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Evaluate an AI tool against the work you plan to give it—not against broad promises or a framework name. Identify the data involved, the consequences of a mistake, and the product features you will actually use. Then check whether the provider’s current documentation connects its policy claims to that specific product and deployment, and what evidence supports them.
Start with the task, data, and consequences
Write down what the tool will do, who will use its output, and what could happen if it is wrong or exposes information. Summarizing public material is not the same risk as processing confidential records or informing a high-impact decision. Those differences should change what evidence you require and whether the tool is suitable at all.
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Make the comparison specific: name the product, feature, deployment context, and version you expect to use. A provider-wide policy may not describe every product tier, integration, or configuration. Where the policy does not clearly cover your intended setup, treat that as an unanswered question rather than an assurance.
Separate policy promises from implementation evidence
A policy tells you what a provider says it does; it does not, by itself, show how the commitment is implemented or how well it works. Look for evidence that is relevant to the claim and to your use case, such as product documentation, evaluation results, monitoring practices, incident handling information, or an independent assessment. Check the scope and date of any evidence: a test of one model or feature may not establish the behavior of another.
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For data handling, verify current provider documentation on what information is collected, how long it is retained, whether it is used for training, who it may be shared with, how deletion works, and what access controls are available. For safety and reliability, look for evaluations and mitigations relevant to your task, known limitations, failure handling, and whether a person can review consequential outputs. For accountability, check for a policy version, a responsible contact, an incident process, and notices about material changes. These are questions to verify with each provider, not assumptions about any particular service.
Use NIST’s AI RMF as a checklist, not a badge
The U.S. National Institute of Standards and Technology (NIST) describes its AI Risk Management Framework as voluntary. Its Playbook organizes guidance around four functions: Govern, Map, Measure, and Manage. Use them to structure questions, not to award a pass/fail score or infer that a vendor is certified. NIST’s framework page says AI RMF 1.0 is being revised, so check that official page for the current edition when making a decision.
- Govern: Who owns risk decisions, approves use, and responds when something goes wrong?
- Map: Which people, data, workflows, and downstream decisions are affected by the tool?
- Measure: How are system behavior and relevant risks evaluated, and what evidence is available for the product and context you will use?
- Manage: How are identified risks mitigated, monitored, and handled as the product or its use changes?
These questions help expose missing information. A provider’s general alignment with a framework does not establish that a particular tool is appropriate for your task.
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Judge trustworthiness characteristics in context
NIST identifies characteristics that include validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. The NIST AI RMF FAQ cautions that addressing characteristics one at a time does not guarantee trustworthiness; they should be considered across the AI lifecycle, and tradeoffs may arise.
For example, a tool may provide useful explanations yet still perform unreliably on your inputs; stronger data protections may affect available features; and a general fairness claim may not show how outcomes were assessed for the people affected by your use. Ask what the provider measured, for which product and conditions, and whether the evidence addresses the harms that matter in your setting. Counting policy claims is less useful than judging their scope and relevance.
For generative AI, examine risks tied to the features you use
NIST’s Generative AI Profile, published July 26, 2024, is a companion resource to AI RMF 1.0 that helps organizations identify generative AI risks and consider risk-management actions. It is guidance, not a certification of an individual service. See the NIST Generative AI Profile.
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Security questions should reflect the tool’s actual features and integrations. OWASP’s 2025 Top 10 for LLM and generative AI applications includes prompt injection, sensitive information disclosure, supply-chain risks, and data and model poisoning. The list is a risk taxonomy, not proof that a particular provider has or has not addressed any risk. Ask which risks apply, what mitigations are in place, how they are evaluated, and what limitations remain. A connected tool or workflow may create risks that do not arise when using a model only to draft text.
Compare providers on the same assumptions
Use a common task and deployment scenario for each tool. Record the answer and its source rather than relying on impressions from marketing language.
| Comparison area | What to verify |
|---|---|
| Data handling | What data is collected, retained, used for training, shared, and deletable; which controls apply to the exact product setup. |
| Safety and security | Evaluations and mitigations for relevant misuse and attack risks, including any residual limitations. |
| Reliability and limits | Evidence for performance on the task, how failures are surfaced or handled, and what human review is available. |
| Transparency and accountability | Policy version, responsible contact, incident process, and how material changes are communicated. |
| Use-case fit | Potential consequences of errors, affected people and data, and controls available to users. |
These comparison areas reflect NIST’s trustworthiness characteristics, but specific answers must come from current documentation for each provider. If a material question is unanswered, record it as unknown and decide whether the uncertainty is acceptable for the task. For high-consequence uses, an unanswered question about data exposure, oversight, or failure handling may be enough reason not to proceed.
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