Evaluate an AI vendor against the system, version, deployment and people involved in your intended use—not against a policy page or framework badge. Ask for evidence across the system’s lifecycle, establish who controls each risk, and decide whether the remaining risks are acceptable for your context.
What exactly are you evaluating?
Start by defining the system and the use you are considering. A vendor may supply a model, a hosted application, an API or only one component in a larger workflow. The buyer may configure, connect or deploy it in ways the vendor does not control. Those differences affect both the evidence you need and who can address a risk.
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Record the following before reviewing claims:
- System identity: product, model and version, relevant settings, and major dependencies.
- Intended use: the task the system will perform, how its output will be used, and whether a person reviews or acts on it.
- Deployment: architecture, data flows, integrations, access controls, and which party manages each component.
- People and consequences: who is affected, how errors or misuse could affect them, and what recourse they have.
- Boundaries: the vendor’s supported and excluded uses, your foreseeable uses, and plausible misuse or unexpected conditions.
- Jurisdiction and roles: where the system will be offered or used, and which organizations act as provider, deployer, customer, or other value-chain participants.
This is a practical way to apply the lifecycle and role-sensitive approach in the NIST AI Risk Management Framework FAQs and the OECD AI Principles; neither source prescribes one universal vendor questionnaire.
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Ask for material that is specific enough to connect a claim to the system and use under review. A high-level policy can explain a vendor’s commitments, but it cannot by itself show how a particular product was assessed, what it can fail at, or how problems are handled after release.
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| Area | Ask for | Check whether the evidence identifies |
|---|---|---|
| Risk assessment | The assessment method, findings, mitigations and residual risks. | The product and version, use cases, affected groups, assumptions, owner and review date. |
| Design and development | Relevant design controls, data and dependency information, and the way safety, security, privacy and fairness concerns are addressed. | Which controls apply to the system being procured, who operates them, and what limitations remain. |
| Evaluation | Evaluation scope, methods, results, important failure modes and follow-up mitigations. | Versions and scenarios tested, coverage and gaps, and whether results relate to your intended use. |
| Documentation | Descriptions of capabilities, limitations, intended uses and relevant instructions for downstream users. | Whether your team can understand the system well enough to use it responsibly and meet its own obligations. |
| Operations | Monitoring approach, incident records or process, escalation routes and intervention options. | Who watches for which risks, what triggers action, and who can pause, roll back, repair or disable the system. |
| Change and retirement | Change-notification and change-control practices, plus procedures for withdrawal or decommissioning. | How changes to models, data, integrations or use conditions are assessed, and what happens when the system is no longer suitable. |
For every item, note whether the vendor supplied evidence, described a process without supporting evidence, or could not answer. Also record the evidence’s date, scope and owner. A document about a different model version or a broad corporate process may be useful background, but it is not equivalent to evidence about the product and deployment you are assessing.
How should you review risk ownership and governance?
Ask the vendor to identify an accountable owner for relevant risks, the escalation route, how often the risk assessment is reviewed, and what events trigger a fresh review. Change triggers might include a model or feature update, a new integration, a shift in intended use, or a newly recognized failure mode. Treat these as procurement questions, not as a universal checklist mandated by a framework.
Then map responsibilities across organizations. For each important control, write down who can perform it, who supplies necessary information, and who has authority to act. Ask how the vendor works with customers and other participants when a risk crosses organizational boundaries. The OECD Recommendation of the Council on Artificial Intelligence emphasizes accountability and traceability, with responsibilities shaped by role and context. Its 2026 Due Diligence Guidance for Responsible AI offers practical guidance for enterprises involved in the AI value chain.
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A useful governance answer names people or roles, procedures, decision rights and escalation paths. “We follow responsible AI principles” is a commitment, not an explanation of who will act when a control fails.
How do you assess testing and known limitations?
Ask what evaluations were performed, what they were intended to establish, which versions and use cases they covered, what important limitations or failure modes emerged, and what the vendor did in response. Find out whether testing reflects the conditions of your deployment, including the languages, populations, inputs, integrations and human review steps that matter to your use. Do not treat a result from one evaluation as proof of safety in every setting.
