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Start with the use case, not the vendor’s policy page
Whether an AI system is suitable depends on its intended purpose, where and how it will be deployed, who may be affected, and foreseeable use or misuse. A vendor-wide safety statement cannot establish that a particular product is appropriate for your workflow. NIST’s voluntary AI Risk Management Framework (AI RMF) guidance and the OECD AI Principles both support context-sensitive, lifecycle risk management.
Before sending a questionnaire, write down your own use case and boundaries. Record the decision the system will inform or make, the people affected, the jurisdictions involved, whether outputs will be reviewed by a person, and what could happen if the system is wrong, unavailable, or misused. Include intended uses as well as plausible misuse and deployment conditions that differ from the vendor’s typical setup.
Identify the exact system under review
Ask the vendor to identify the product, model and version, intended and excluded uses, deployment architecture, key dependencies, and relevant data flows. Clarify whether the vendor operates the system, supplies a model or API, or provides another component. Record which party configures, hosts, updates and controls each part. These are practical due-diligence questions, not a universal questionnaire prescribed by NIST or OECD.
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Keep the buyer’s proposed configuration and operating context alongside the vendor’s description. A model-level evaluation may not cover a product’s added features, customer-specific settings, connected tools, or the way your staff will use its outputs.
Map risk ownership and accountability
Ask who is accountable for identifying and managing each material risk, both at the vendor and in your organization. OECD guidance emphasizes accountability according to actors’ roles, context and ability to act; risks can cross organizational boundaries, so a contract or general policy should not leave ownership implicit. See the OECD Recommendation of the Council on Artificial Intelligence and its 2026 Due Diligence Guidance for Responsible AI.
- Named owners: Who owns risk assessment, security, privacy, product changes, incident response and customer escalation?
- Decision rights: Who can approve a deployment, impose restrictions, pause service, roll back a change or retire the system?
- Review cadence and triggers: How often are risks reviewed, and what events trigger an additional review—for example, a material system change or a new use?
- Cross-party escalation: How are issues involving a vendor component, customer configuration and downstream use assigned and resolved?
Request the vendor’s risk-assessment method, a relevant assessment or summary, review cadence, and escalation path. Confirm that the named contacts and contractual processes can reach people with authority to act, not only a general support queue.
Examine testing, results and known limits
Ask what evaluations were performed and what they actually covered. A useful response identifies the tested model or product version, the evaluated tasks and conditions, the evaluation method, material findings, and mitigations. Ask which important failure modes remain and whether the evidence is relevant to your intended use. A broad claim such as “tested for safety” is not enough to determine what was tested or what the results mean for your deployment.
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- Ask what failure modes or harmful outcomes were found, and what mitigations were made or are still planned.
- Find out whether evaluations covered the version and configuration you will use, and what changes would make the evidence stale.
- For generative AI, ask about relevant testing for risks associated with generative capabilities. NIST’s Generative AI Profile (NIST-AI-600-1), released July 26, 2024, is a companion resource for identifying risks and selecting risk-management actions.
For higher-impact uses, consider whether an evaluation’s scope, independence and recency are adequate for the consequences of failure. Do not treat a test result as a guarantee: NIST notes that trustworthiness characteristics involve tradeoffs, and that considering them individually does not ensure overall trustworthiness. Its AI RMF materials describe a voluntary framework, not a product approval or proof that a particular system is safe for your use.
Check transparency and documentation for downstream users
Ask for documentation that helps your team understand the system’s capabilities, limitations, intended uses, dependencies and operating assumptions. Determine whether your staff can recognize when an output needs review, what information must be provided to users, and what your organization needs to document about its own configuration and use.
If the vendor supplies a general-purpose AI model or another component that your organization will incorporate into a system, ask what technical and use information is available to downstream parties. The European Commission’s guidance on obligations for general-purpose AI providers describes information duties intended to help downstream providers understand the model and meet their own obligations. The applicability of a particular duty depends on the system and the parties’ roles; do not assume that every vendor has the same obligations.
Verify monitoring, incident handling and human intervention
Pre-release testing cannot show how every deployment will perform over time. Ask how the vendor monitors performance and emerging risks after release, what it records, how issues are escalated, and how customers are told about incidents or material changes. Establish which party monitors the effects of your particular use and how findings are shared.
