Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsAssess an AI system for compliance risk by documenting its intended use and your organization’s role, identifying the laws and obligations that apply in that context, testing the system against defined criteria, and recording a go, restricted-go, remediate, defer, or no-go decision. A framework can organize that work, but it cannot by itself establish legal compliance: the answer depends on the system, its use, the affected people, the jurisdiction, and who is developing or deploying it.
What should a pre-deployment assessment establish?
The assessment should leave a decision-maker with enough evidence to answer four questions: What exactly is being deployed and for what purpose? Which obligations apply to this organization in this use case? What risks remain after testing and controls? And, given those risks, should deployment proceed, proceed with restrictions, wait for remediation, or stop?
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Run the assessment for a defined system and deployment context—not for “AI” in the abstract. A model or vendor product used to draft internal meeting notes presents a different set of affected people, potential harms, and legal questions from a system that ranks job candidates, determines eligibility, or influences access to essential services. A material change in model, configuration, data, users, or purpose can change the answer.
This guide provides a repeatable risk-management process, not a universal legal checklist or legal advice. Have qualified counsel assess how applicable law applies to your specific facts.
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How should you organize the review?
The NIST AI Risk Management Framework (AI RMF 1.0) is voluntary, cross-sector guidance organized around four functions: Govern, Map, Measure, and Manage. It can help structure responsibilities and evidence across the AI lifecycle; using it does not itself establish compliance with a law or replace jurisdiction-specific analysis. NIST’s AI RMF Playbook offers suggested actions and references, but NIST says it is neither a checklist nor an ordered set of steps every organization must adopt. NIST reports that AI RMF 1.0 is being revised, so consult the current official NIST materials when applying it.
NIST says the framework was developed over 18 months with contributions from more than 240 organizations. That is background about its development, not evidence that it produces compliance or particular risk outcomes.
For generative AI, NIST AI 600-1, the AI RMF Generative AI Profile, is a companion resource published July 26, 2024. NIST’s publication page reports an update on April 8, 2026. It suggests risk-management actions, while noting that their applicability depends on organizational considerations and the tasks of the AI actor. Use it alongside—not in place of—the core framework and legal analysis.
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What information should you collect before judging risk?
Create a record for the specific system and deployment under review. Assign an accountable business owner and identify who can approve deployment; a committee can advise, but responsibility should not be ambiguous.
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- System identity: product and model names, relevant model and configuration versions, provider, vendors, connected services, and planned deployment date.
- Purpose and boundaries: the intended business purpose, tasks the system may perform, decisions it may influence, prohibited uses, foreseeable misuse, and limits communicated by the provider.
- People and setting: users, people affected by outputs or decisions, deployment environment, user expectations, scale, and whether a human reviews or can override the output.
- Data and dependencies: inputs, output recipients, data sources and flows, sensitive or personal information involved, integrations, and operational dependencies.
- Organizational and legal context: jurisdictions, sector, relevant business process, and your role in the system’s lifecycle—such as developer, provider, integrator, or deployer.
Do not treat a vendor’s product description as a substitute for this context record. Provider documentation can inform the review, but the organization still needs to establish what it will actually do with the system and who may be affected.
How do you identify the rules that apply?
Map requirements to the use case, jurisdiction, sector, system category, and organizational role. NIST AI RMF Govern 1.1 specifically calls for understanding, managing, and documenting legal and regulatory requirements. The review may need to cover privacy, employment, consumer protection, sector-specific rules, intellectual property, and AI-specific regulation. Which areas apply depends on the facts; do not assume every AI system has the same obligations.
For each potentially applicable requirement, record the jurisdiction and source, the obligation or question to resolve, the role responsible, the evidence needed, and the person accountable for the conclusion. Mark unresolved questions explicitly and assign an owner and due date rather than treating an assumption as a legal determination.
For EU use, independently determine the system’s classification and the organization’s role under the AI Act, then check the provisions that apply to that combination. The Act has role- and category-specific duties; the fact that an organization uses an AI system does not by itself establish that every high-risk deployer obligation applies.
