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Start with the business decision, not the safety label
Safety depends on how a system is used and who may be affected. A model that performs acceptably in one workflow may create unacceptable risk in another. Define the intended use and consequences of failure before deciding what evidence would count.
- Describe the job. State what the model will do, what decisions or outputs it will influence, and what it must not do.
- Map the people and information involved. Identify users, people affected by outputs, data the model receives, and any sensitive or personal information.
- Map the full system. Include prompts, retrieval sources, connected tools, permissions, integrations, human review, and how outputs reach users or downstream systems.
- Define harmful outcomes. Describe realistic errors, misuse, privacy exposures, security failures, unfair impacts, or unreliable behavior—and how severe each would be.
- Set acceptable outcomes and limits. Decide in advance which failures block launch, which require mitigation or human review, and which uses are out of scope.
This context gives meaning to a vendor’s claim. Without it, “safe,” “responsible,” and “aligned” do not tell you whether the system is suitable for your workflow.
Ask vendors for evidence you can examine
Request evidence for the precise product and configuration under consideration. A test of a different model version, or of a model without the tools and guardrails in your proposed deployment, may not answer your procurement question.
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- Which exact model and version were evaluated, and when?
- What system configuration was tested—including prompts, guardrails, retrieval, tools, permissions, and human oversight?
- Which intended uses, threat scenarios, and failure modes were covered? Which were excluded?
- What test data and methods were used, and how closely do they represent your expected users, tasks, and conditions?
- Who conducted the evaluation? Was the evaluator independent of the vendor or product team?
- What were the results for relevant tasks or affected groups, where such breakdowns matter? What uncertainty or limitations accompany the results?
- How are findings documented, updated, and communicated when the model or system changes?
Ask for meaningful results and methodology, not just confirmation that testing took place. NIST’s AI Risk Management Framework (AI RMF) says accuracy measurements should be paired with realistic, representative test sets and details of the test methodology in associated documentation. A claim without those details is difficult to interpret or reproduce.
Run evaluations that reflect your use case
Vendor evidence is one input; it does not replace testing in your own business context. Create a test set from realistic examples and foreseeable failure conditions, then use the same tasks and scoring rules for each candidate.
- Include ordinary cases, edge cases, ambiguous requests, and foreseeable misuse.
- Test privacy-sensitive scenarios and adversarial prompts relevant to the application.
- Check how the system behaves when information is incomplete, contradictory, or outside its intended scope.
- Test the complete configuration, including connected data and tools, permissions, and escalation to a person.
- Review failures with domain experts and, when appropriate, people likely to be affected by the system.
- Record uncertainty and failure severity as well as task performance; an average score can hide consequential failures.
Define success criteria and severity thresholds before reviewing results. NIST’s Generative AI Profile calls for empirical validation of capability claims and sharing pre-deployment testing results with relevant actors. Treat pre-deployment evaluation as evidence for a launch decision, not as proof that later operation will remain safe.
Compare candidates on the same decision criteria
Use common tasks, test conditions, and scoring rules across the models you are considering. Weight the criteria according to the use case and the impact of failure; a low-impact drafting assistant and a system influencing consequential decisions need not have the same approval bar.
Rank #3
| Criterion | Evidence to compare |
|---|---|
| Task performance and reliability | Results on representative business tasks, behavior on edge cases, and uncertainty or failure severity. |
| Safety and security | Results for relevant misuse, adversarial, and harmful-output scenarios; configuration and limitations of the tests. |
| Privacy and data handling | How the proposed system handles the data in your workflow and what controls or limits apply. |
| Fairness and impact | Relevant performance or impact differences across tasks or affected groups, where appropriate to the use case. |
| Transparency and accountability | Documentation of intended use, limitations, evaluation methods, ownership, and escalation paths. |
| Human review and recovery | Where a person can review, correct, or stop outputs, and how serious failures are handled. |
| Monitoring and change control | How performance and incidents are monitored, and how model or system changes are communicated and retested. |
Do not treat one public benchmark or aggregate score as a verdict. It may offer useful evidence about a measured capability, but its value depends on what it tests, how it was measured, and whether its conditions match your deployment. There is no directly comparable published numeric measure in the cited authoritative materials for the safety of a business model or the accuracy of vendor safety claims.
Make a documented decision—and keep it current
Record the decision so someone else can understand what was assessed and why the use was approved, limited, or rejected. Include the assumptions, evidence, unresolved limitations, thresholds, accountable owners, and mitigation plans.
Rank #4
Choose an outcome that fits the evidence: proceed, restrict the use case, add human oversight or other mitigations, or reject the candidate. Set retesting triggers, including a model-version change, new data or tools, a material incident, or a change in the business context. Continue monitoring after launch; pre-deployment results cannot establish how a system will behave as its configuration, users, or operating conditions change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What frameworks and standards can—and cannot—tell you
Frameworks and standards can help organize governance and risk management. They do not certify that a particular model is safe or establish that it fits your workflow.
| Resource | What it provides | What it does not establish |
|---|---|---|
| NIST AI RMF 1.0 | Voluntary, use-case-agnostic guidance for managing AI risks across the lifecycle. | Product certification or evidence that a particular model is fit for your use. NIST says version 1.0 is being revised; check NIST’s official resources for current status. |
| NIST AI RMF Generative AI Profile (NIST-AI-600-1) | A 2024 profile with generative-AI-specific risk actions, including empirical validation of capability claims. | A pass/fail determination for a model or deployment. |
| NIST AI RMF Playbook | Suggested actions organized under Govern, Map, Measure, and Manage. | A mandatory checklist: NIST describes the Playbook as voluntary and says it need not be applied in full. |
| ISO/IEC 42001:2023 | An AI management-system standard. | Technical evidence about the performance or safety of an individual model. Management-system conformity and model-specific evaluation address different questions. |
Check the current framework or standard status and any obligations that apply in your jurisdiction at procurement time. NIST’s AI RMF is voluntary guidance, not a substitute for applicable legal requirements or the evaluation of the system you intend to deploy.
A practical vendor-question checklist
- Can you provide evaluation results for the exact model version and configuration we would use?
- Which realistic use conditions, failure scenarios, and affected groups were included, and what was left out?
- Can we review the test method, limitations, and uncertainty—not only an aggregate score?
- How will you notify us about relevant model or system changes, and what retesting or monitoring do you support?
- What controls let us restrict use, escalate to a person, or respond to a serious incident?
If answers remain broad, treat the corresponding claim as unverified for your use case. Seek additional evidence, narrow the deployment, add safeguards, or defer the decision rather than assuming a general safety statement settles it.
Quick Recap
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