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Assess an AI system against the decision it will influence, the people affected, the evidence that it works in that setting, and the controls that will catch harm after launch. Start with a documented use-case review, test for failures and unequal outcomes, assign human accountability, and set conditions for monitoring, appeal, and suspension. In the United States, NIST’s AI Risk Management Framework (AI RMF) is a useful voluntary starting point—not a substitute for laws, agency rules, or supervisory expectations that may apply to a particular institution and use.
Start by defining the use and its consequences
“AI in government” and “AI in financial services” are not single legal or operational categories. A tool that drafts internal text has a different risk profile from one that affects eligibility for public benefits, helps set credit terms, flags a transaction for fraud review, or informs an enforcement action. Before assessing a system, identify the jurisdiction, institution, system, affected population, and specific decision or service. Those details determine which risks matter and which requirements may apply.
Write down the system’s intended purpose, who uses its output, where that output enters the decision pathway, and whether it is advisory or a principal basis for action. Include the surrounding workflow: a human may nominally make the final decision while relying heavily on an AI score or recommendation. Record foreseeable consequences of error, how reversible they are, and how an affected person can challenge or correct an outcome.
NIST describes its AI RMF as voluntary and intended to help organizations incorporate trustworthiness considerations across the design, development, use, and evaluation of AI. NIST’s AI RMF page says the framework is being revised, so check its current version status when applying it. Its four functions—Govern, Map, Measure, and Manage—provide a practical structure for the assessment and ongoing oversight.
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Use a lifecycle assessment, not a one-time approval
1. Govern: assign accountability
Decide who owns the use case, who can approve deployment, who independently reviews the assessment, and who can pause or withdraw the system. Set policies for acceptable use, documentation, human oversight, incident escalation, and reassessment. Make sure responsibilities cover the whole service, including vendors and operational teams, rather than stopping at the model developer.
2. Map: describe context, data, and affected people
Document the purpose, users, operating environment, inputs, outputs, and decision pathway. Identify individuals and communities who may be affected, including people who are not direct users. Consider impacts on rights, safety, access to services, financial outcomes, privacy, and opportunity. Record data origin, quality, representativeness, accuracy, currency, permission, retention, and access controls, as well as foreseeable misuse and failure modes.
3. Measure: test whether the system is fit for this use
Evaluate performance in the actual deployment context, not just on a vendor demonstration or a general benchmark. Define the relevant success and failure measures, test cases, thresholds, and subgroup analyses before reviewing results. Examine accuracy, robustness, explainability, privacy, security, and potential harmful impacts. Test edge cases and realistic failures, including missing, stale, or incorrect data and changes in user behavior. Record limitations and what evidence remains uncertain.
4. Manage: decide what to do and keep watching
Rank risks by likelihood, severity, affected population, and reversibility. For each material risk, name a mitigation, an owner, and a way to verify that the mitigation works. Set monitoring thresholds and escalation paths before deployment. Define events that trigger reassessment—such as a model or vendor update, a change in purpose or data, a shift in performance, a security incident, or a pattern of complaints—and specify when the system should be restricted or stopped.
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NIST’s AI RMF Playbook offers suggested actions organized around these functions. For generative AI, the NIST Generative AI Profile is a cross-sector companion published in July 2024. These materials support risk management; they do not determine whether a particular deployment complies with applicable law.
Pay special attention to government uses that affect rights or safety
For a government system, ask whether its output could affect a person’s rights, safety, eligibility, enforcement exposure, public benefits, or access to services. The 2023 federal executive-order text describes practices for relevant government uses that include assessing data quality, mitigating disparate impact and algorithmic discrimination, giving notice, continuously monitoring and evaluating deployed AI, and providing human consideration and remedies for adverse decisions. Its applicability and current status should be checked before treating any of those provisions as a current legal requirement; the Federal Register text is a source for what the order said, not a blanket statement of present obligations.
The Federal Reserve Board’s M-24-10 compliance plan illustrates one federal agency’s implementation approach. It describes assessing whether uses are safety- or rights-impacting, whether output is a principal basis for decisions, and what real-world harms could result; its impact assessments consider data, purpose, harms, security, testing, and validation. It is an agency example, not a universal rule for all public bodies.