EU deployer duties and rights assessments
Article 26 addresses deployers of high-risk AI systems. Among other matters, it requires appropriate measures to use the system according to its instructions and addresses human oversight, monitoring, input data, logs, and communication of risks or incidents. Read Article 26 in full context and determine which provisions apply to the particular system and deployer; do not infer a complete compliance program from a short list of topics.
Article 27 requires certain deployers to perform a fundamental rights impact assessment before deploying specified high-risk AI systems. The trigger depends on both the deployer type and system category, so it is not a universal requirement for every AI deployment. The provision permits coordination with certain data-protection impact assessment work in circumstances it specifies.
EU application dates to verify
The European Commission AI Act Service Desk timeline cited for this guidance reports the following milestones. These dates concern different sets of provisions, not one general date on which every AI Act duty begins. Verify the official timetable and the provisions applicable to your case before acting, since implementation details and guidance can change.
| Reported milestone | Application date | What to take from it |
|---|---|---|
| Transparency obligations | August 2, 2026 | This date has passed as of October 7, 2026. Check which transparency provisions apply to the system and activity in question. |
| Annex III high-risk system rules | December 2, 2027 | Use the intervening period to establish whether the system falls within a relevant category and prepare for applicable requirements. |
| High-risk AI systems embedded in regulated products | August 2, 2028 | This is a separate milestone for the specified category; do not apply it as a general date for all high-risk systems. |
How do you turn the context into concrete risks?
Identify how the system could fail or cause harm in its actual setting, including downstream effects and uses outside the intended scope. Consider the NIST AI RMF characteristics—validity and reliability, safety, security and resilience, accountability, transparency and explainability, privacy, and harmful bias—but translate each relevant concern into a specific scenario rather than treating the list as a pass/fail checklist.
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For each scenario, record who could be affected, the potential consequence, how likely and serious it may be in the deployment context, what controls already exist, and what evidence supports the assessment. Examples of useful prompts include:
- Could an inaccurate or unstable output lead a user to make a consequential decision without adequate review?
- Could the system perform differently for affected groups, or produce harmful bias through its data, design, or use?
- Could sensitive data be exposed, used outside the authorized purpose, or retained or logged inappropriately?
- Could prompt manipulation, unauthorized access, or a dependent service failure undermine security or resilience?
- Will users understand when AI is involved, what the system can and cannot do, and how to challenge or escalate an output?
- Could an output be reused downstream in a way that changes the stakes or bypasses intended human oversight?
Separate risks inherent to the system from risks introduced by the workflow around it. A capable model can still create unacceptable risk if staff rely on it for decisions beyond its demonstrated limits, if users cannot contest an outcome, or if no one is responsible for responding to an incident.
What tests should happen before deployment?
Define acceptance criteria from the intended use and risk scenarios before evaluating performance. Select test data and methods that reflect realistic operating conditions, relevant users and inputs, known edge cases, and plausible misuse. A test that measures general model quality does not establish suitability for a particular decision or population.
NIST AI RMF Core states: “AI systems should be tested before their deployment and regularly while in operation.” The assessment record should make that principle operational by capturing the method, results, limitations, and reviewer—not just a statement that testing was completed.
- Performance and validity: evaluate whether outputs meet the task-specific acceptance criteria; examine error types and uncertainty, not only an aggregate score.
- Reliability and limitations: test realistic variations in inputs and operating conditions, identify failure modes, and establish what the system should not be asked to do.
- Bias and impact: assess relevant groups and outcomes in the deployment context, using methods appropriate to the data and use. Document limits in the evaluation rather than claiming that bias has been ruled out.
- Security and resilience: test relevant threats, access boundaries, dependencies, and recovery assumptions for the planned environment.
- Privacy and data handling: check the actual input, output, retention, access, and logging arrangements against applicable requirements and commitments.
- Human-AI interaction: establish whether users can recognize uncertainty, understand appropriate reliance, review outputs meaningfully, and escalate or override when needed.
