mode. Route material changes through a proportionate re-assessment rather than assuming the original approval still applies.
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9. Prepare incident response and accountability records
Before launch, decide how the organization will:
- Pause, disable, or roll back the system and connected actions
- Preserve prompts, outputs, logs, configurations, and decision records needed for investigation
- Notify the operational owner, executive sponsor, security team, legal team, and other decision-makers
- Assess who was affected and correct or revisit erroneous outcomes
- Communicate with customers, employees, regulators, partners, or other parties when required
- Identify root causes and update data, model, access, training, or human-review controls
Keep a record of the inventory entry, context and impact assessment, approvals, tests, limitations, incidents, remediation, and review dates. Records should be proportionate to risk but sufficient to reconstruct what the system did and why the organization allowed it.
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Generative AI requires additional review
NIST AI 600-1, the Generative AI Profile, was published on July 26, 2024 as a cross-sector companion to AI RMF 1.0. NIST says it addresses risks novel to or exacerbated by generative AI and provides suggested actions across lifecycle stages and shared business processes, including large-language-model use, cloud services, and acquisition.
“This document defines risks that are novel to or exacerbated by the use of GAI.”
— NIST AI 600-1, published July 26, 2024
Apply the core checklist to prompts, retrieval data, system instructions, fine-tuning data, generated text or code, tool calls, and human review. Pay particular attention to fabricated information, leakage of confidential material, unlicensed or inappropriate content, prompt injection, output over-reliance, and the possibility that a generated artifact will be reused outside its original context.
Choose controls in proportion to the use case
NIST’s risk-oriented approach allows different implementations. Compare your governance approach using these axes:
| Decision axis | Questions to answer |
|---|---|
| Risk and impact | What is the consequence of an incorrect, biased, unsafe, or unavailable output? |
| Organization and expertise | Which internal skills, independent reviewers, and escalation capacity are available? |
| Build, buy, or embed | Which controls can your organization configure, test, monitor, and change? |
| Data sensitivity | What information is processed, and what would disclosure, alteration, or loss mean? |
| Sector and geography | Which employment, finance, health, education, consumer, critical-infrastructure, privacy, or other rules may apply? |
| Evidence quality | How strong and representative are testing, monitoring, security, and vendor-evidence results? |
| Human review and documentation | What level of review, challenge, recordkeeping, and explanation is practical for the impact involved? |
A low-impact drafting assistant may need access restrictions, disclosure, and quality review. A system that ranks applicants, approves credit, recommends treatment, or controls a safety-related operation requires a substantially stronger evidence, oversight, and appeal process—or may be inappropriate for autonomous use.
Check legal and contractual obligations separately
The AI RMF is voluntary. Following it does not, by itself, satisfy privacy law, employment rules, sector regulation, consumer-protection requirements, accessibility duties, intellectual-property obligations, security contracts, records rules, or notification requirements. Determine where the organization operates, where affected people are located, what sector the activity belongs to, and whether the use is consequential.
Ask qualified legal and compliance advisers to identify obligations for the actual system and workflow. Include contractual commitments to customers, suppliers, employees, and data processors. Re-check requirements when the organization enters a new jurisdiction, changes the purpose, or expands the affected population.
A practical operating rhythm
Use the lifecycle to make the checklist repeatable:
- Before approval: assign owners, inventory the system, map context and harms, define data rules, select trustworthiness tests, and set human boundaries.
- Before launch: run representative and adversarial tests, document limitations and acceptance criteria, confirm access and incident controls, and record the approval decision.
- During use: monitor performance, complaints, incidents, security signals, data and model changes, and whether users are bypassing required review.
- After a material change or incident: pause or restrict use when necessary, investigate, correct affected outcomes, update controls, and record the new review date.
The result is a living governance record rather than a one-time questionnaire. It makes clear who can authorize use, what evidence supports that decision, what users must do, and when the organization must stop and reassess.
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