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Evaluate an AI tool against the risks of the work you plan to give it—not against a universal “safe” label. Identify the task, the information involved, who could be affected by mistakes, and whether the tool can take actions. Then compare providers using the same use case, product configuration, and plan, looking for evidence of how safeguards were tested and maintained.
What should you assess before comparing AI tools?
Start by defining the intended use. A chatbot drafting low-stakes text presents different concerns from an agent that can send email, access files, change customer records, make purchases, or execute code. The more sensitive the data and the greater the potential impact of an error or action, the stronger your required controls should be.
- Task: What will the tool do, and what would a consequential mistake look like?
- Data: Will you provide public information, confidential business material, personal data, credentials, or regulated information?
- People affected: Could an error affect a customer, employee, applicant, patient, or someone else beyond the person using the tool?
- Authority: Can the tool only suggest content, or can it send, edit, delete, buy, publish, or run something?
These answers determine which safeguards are essential. A single score that treats every safety dimension as equally important can obscure the risks that matter most to your use.
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Safety is broader than whether a model refuses certain prompts. NIST’s voluntary AI Risk Management Framework (AI RMF) identifies trustworthiness characteristics including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. NIST says these considerations apply across design, development, deployment, use, and evaluation. Read the NIST AI RMF FAQs for its explanation of the characteristics and their tradeoffs.
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NIST cautions: “Addressing AI trustworthiness characteristics individually will not ensure AI system trustworthiness; tradeoffs are often involved, rarely do all characteristics apply in every setting, and some will be more or less important in any given situation.” The framework is guidance, not a product certification or proof that a particular tool is safe.
How can you evaluate the evidence behind safeguards?
A feature name or demonstration is not enough. Ask what the provider tested, how it tested it, which model and configuration were covered, what the known limitations are, and what happens when a safeguard fails. Useful evidence is specific enough to understand its scope and date.
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- Evaluation scope: Which tasks, risks, languages, user groups, and product configurations were included?
- Methods: Were tests repeatable? Did they use representative inputs, adversarial testing, human review, or external evaluators?
- Limitations: What was not tested, and what failure modes remain?
- Monitoring and response: How are incidents reported, investigated, and used to update safeguards?
- Change management: Does the provider explain when models or safety controls change and whether they are re-evaluated?
NIST’s AI Resource Center supports operationalizing the AI RMF and provides resources for testing, evaluation, verification, and validation. Its listed November 2025 ARIA 0.1 pilot report combines expert annotators and human testers; that is an example of evaluation work, not a single test method that establishes safety for every tool. Explore the NIST AI Resource Center.
Provider-reported results need careful interpretation. OpenAI’s 2025 Operator system card reports a 1% success rate on its biorisk tooling evaluation set. That is a narrow result for a particular test set, not an overall safety score, a comparison with other tools, or a result that transfers to unrelated tasks. The card describes internal testing and external red teaming, while also noting that red-team constraints may not capture worst-case real-world risks. Read the Operator System Card.
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How should you check privacy and security?
Do not assume that similar-looking AI features have the same data terms. Check the current documentation for the exact product, plan, region, and settings you would use. Establish what inputs and outputs are collected, how long they are retained, whether they may be used to improve models, who can access them, and which administrative or contractual controls are available.
- Confirm retention periods and whether retention can be changed.
- Check whether data use for model improvement can be controlled and whether the setting applies to your plan.
- Review account roles, access logging, API controls, and any enterprise administration options you need.
- For connected services, determine what data and actions an integration can reach, and how those connections are protected.
- Check the provider’s security documentation, vulnerability response process, and stated boundaries for connected systems.
Google’s Secure AI Framework (SAIF) is one example of a framework for integrating security and privacy measures into machine-learning-powered applications. Its guidance starts with understanding the use, assembling a cross-functional team, and learning the model lifecycle before applying its six core elements. It is not a universal certification or proof that a specific product is safe. See Google’s SAIF guidance.
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What should you check when an AI tool can take actions?
For an agent or integration with access to external systems, examine the permission boundary separately from the model’s answers. A useful control makes clear what the tool is allowed to do, shows proposed consequential actions, and gives a person a meaningful chance to approve or stop them.
- Can you grant only the minimum access needed for the task?
- Does the tool ask for confirmation before sending messages, making purchases, changing records, deleting content, or taking other consequential actions?
- Can you review an action before it happens and see an audit trail afterward?
- Can you stop a running task, revoke access, or recover from an unwanted change?
OpenAI’s Operator system card describes human oversight at key steps, explicit confirmation for certain actions such as financial transactions and sending emails, and restrictions on some tasks. These are documented controls for that system at the card’s date—not evidence that all AI products have equivalent controls or that the controls eliminate risk.
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How do you compare tools without a misleading score?
Use a consistent use case and configuration, then set minimum requirements for the risks that matter. A confidential-data workflow might make retention and access controls non-negotiable; an agent that edits external systems might require granular permissions, approval before consequential actions, and useful logs. Compare other differences only after each candidate meets your minimums.
| Evaluation area | Questions to ask | Evidence to look for |
|---|---|---|
| Use-case fit and reliability | Which tasks and limitations are documented? How is performance evaluated on representative inputs? | Task-specific methods, model or version and date, stated limitations, and a safe way to test representative cases. |
| Harmful-output safeguards | Which categories of harmful requests or outputs are addressed? How can failures be reported? | A clear policy and reporting process, plus documented evaluation scope—not just refusal examples. |
| Privacy and data handling | What is retained, for how long, for what purposes, and accessible to whom? Which settings or contract terms apply? | Current product- and plan-specific documentation, including configurable retention or model-improvement choices where offered. |
| Security | How are accounts, data, APIs, integrations, and application components protected? | Security documentation, access controls, vulnerability response, and clear boundaries for connected systems. |
| Human control and permissions | Can the tool act on external systems? Which actions require review or confirmation? | Granular permissions, visible proposed actions, approval for consequential actions, and ways to stop or reverse them. |
| Transparency and accountability | Can the provider explain intended use, limitations, evaluation, and incident reporting? | Dated system documentation and named accountability or reporting paths. |
| Fairness and affected people | Who may be affected by errors or uneven performance? Are relevant groups and contexts represented in evaluation? | Evaluation tailored to affected populations and a plan for addressing disparate outcomes. |
| Ongoing evaluation | How often are models and safeguards re-evaluated after changes? What happens when a test fails or a new risk appears? | Lifecycle monitoring and documented change management. |
Use the table to identify evidence gaps and fit, not to award an undifferentiated safety score. An axis that is decisive for one use case may be less relevant to another.
How do you verify that the documentation applies to your setup?
AI product terms and controls can change with the model, product tier, region, settings, and integrations. Check the documentation date and verify that it describes the configuration you will actually use. NIST released AI RMF 1.0 on January 26, 2023; its framework page says the framework is being revised and notes that the Generative AI Profile was released July 26, 2024. Those dates matter when relying on guidance or product documentation. Check NIST’s AI RMF page for current framework information.
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Before adoption, test with safe, representative inputs and verify the controls in the actual product rather than relying only on general marketing claims. Avoid putting sensitive material into a trial until you have confirmed the applicable data terms and access settings.
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