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Before trusting an AI tool, decide what you will use it for, what could go wrong, and whether its data practices, security and performance are acceptable for that use. Then test it with representative examples, set limits on what it may do, and document when you will review the decision. A tool that is fine for brainstorming may be unsuitable for handling sensitive information or influencing a consequential decision.
What does “safe and trustworthy” mean for an AI tool?
Trustworthiness is not a single score or feature. NIST identifies characteristics including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy, and fairness. Which matter most depends on the tool’s context and potential impact; addressing each characteristic separately does not automatically make a system trustworthy. NIST’s voluntary AI Risk Management Framework (AI RMF) offers a way to think about risks across design, deployment, use and evaluation.
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For generative AI, NIST’s Generative AI Profile (AI 600-1) adds guidance on risks such as privacy, information security and intellectual property when third-party services or integrations are involved. For application-security threats specific to large language model applications, OWASP’s 2026 LLM Top 10 is a complementary community-developed reference, not a substitute for evaluating the particular product and deployment.
NIST describes AI RMF 1.0 as voluntary and says it is being revised; the Generative AI Profile was published July 26, 2024. OWASP’s 2026 LLM Top 10 page is dated August 3, 2026. These are guidance resources, not certifications or guarantees that a particular tool is safe.
#1 Best Overall
How to evaluate an AI tool before using it
1. Define the task and the cost of failure
Write down what the tool will do, who will use it, what information it will receive, and what decisions or actions may follow from its output. Consider the consequences if it is wrong, biased, unsafe, unavailable or manipulated. A low-stakes writing aid does not need the same scrutiny as a system whose output could affect someone’s finances, health, employment or access to services.
Also identify the boundaries of the task: what the AI may suggest, what a person must verify, and what the tool must not decide or do. The higher the stakes and the harder an error is to reverse, the stronger the evidence and oversight you should require.
2. Understand what happens to your data
Read the vendor’s current privacy terms and settings before entering information. Find out what the service collects, how long it retains prompts, uploaded files and outputs, whether it may use them to improve or train models, and how deletion works. Check whether retention can be disabled and which subprocessors or other third parties may receive the data.
Do not submit confidential, personal, regulated or otherwise sensitive information until you understand these conditions and know that its use is permitted by the relevant policy. A setting that limits model training, for example, does not by itself answer questions about retention, access by service providers or deletion.
3. Look for security and accountability evidence
Identify who operates the service and look for current security documentation, access-control details, incident-response information and an explanation of important model or service dependencies. Consider whether the vendor describes how it will notify customers about incidents and provide support if something goes wrong.
For organizational procurement, request evidence proportionate to the risks and stakes. Depending on the service, that may include a software bill of materials (SBOM), security assurance reports, service-level agreements (SLAs), contractual rights to evaluate the service, incident-notification commitments and documented response processes. NIST presents these as examples of due diligence and transparency measures, not requirements that every individual user must obtain.
Rank #3
4. Test the task you will actually use it for
Try a small, representative set of examples in the intended setting before relying on the tool. Include ordinary inputs, edge cases and foreseeable misuse. Check factual claims against trusted references; vary prompts slightly to see whether answers change unexpectedly; and examine whether refusals and safety behavior are appropriate for the task.
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NIST recommends iterative, documented testing, evaluation, validation and verification (TEVV). A polished demonstration or a benchmark result is not enough to establish that a tool will work reliably in your setting: tests designed for another context may not transfer, and benchmark performance may not predict real-world behavior.
Rank #4
5. Set limits, oversight and a fallback
Decide which inputs are allowed, which outputs need human review, when users should be told AI was involved, and when a case must be escalated to a person. Set a fallback for service outages or answers that cannot be verified. For consequential uses, do not treat an AI response as the final decision-maker unless the use has been specifically assessed and authorized for that role.
Keep the evaluation record practical: note the tool and version, intended users and task, evidence reviewed, test cases and failures, safeguards, approval conditions and review date. Revisit the decision if the vendor changes its data terms, model, integrations, access controls or intended use. NIST’s Generative AI Profile recommends ongoing monitoring of third-party systems as well as preparation for incidents and fallback.
How to compare two or more AI tools
Use the same task, representative inputs and evaluation criteria for each option. Compare evidence rather than relying on marketing claims or a single impressive answer.
| Comparison area | What to check |
|---|---|
| Data protection | Collection, retention, training or improvement use, deletion, and sharing with subprocessors or other third parties. |
| Security and accountability | Access controls, incident response, support, and evidence of the vendor’s security practices. |
| Task performance and limits | Accuracy on representative inputs, consistency, known failure modes, edge-case behavior and unsafe outputs. |
| Transparency and control | Understandable terms, usable settings, human oversight, and the ability to restrict or stop use. |
| Fit for the consequences | Whether remaining risks are acceptable for the task and affected people, and whether a workable fallback exists. |
There may be trade-offs: a tool can perform well on the task but offer insufficient control over data, or provide useful transparency while still producing errors that require review. Choose based on the risks that matter for your use, not a generic ranking. NIST’s framework emphasizes that trustworthiness characteristics can involve trade-offs and do not apply equally in every setting.
When to use formal guidance—and what it cannot tell you
For a broad risk-management structure, start with the NIST AI RMF and its Generative AI Profile. For application-security threat categories, consult the OWASP 2026 LLM Top 10. Use these resources to shape questions and controls; they do not establish that a particular vendor meets your requirements.
This is a practical evaluation method, not legal, compliance or certification advice. Requirements vary by use and jurisdiction, and vendor terms and controls can change. Verify the current terms for the service you plan to use and adapt the level of review to the people and decisions affected.
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