Document the version tested, configuration, data and test conditions, acceptance thresholds, results, known gaps, and any independent review. If a system is changed after testing, establish whether the change invalidates earlier evidence and requires targeted or full reassessment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you make and record the deployment decision?
Compare the risks remaining after controls with the organization’s approved risk tolerance and the expected benefits. A favorable test result does not settle the decision if an applicable obligation is unresolved or a severe harm has no credible mitigation. Conversely, a documented risk may be manageable if suitable safeguards, oversight, and escalation are in place.
| Decision | When it fits | What the record should specify |
|---|---|---|
| Go | Applicable requirements are resolved, evidence meets acceptance criteria, and residual risk is within approved tolerance. | Approver, approved scope, operating controls, and review conditions. |
| Restricted go | Deployment is acceptable only for a limited purpose, user group, location, or operating condition. | Explicit boundaries, controls preventing out-of-scope use, monitoring, and conditions for expansion. |
| Remediate | Known gaps appear addressable, but must be fixed before the relevant use is allowed. | Control or evidence required, accountable owner, deadline, and retest or approval gate. |
| Defer | Material facts, legal analysis, testing, or operational readiness are not yet sufficient to decide. | Open questions, owners, due dates, and the evidence needed to return for a decision. |
| No-go | Residual risk exceeds tolerance, a required condition cannot be met, or the use cannot be made acceptably safe and compliant. | Reason for rejection, decision authority, and any conditions under which a new proposal could be considered. |
Record the rationale, evidence reviewed, material uncertainty, dissent or unresolved issues, decision authority, and any conditions attached to approval. NIST’s Manage function includes deciding whether development or deployment should proceed and prioritizing treatment of identified risks; the decision should be traceable to the evidence and accountable owner.
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Approval is not the end of assessment. Specify how the deployment will be kept within its approved scope and how emerging problems will be detected and handled. Assign owners and make the controls actionable in operational procedures.
- Human oversight: define who reviews outputs, what review requires, when a person must intervene, and who can stop or override the system.
- Access and inputs: limit access and data to authorized users and purposes; document constraints for sensitive or untrusted inputs.
- Monitoring and logs: identify performance, error, drift, complaint, and incident signals to monitor; define logging responsibilities and access controls.
- User communication: explain appropriate use, relevant limitations, escalation paths, and any required notice or transparency measures.
- Incident response: identify who receives reports, how suspected harm or security events are escalated, and who determines whether use must be restricted or suspended.
- Change management: review material changes in model or version, configuration, data, vendor, workflow, purpose, affected population, or jurisdiction before they enter operation.
- Rollback and shutdown: specify authority, triggers, and practical steps to suspend the system or return to a prior process.
- Review cadence: set a schedule suited to risk and operating conditions, alongside event-triggered reviews after material changes, incidents, or evidence that assumptions no longer hold.
For high-risk EU AI deployments, confirm the applicable Article 26 deployer duties and whether Article 27 requires a fundamental rights impact assessment for the particular deployer and system category. Build any applicable requirements into the operating plan rather than relying on a generic monitoring policy.
What should the final assessment file contain?
Keep a concise, auditable record that another reviewer can use to understand what was approved and why. A practical file includes:
- System inventory entry, versions, vendors, accountable owner, and decision authority.
- Intended purpose, deployment boundaries, affected people, data flows, dependencies, and jurisdictions.
- Organizational role and documented analysis of applicable legal and regulatory requirements, including open questions and counsel’s advice where needed.
- Risk scenarios, affected parties, existing safeguards, residual risks, and approved risk-tolerance basis.
- Acceptance criteria, test methods and conditions, results, limitations, and independent-review findings where used.
- Decision and rationale, including restrictions, remediation items, accountable owners, deadlines, and approval conditions.
- Operating controls, monitoring and incident procedures, review cadence, change triggers, and rollback or shutdown arrangements.
Keep the file current as the system and its use evolve. It is an evidence trail for governance and reassessment, not proof of compliance by its existence alone.
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