Before adopting an AI model, assess whether the provider can show that its safety practices address your specific use, affected people, and deployment conditions. Define the harms you need to prevent, examine evidence for the exact model and service, verify who handles risks and incidents, and agree on monitoring and reassessment before launch. A framework, model card, or certification can inform that review; none guarantees that a model is safe for every application.
Start with your use case and the harms that matter
Assess the model in the setting where you plan to use it—not as an abstract product. The same model may pose different risks depending on its users, the people affected by its outputs, the decisions it influences, and how it is integrated into software, data, tools, and human workflows.
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Write down the intended task, who will use the system, who may be affected, expected operating conditions, and the consequences of an incorrect, misleading, biased, or misused output. Consider both how likely a harm is and how severe it could be. NIST’s AI Risk Management Framework (AI RMF) calls this kind of context and impact work part of its Map function; it can inform an initial go/no-go decision.
Set a risk tolerance before reviewing vendors. Specify which outputs require human review, when use must be restricted, what fail-safe behavior is acceptable, and what conditions would rule out adoption. This gives you concrete criteria against which to assess provider evidence.
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Request evidence for the exact model and service
Ask the provider for documentation tied to the model version, product configuration, and service you are considering. Broad statements such as “tested for safety” are not enough to establish what was tested, how it was tested, or whether the findings apply to your use.
- Scope and limitations: Intended and excluded uses, known limitations, and conditions outside the evaluation scope.
- Methods and results: Evaluation methods, test-set descriptions, metrics, tools, benchmark comparisons where relevant, and uncertainty or caveats where available.
- Context fit: Results relevant to your intended use, operating conditions, and affected groups, together with an explanation of how closely test conditions resemble deployment.
- Risk coverage: Evaluations addressing the mapped risks, including relevant safety, security, privacy, reliability, robustness, and fairness or bias concerns.
- Version and change history: The evaluated model version and date, changes since evaluation, and the events or changes that trigger reassessment.
- Reviewers: Whether evaluation included independent reviewers, domain experts, users, or affected groups where appropriate.
NIST’s AI RMF calls for documented test sets, metrics, and tools; testing under conditions similar to deployment; and documentation of trustworthiness characteristics such as security, privacy, and fairness. It also notes that independent review can help mitigate internal bias or conflicts of interest. Ask for enough detail to judge the evidence, not just a summary claim.
Examine governance and accountability
Safety depends on operating practices as well as model behavior. Find out who within the provider owns risk decisions, who can pause or change the service, and how concerns move from identification to action.
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- Are responsibilities and escalation routes documented?
- Does the provider maintain risk and impact records and defined processes for identifying incidents?
- Can the provider explain how it shares relevant incident information and receives feedback from external parties?
- How does it manage risks in third-party software, data, and other supply-chain components?
- What happens if the provider or an upstream supplier finds a serious defect?
Look for evidence of workable processes rather than relying on policy language alone. The answers should make clear who acts, what triggers action, and how the provider communicates material changes or incidents to customers.
Agree on monitoring and response before deployment
Pre-deployment results cannot establish how a system will behave in every live setting. NIST’s AI RMF Core says, “AI systems should be tested before their deployment and regularly while in operation.” Treat ongoing monitoring and response as part of procurement, not as an afterthought.
Before launch, agree with the provider on the operational details that matter to your use:
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- What safety or performance signals will be monitored, and who can access them?
- How can users report problems, and how are reports triaged and escalated?
- How will the provider notify you about incidents, model updates, or material changes?
- What events trigger a new assessment, such as a version change, newly identified risk, or change in use?
- Can your organization suspend use, roll back a version, or route cases to human review?
- How will you track risks and gather feedback from users and affected people after launch?
Make ownership, notification, and response expectations explicit. If you cannot pause or limit use when conditions change, account for that constraint in your risk decision.
Use model cards, standards, and regulatory documentation as inputs
Model cards
A model card can help you understand a model’s intended use, evaluation methods, performance characteristics, and differences across conditions or groups. The original model-cards paper proposed this reporting approach. Treat a card as a starting point: it may not answer questions about your integrations, workflows, or local operating conditions.
ISO/IEC 42001
ISO/IEC 42001:2023 is an organizational AI management-system standard; ISO lists its publication date as December 2023. It concerns policies and processes for AI governance and managing AI-related risks and opportunities across an organization. A provider’s relevant certification or implementation evidence can indicate management practices, but it is not, by itself, a test showing that a particular model is appropriate for your application.
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NIST AI RMF
NIST’s AI RMF 1.0 is voluntary guidance for managing AI risks and trustworthiness across design, development, use, and evaluation. Its companion Playbook is also voluntary and organizes suggested actions around Govern, Map, Measure, and Manage. NIST released a Generative AI Profile on July 26, 2024, to help identify risks and actions specific to generative AI. These resources can structure your review; they are not certifications or universal safety guarantees.
European Union provider documentation
The European Commission identifies documentation routes for covered general-purpose AI providers, including safety and security framework or model reports and serious-incident reporting. Its provider-guidance page was last updated April 28, 2026. Whether a requirement applies depends on the provider and model’s legal status and the relevant jurisdiction; check scope rather than assuming the same duties apply to every AI company.
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If you are considering more than one provider, use a consistent set of questions tied to your use case. The following comparison axes are a practical synthesis, not a published scoring system.
| Comparison axis | What to examine | Evidence to request |
|---|---|---|
| Evidence quality | Whether claims are specific to the model version and relevant to your conditions, and whether methods, limitations, uncertainty, and independent scrutiny are clear. | Evaluation methods, test-set descriptions, results, dates, limitations, and reviewer roles. |
| Risk coverage | Whether evaluations address the safety, security, privacy, fairness, reliability, and misuse risks you identified. | Risk-specific evaluation documentation and explanations of gaps or out-of-scope conditions. |
| Governance | Whether accountability, escalation authority, risk records, supplier controls, and feedback mechanisms are defined. | Named responsibilities, incident procedures, supply-chain controls, and external feedback routes. |
| Operational assurance | Whether the provider can monitor, respond to incidents, manage changes, and support reassessment or stopping use. | Monitoring and notification commitments, reassessment triggers, version-change processes, and suspension or rollback options. |
| Transparency and fit | Whether the provider clearly explains intended use and limitations and can supply evidence relevant to your decision. | Use documentation, known limitations, and responses to your deployment-specific questions. |
Compare the quality and relevance of the evidence, not the volume of documents or the presence of a familiar framework name. A gap that matters to your use should remain visible in the decision rather than being treated as resolved by a general assurance.
Make an adoption decision with explicit conditions
Record how the available evidence compares with the harms and thresholds you defined. A decision can be to proceed, proceed only with safeguards, defer until evidence or controls improve, or reject the use. Document the reasons, unresolved risks, and who accepts them.
Where adoption depends on safeguards, specify them before launch—for example, human review for consequential outputs, restricted access, a fallback process, or a defined reassessment trigger. If evidence does not cover a material risk, do not interpret the absence of a reported problem as proof that the risk is absent.
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