In financial services, assess consumer, model, and third-party risks
Financial institutions should connect the AI use case to existing consumer and compliance obligations rather than treating AI as an exemption. The June 12, 2024 Federal Register notice identifies risks including discrimination and bias, privacy, inaccurate data or output, and vendor relationships, and notes that existing consumer financial protection and fair-lending laws may apply. The CFPB has likewise said it monitors whether companies using technologies marketed as AI violate federal consumer financial protection laws in its 2024 comment on Treasury’s AI request for information.
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The U.S. Treasury’s December 2024 financial-services AI report describes increasing AI use and highlights data privacy, bias, and third-party-provider risks. It recommends continued regulator-industry coordination, analysis of regulatory gaps and consumer harm, information sharing, and firms’ review of use cases for compliance with existing law before deployment and periodically afterward. Treasury reported receiving 103 comment letters in response to its 2024 request for information; that figure describes responses to the request, not the prevalence of AI-related harm.
Treasury’s March 2024 financial-sector cybersecurity report focuses on operational risk, cybersecurity, and fraud. Its discussion of “nutrition labels” for training-data origin and data handling is a prompt for due diligence, not a binding disclosure rule. Ask how the provider handles sensitive information entered into external services, secures model access, supports fraud detection without enabling fraud, maintains resilience, and reports incidents.
For bank model risk, the OCC’s April 17, 2026 revised interagency guidance addresses model development and use, testing, validation and monitoring, governance and controls, and validation of vendor or third-party products. The bulletin states that the guidance is not an enforceable standard or a prescriptive requirement. Banks should consider it alongside their supervisory context and applicable requirements rather than assuming it resolves every institution’s obligations.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare systems and deployment options on the same risk axes
When choosing between a vendor model, an internally developed system, or a non-AI process, compare options against the same use case and evidence standard. A lower error rate in aggregate may not make a system preferable if errors are concentrated among a vulnerable group, hard to contest, or difficult to reverse.
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| Assessment axis | Questions to compare | Particular emphasis |
|---|---|---|
| Consequence and reversibility | What happens when the output is wrong? Can the harm be undone, and how quickly? | Government: rights, safety, access to benefits or services. Financial services: consumer loss, access to credit, payment disruption, or fraud exposure. |
| Data quality and provenance | Where did the data come from? Are they accurate, current, representative, permitted, and traceable? | Both sectors: inspect data lineage, quality controls, retention, and access. Financial services also need clear handling of sensitive consumer information. |
| Performance and robustness | What evidence supports performance in this deployment, across relevant populations and failure cases? How is drift detected? | Government: evaluate potential disparate impact. Financial services: examine consumer outcomes and relevant fair-lending concerns. |
| Explainability and contestability | Can decision-makers understand the important factors behind an output? Can affected people challenge it and seek review? | Government: consider notice, human consideration, and remedies for consequential decisions. Financial services: assess how adverse outcomes can be reviewed and corrected. |
| Privacy, security, and resilience | Who can access inputs and outputs? How are data protected? What happens during an outage or compromise? | Financial services: include cybersecurity, fraud, and operational resilience. Both: define incident response and access controls. |
| Vendor dependency and oversight | What does the provider disclose about models, data, updates, subcontractors, and incidents? Can the organization validate the product and exit safely? | Both: examine contractual controls, change notifications, audit or testing support, and continuity plans. |
Questions to ask the system owner before approval
- What exact decision, service, or workflow uses the AI, and who may be affected directly or indirectly?
- What evidence demonstrates intended performance in this specific setting, including subgroup results, edge cases, and known failure modes?
- What data are used, where did they originate, how accurate and current are they, and who can access, retain, or reuse them?
- For consequential outputs, can a person understand the important factors, contest an error, and obtain human consideration or an appropriate remedy?
- How will the organization validate and monitor the system for drift, harmful errors, bias, privacy problems, security failures, and fraud?
- What does the provider disclose about the model, training-data handling, updates, incident response, access, and subcontractors? What contractual controls and exit options are available?
- Which laws, agency policies, supervisory expectations, and other rules apply to this jurisdiction, institution, use, and population—and who is accountable for confirming that analysis?
These questions are a practical review aid, not a claim that every control is legally required in every setting. Legal and supervisory duties depend on the specific system and use; the cited federal materials do not establish requirements for every state, agency, institution, or non-U.S. jurisdiction.
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