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- How and when will affected customers or users be notified of a material issue?
- Who can pause, roll back, repair or disable the system, and how quickly can they do so?
- What human review or override is possible, and what happens if the system or a dependent service is unavailable?
- What records can be retained to support traceability, investigation and accountability?
OECD principles call for traceability and mechanisms to override, repair or safely decommission systems where appropriate. Ask for an operational explanation of how those actions work for the system you will use, rather than relying only on a general statement of principle.
Set change and retirement controls
Approval should attach to a defined system and use, not to an indefinitely changing service. Ask how the vendor communicates changes to the model, product, dependencies, operating terms or available safeguards. Agree which changes require notice, reassessment, renewed approval or a right to suspend use. The appropriate triggers depend on your use and the significance of the change; establish them with the vendor rather than assuming every update has the same effect.
Plan for safe exit as well as deployment. Identify how your organization can disable the system, transition to a fallback process, preserve records needed for accountability, and handle data or dependent services when use ends. Where appropriate, confirm who is responsible for safely decommissioning the system if it causes undue harm or behaves undesirably.
Compare vendors by evidence, not by framework name
Use a consistent set of questions for each candidate, but do not turn the comparison into a universal pass/fail score unless your organization has defined and validated one. A vendor that provides more documents is not necessarily safer; relevance, scope and ability to act matter.
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| Comparison area | Evidence to look for | Question for your team |
|---|---|---|
| Use-case fit | Intended and excluded uses, supported configurations, known limitations | Does the evidence cover our actual purpose, users and deployment conditions? |
| Evaluation quality | Version, scope, methods, findings, mitigations and date of evaluations | Are the tests relevant and recent enough for the consequences of failure? |
| Risk clarity | Documented failure modes, dependencies and residual risks | Can we understand what can still go wrong and decide whether we can manage it? |
| Accountability | Named owners, escalation paths and intervention authority | Can the responsible people be reached, and can they take effective action? |
| Operations | Monitoring, incident, notification, rollback and disablement processes | Can we detect and respond to problems during our use? |
| Downstream information | Documentation on capabilities, limitations, use and dependencies | Can our operators and downstream users understand the system well enough to use it responsibly? |
| Applicable obligations | Role- and jurisdiction-specific analysis, with responsible owners | Have we established which duties apply to this system and each party? |
For each answer, note whether it is supported by product-specific documentation, an evaluation or operational record, or only a general commitment. Record gaps as unresolved questions or conditions for approval; do not silently treat missing evidence as proof of safety or proof of harm.
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NIST AI RMF is voluntary guidance
NIST describes the AI RMF as voluntary risk-management guidance. It addresses characteristics including validity and reliability, safety, security and resilience, accountability and transparency, explainability, privacy enhancement, and fairness with harmful bias managed. NIST also cautions that trustworthiness characteristics can involve tradeoffs and that not every characteristic matters equally in every setting. A vendor’s claim of alignment may help organize questions, but by itself it does not prove a product is suitable for your use or legally compliant. NIST identifies the framework as under revision; check its current framework page for status when relying on it.
OECD principles support lifecycle due diligence
The OECD principles call for AI systems to be robust, secure and safe throughout their lifecycle, with accountability, traceability and ongoing risk management. Its 2026 due diligence report gives enterprises involved in the AI value chain practical guidance for responsible business conduct and maps frameworks including ISO/IEC 42001 and the NIST AI RMF. Use these materials to structure due diligence, not as a substitute for evidence about the vendor’s specific system.
EU AI Act duties depend on the system and role
Do not assume that all AI vendors or customers have identical obligations. Determine whether a system and a party’s role bring a particular requirement into scope, and check the relevant jurisdiction and current implementation guidance. The European Commission’s transparency guidelines, published July 20, 2026, address Article 50 obligations that apply from August 2, 2026; its Article 50 guidance page provides related information.
Separately, Article 55 sets duties for providers of general-purpose AI models with systemic risk, including standardized evaluations, documented adversarial testing, systemic-risk mitigation, serious-incident reporting and cybersecurity. Those are not blanket duties for every AI vendor. See the European Commission AI Act Service Desk text for Article 55. For a real procurement decision, have qualified legal or compliance staff confirm which provisions apply to the system, provider/deployer roles and markets involved.
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