Evaluate an AI risk tool against a defined task, representative evidence, and the conditions in which it will actually be used—not a vendor demo or a universal score. Set the use case and acceptable risk first, then test the system, examine the provider and its dependencies, document controls and approval, and monitor performance after deployment.
Start with the system’s purpose and boundaries
Before comparing vendors, write down what the tool is meant to do and what decision its output may influence. A tool that flags unusual transactions, estimates exposure, summarizes risk information, or recommends an action can create different risks even if each produces a number or label. Be precise about the intended task rather than treating every quantitative output as the same kind of model.
- Task and decision: What does the system produce, who uses it, and what decision does it inform? State what it is not authorized to decide.
- People and consequences: Who could be affected by an error, and how serious, widespread, or reversible could the harm be?
- Operating context: Record the institution type, jurisdiction, business function, data sources, users, workflow, and deployment setting.
- Human role: Identify who reviews outputs, can challenge or override them, and remains accountable for the decision.
- System type: Distinguish traditional statistical or quantitative models, non-generative AI, generative AI, and agentic systems that can take actions or call tools.
- Failure and fallback: Specify what happens if the system is unavailable, produces an implausible result, or cannot be confidently interpreted.
These boundaries determine which internal controls and supervisory expectations are relevant. For U.S. banking organizations, the Federal Reserve’s interagency model risk guidance dated April 17, 2026 covers traditional statistical and quantitative models and non-generative, non-agentic AI; it expressly excludes generative and agentic AI because those systems are novel and rapidly evolving. The guidance says existing risk management and governance practices should inform controls for tools outside its scope. Read the Federal Reserve’s guidance and stated scope.
Set evaluation depth according to materiality
Decide how much evidence and oversight the use case warrants before choosing a vendor or a performance metric. Consider the potential impact and scale of errors, how easily decisions can be reversed, the institution’s exposure, and how much users will rely on the output. Set risk tolerance and approval authority in advance; not every model or application presents the same level of risk.
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The Federal Reserve says its 2026 guidance is most relevant to banking organizations with over $30 billion in total assets. That is a relevance statement for banking organizations—not a universal regulatory threshold for financial firms, AI tools, or the application of risk management. The guidance describes a risk-based approach tailored to an organization’s model risk profile, size, and complexity. See the Federal Reserve’s applicability discussion.
Demand evidence that matches your use
Ask the provider for material that lets your team assess the system, not just its marketing claims. A benchmark, generic accuracy score, or polished demonstration does not establish that the tool will work for your institution’s population, data distribution, products, workflow, or market conditions. Require documented, repeatable tests under conditions similar to the proposed deployment, and record the evidence and its limitations.
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Evidence to request
- A description of intended purpose, system design, key assumptions, output interpretation, and known limitations.
- Descriptions of development and evaluation data, including how well they represent the proposed users, activity, and operating environment.
- Test design, metrics, results, and examples of failure modes, with enough detail for reviewers to understand what was and was not tested.
- Evidence addressing validity, reliability, fitness for purpose, and performance in relevant operating conditions.
- Where appropriate, assessment of security, resilience, privacy, fairness, and explainability, with a clear account of which issues were evaluated and how.
NIST’s AI RMF Core calls for testing before deployment and at regular intervals in operation, with documented, context-sensitive evidence on matters such as validity, reliability, security, resilience, privacy, fairness, and explainability. It is a lifecycle framework, not a product pass mark. Explore the NIST AI RMF Core.
Apply extra scrutiny to generative AI
For a generative system, test the actual task and workflow rather than assuming a general-purpose benchmark predicts performance in your domain. NIST’s Generative AI Profile warns that available pre-deployment testing may be inadequate, inconsistently applied, or mismatched to deployment context; anecdotal testing or tests designed for humans do not guarantee validity or reliability for a particular domain. Read NIST AI 600-1.
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Assess the provider, dependencies, and changes
Limited access to proprietary code, data, or methods does not remove the need to validate a vendor product. The Federal Reserve’s 2026 guidance notes that customized vendor and other third-party products can create distinct challenges for validation and other model risk management activities. Ask whether the provider can explain conceptual soundness, design, development data, output interpretation, limitations, and change history well enough for your team to make a meaningful assessment. Also require ongoing outcome analysis for accuracy, fitness for purpose, and reliability. See the guidance on vendor products and validation.
For generative AI services and integrations, include diligence on input data handling, privacy, intellectual property, information security, subcontractors, and system components. Ask how updates and material changes are disclosed, what happens to your data, and what evidence can be reviewed if source code or training data cannot be provided. NIST identifies procurement diligence, software bills of materials, service-level agreements, and attestation reports as possible tools for transparency and third-party risk management; none of those artifacts alone proves that a system is safe or suitable. Consult NIST’s Generative AI Profile.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare candidates on decision-relevant criteria
If multiple tools are genuinely suitable for the same task, compare them using the same evidence standard. Do not turn the comparison into a single vendor score unless your organization has defined and justified how the measures should be weighted; the cited guidance does not establish a universal pass mark or vendor ranking.
Best Value
| Evaluation area | Questions to resolve |
|---|---|
| Task and context fit | Does the system address the defined task with the proposed data, workflow, users, and deployment conditions? |
| Performance and robustness | Do representative tests support validity and reliability? What happens when data, products, exposures, clients, or market conditions change? |
| Interpretation and challenge | Can users understand the output’s limits, question it, and obtain an appropriate review or override? |
| Fairness and affected parties | Where people or groups may be affected, what bias or fairness concerns have been assessed and how can a result be challenged? |
| Security, privacy, and resilience | How is sensitive information handled, what security and resilience evidence is available, and what is the fallback if the service fails? |
| Provider and lifecycle controls | Can the provider explain data provenance, dependencies, material changes, incident handling, and exit or contingency options? |
| Operational burden | What staffing, review, monitoring, escalation, and maintenance will be needed throughout the system’s use? |
Make a documented decision and define conditions of use
Approval should reflect both the evidence and the risks that remain. Record the decision, evidence reviewed, unresolved limitations, accountable owners, allowed uses, required human controls, monitoring arrangements, escalation triggers, and the rationale for approval, restriction, or rejection. Make conditions specific enough that users and operators can tell when the system is being used outside its approved purpose.
NIST describes its AI Risk Management Framework as intended for voluntary use; it is not a mandatory certification or a substitute for applicable law. NIST also says the framework is being revised, so check the current framework status when adopting it. Check NIST’s AI RMF status.
Monitor the system after it goes live
Deployment is not the end of evaluation. Set a monitoring cadence and assign responsibility for reviewing outcomes, changes, and incidents. The cadence and thresholds should reflect the use case’s materiality and risk tolerance; the sources do not establish one universal interval or numerical trigger.
- Track performance and whether outputs remain fit for the approved purpose.
- Watch for changes in data relevance, products, exposures, activities, clients, and market conditions that could undermine earlier evidence.
- Review provider updates, changes in inputs or dependencies, and incidents that may affect system behavior.
- Provide a route for users to raise concerns or challenge outputs, and define how those issues are assessed and resolved.
- Set escalation triggers and response options in advance, including adding a control or overlay, adjusting or redeveloping the system, restricting use, or suspending or retiring it.
NIST’s AI RMF frames risk management as ongoing prioritization, response, recovery, communication, and improvement, while the Federal Reserve guidance emphasizes monitoring for changes that can affect model performance. NIST AI RMF Core · Federal Reserve model risk guidance.
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