For generative AI, the NIST Generative Artificial Intelligence Profile (NIST-AI-600-1), released on July 26, 2024, is a companion resource for identifying generative AI risks and selecting risk-management actions. It can help structure questions; its existence does not certify a particular vendor or product.
Under the EU AI Act, providers of general-purpose AI models with systemic risk have separate duties, including standardized evaluations, adversarial testing that is conducted and documented, systemic-risk mitigation, serious-incident reporting and cybersecurity. Whether a particular provider or model falls within those provisions is a legal-scope question; do not assume every AI vendor has the same duties. See the European Commission’s Article 55 text and its guidelines for general-purpose AI providers.
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Can your team operate and intervene in the system?
Pre-release evidence is only part of the review. Ask how the vendor monitors performance and risks after release, what is logged, which incidents are recorded and escalated, and how affected users are notified when appropriate. Clarify what signals trigger investigation and whether the vendor will share information needed for your own monitoring.
Establish practical intervention options before deployment. Depending on the system and use, your team may need the ability to suspend a workflow, route cases to a person, roll back a change, disable an integration or stop using the service. Clarify who can take each action, how quickly it can happen, what support the vendor provides, and how service can be restored safely. OECD principles call for lifecycle traceability and mechanisms to override, repair or safely decommission systems where appropriate; they do not mean every system needs the same intervention design.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which framework or legal obligations apply?
NIST AI Risk Management Framework
NIST describes its AI RMF as voluntary guidance for managing AI risks. It covers trustworthiness characteristics including validity and reliability, safety, security and resilience, accountability and transparency, explainability, privacy enhancement, and fairness with harmful bias managed. Use it to organize due diligence, not as a product approval or proof of legal compliance. NIST cautions that considering characteristics one by one does not automatically make a system trustworthy: trade-offs and context matter. Check the NIST AI RMF page for the framework and its current status; NIST identifies it as under revision.
OECD guidance
The OECD AI Principles emphasize safety across the lifecycle, accountability, traceability and ongoing risk management. The OECD’s 2026 Due Diligence Guidance report maps relevant standards and frameworks, including ISO/IEC 42001 and the NIST AI RMF. A vendor’s reference to a framework is a starting point for asking what practices and evidence apply to the specific system.
EU AI Act transparency duties
Do not infer a vendor’s obligations from the fact that it sells AI. Determine which system and organizational roles are in scope, where the system is used, and which provision applies. The European Commission’s transparency guidelines, published July 20, 2026, address Article 50 duties. The Commission says Article 50 transparency obligations apply from August 2, 2026. Consult its Article 50 guidance and obtain legal advice for the particular deployment rather than treating this summary as a determination of applicability.
Best Value
How should you compare vendors and make a decision?
Use the same use-case assumptions and evidence requests for each option. Compare the substance and relevance of the answers, not the length of a policy or the number of framework names in a presentation. These comparison dimensions are a practical synthesis of lifecycle risk management and role-based accountability, not an official universal scoring rubric.
- Use-case fit: Does the vendor’s stated purpose and supported-use guidance cover your intended use?
- Evaluation quality: Are the tests relevant to your use, sufficiently clear about scope and limitations, and recent enough for the version under review?
- Transparency: Does the vendor explain capabilities, dependencies, known limits and residual risks in terms your teams can use?
- Responsibility: Are accountable owners, escalation routes and decision rights clear across vendor and customer?
- Operational readiness: Are monitoring, incident handling, notification and intervention arrangements workable in your deployment?
- Change management: Will you learn about material changes, and can the parties reassess risk or stop use if the system changes or proves unsuitable?
- Legal scope: Has your organization checked applicable requirements for the system, roles, use and jurisdictions rather than relying on a general compliance claim?
Decide against your organization’s risk tolerance and the consequences of failure. Proceed only when material risks have owners and workable controls; set conditions or restrict the use where evidence or mitigations are incomplete; and reject or pause a deployment when serious risks remain unaddressed or the vendor cannot provide information essential to safe operation. Record the rationale, assumptions, accepted residual risks, approval owner and conditions that would trigger reassessment. Revisit the decision when the system, use, affected population or applicable requirements change.
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