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Evaluate an AI tool against the task and data you actually plan to use—not the provider’s broad safety language. Treat policies as claims to verify, look for evidence of implementation, and compare tools using the same assumptions about data, users, and consequences of errors. A framework can help organize that review, but it cannot certify that a particular product is suitable.
Start with the use case and the consequences of failure
Write down what the tool will do, who will use its output, what information it will process, and what could happen if the output is wrong or exposed. Summarizing public webpages is different from processing confidential records or informing a high-impact decision. The more serious the consequences, the stronger the evidence and human oversight you should require.
Ask the provider to connect each relevant claim to the specific product, feature, deployment context, and version you intend to use. A statement about a company’s general approach may not describe a particular model, integration, or account type.
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A policy tells you what a provider says it does. Evidence helps you judge whether those practices apply to the product and are being implemented. Look for current documentation, evaluation methods and results, monitoring practices, incident handling, and independent assessments where available. A claim without relevant supporting detail is not proof of a control.
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For each tool, check current provider documentation for what data is collected, how long it is retained, whether it is used for training, whether it is shared, how deletion works, and who can access it. Also look for the policy version and date, a responsible contact, incident procedures, and notices of material changes. These details can vary by product, feature, deployment, or account; verify the terms for the configuration you will actually use.
Use NIST’s AI Risk Management Framework as a checklist, not a badge
NIST describes its AI Risk Management Framework (AI RMF) as voluntary. Its Playbook organizes risk work into four functions: Govern, Map, Measure, and Manage. Use them to structure questions, not to assign a pass/fail label to a vendor.
- Govern: Who is accountable for risk decisions, and how are responsibilities and policies maintained?
- Map: Which people, data, workflows, and decisions are affected? What harms could arise in this context?
- Measure: How does the provider evaluate system behavior, reliability, safety, security, privacy, or fairness for relevant uses?
- Manage: How are risks mitigated, monitored, escalated, and addressed when the system or its use changes?
The framework page says AI RMF 1.0 is being revised. Check the official NIST AI RMF page for its current status and edition when you evaluate a provider.
Compare evidence across trustworthiness characteristics
NIST identifies characteristics including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. These are useful lenses for comparison, but they do not matter equally in every use case and can involve tradeoffs. NIST cautions that addressing characteristics one by one does not, by itself, ensure trustworthiness.
Compare the quality and relevance of the evidence—not the number of principles or policy statements a provider lists. For example, a reliability claim is more useful when the provider explains what was evaluated and under what conditions, and how failures are handled in a workflow like yours.
For generative AI, ask about risks specific to the product
NIST’s Generative AI Profile is a companion resource to AI RMF 1.0. NIST published it on July 26, 2024, to help organizations identify generative AI risks and consider risk-management actions. It is guidance, not certification of an individual AI service. See the NIST Generative AI Profile.
For security, 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. Use the list as a risk taxonomy: ask which risks apply to the tool’s features and integrations, what mitigations exist, how they are evaluated, and what limitations remain. The list does not establish whether a specific provider has mitigated any particular risk.
Compare tools on the same criteria
Use the same task and deployment assumptions for each candidate. Record what the provider documents and what remains unclear; do not treat an unanswered question as evidence that a control is absent or present.
| Comparison area | Questions to ask |
|---|---|
| Data handling | What data is collected, retained, used for training, or shared? How can it be deleted, and who can access it? |
| Safety and security | Which relevant misuse and attack risks have been evaluated? What mitigations are described, and what residual limitations remain? |
| Reliability and limits | How does the tool perform for the task? What can it get wrong, how are failures handled, and when is human review required? |
| Transparency and accountability | Which policy version applies? Is there a responsible contact, an incident process, and notice of material changes? |
| Use-case fit | What are the consequences of errors in your workflow, and what user controls or safeguards are available? |
Make the decision proportional to risk
Choose a tool only when its documented controls and limitations fit your intended use. If an important point is unclear—such as training use, deletion, evaluation scope, or incident response—ask the provider for a specific answer and keep the response with your review. For consequential uses, consider whether the evidence is strong enough for the potential harm and whether a person can review or override outputs. Reassess when the product, policy, deployment, or task changes.
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