The Tool Desk
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Start with the deployment you are buying
Safety evidence is meaningful only in context. The same model may present different risks when used for internal drafting, customer support, hiring, or decisions that affect access to services. Before reviewing a vendor, write down the intended deployment and the consequences of a wrong or misleading output.
- System: Identify the model or product, version, configuration, and provider role. Include connected services, third-party components, and any changes your organization will make.
- Purpose and users: State what the system will do, who will use it, who may be affected, and which uses are prohibited.
- Operating conditions: Record the data and inputs it will receive, the setting and jurisdiction, human review arrangements, and any assumptions the vendor makes about how it will be used.
- Failure consequences: Describe plausible errors, who bears their effects, how severe or reversible they may be, and what happens if the system is unavailable or unreliable.
Ask the vendor to confirm this description and identify where its own assumptions differ. NIST’s AI Risk Management Framework (AI RMF) evaluation guidance emphasizes documenting scope, assumptions, data, and measures in relation to the intended deployment; its crosswalk with ISO/IEC FDIS 42001 also addresses third-party components.
What evidence should you ask an AI vendor for?
Request materials that let your team verify claims against the deployment you scoped. A policy or framework mapping can help explain the vendor’s approach, but it does not substitute for evidence about this system and use case.
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System boundaries and limitations
- A system and version description, including the vendor’s role and any relevant third-party components or connected services.
- Intended uses, prohibited uses, deployment assumptions, and known performance limits.
- Information about changes in configuration or integration that could affect the evidence the vendor provides.
Risk and impact assessments
- A risk register or equivalent assessment covering foreseeable harms, affected groups, severity and likelihood methods, and mitigations.
- An impact assessment where appropriate, with residual risks, their rationale, mitigation status, and the person or role accountable for accepting them.
- Evidence that risks are revisited when the system, deployment, or operating context changes.
NIST AI RMF organizes risk management across the lifecycle; its ISO/IEC FDIS 42001 crosswalk maps related practices such as risk and impact assessment, supplier controls, and feedback from external sources. Ask how the vendor’s process applies to your proposed deployment rather than treating the existence of an assessment as a result.
Testing and evaluation
Request the evaluation plan and enough detail to judge whether its results transfer to your use:
- Test goals, methods, test sets, metrics, measured results, and documented failure cases.
- Coverage of realistic operating conditions and relevant populations, with gaps and limitations identified.
- Adversarial testing where relevant to the system’s risks.
- Who performed the work, how evaluation was separated from front-line development, and when retesting is triggered.
NIST evaluation guidance calls for documented testing, evaluation, verification, and validation (TEVV) details and evidence from conditions similar to deployment. It says verification and validation roles ideally differ from test and evaluation roles. Treat independence as a matter of degree: ask who set the test, who judged the results, and what authority they had to report adverse findings.
Rank #2
Security, privacy, and fairness
Ask for controls and evidence tied to the risks in your deployment, not an undifferentiated list of safeguards. Relevant topics can include access controls and data protection, robustness and resilience, privacy risks, and how fairness or harmful bias is measured and addressed. Ask which groups and operating conditions were covered, what the controls do, and what important risks remain. NIST identifies these as trustworthiness characteristics; its crosswalk describes documenting measurement and controls.
Operational monitoring and change control
A pre-launch report cannot show how a system will behave after changes in inputs, users, integrations, or the model itself. Ask how the vendor will:
- Monitor performance and safety in production, record relevant events, and detect drift or failures.
- Notify you about updates or changes that may affect system behavior or the evidence you relied on.
- Decide when new assessment is needed, and track corrective actions through completion.
- Support rollback, suspension, or safe failure when the system is unreliable or a serious issue occurs.
- Detect and communicate incidents, including what information it will provide to help you investigate.
NIST evaluation guidance includes ongoing operational monitoring and recurring safety evaluation. Define reassessment triggers with the vendor before deployment; examples to discuss include material system changes, changed use conditions, or evidence of a failure relevant to your risk assessment.
Rank #3
Accountability and contract commitments
Make responsibility observable. Ask for an accountable executive and operational contacts, an escalation route, and clarity about who can accept, mitigate, or stop a risk. In contract discussions, address access to supporting evidence or audit rights, incident notification commitments, cooperation during investigations, allocation of responsibilities, and escalation of unresolved risks. The appropriate terms depend on the system, bargaining context, and applicable jurisdiction; NIST governance guidance supports clear oversight and accountability, but it does not prescribe a universal contract.
How to judge whether the evidence is strong enough
Assess the substance and relevance of each item, not the polish of the presentation. For each material risk, ask whether the vendor’s evidence answers these questions:
- Does it match the deployment? Are the model or system version, use, population, data, and operating conditions close enough to yours to make the result informative?
- Can the claim be checked? Are methods, measures, coverage, results, and limitations described, rather than only a conclusion or assurance?
- Are gaps visible? Does the vendor say what was not tested, what remains uncertain, and how residual risk is handled?
- Was challenge possible? Is there meaningful separation between development and evaluation, or other review capable of surfacing unfavorable results?
- Does evidence lead to action? Are monitoring, escalation, mitigation, and retesting connected to identified owners and decision points?
If commercial confidentiality limits disclosure, ask whether the vendor can provide a redacted report, a structured summary with methods and results, or controlled access to supporting material. A refusal to share details does not by itself establish that a system is unsafe, but it limits what your organization can verify and should affect the decision and contract protections.
Compare vendors on the same basis
Give each vendor the same deployment description and evidence request. Compare their answers against your risk assessment, weighting serious or hard-to-reverse harms more heavily than broad policy statements. A useful comparison record is:
- Evidence relevance and coverage: How closely the materials match your use, affected people, and operating conditions.
- Evaluation quality: How well methods, test coverage, results, limitations, and evaluator roles are documented.
- Use boundaries: How clearly intended uses, prohibited uses, assumptions, and performance limits are stated.
- Risk ownership and remediation: Whether accountable people can make decisions and whether mitigations have owners and follow-through.
- Operational readiness: Whether production monitoring, incident response, change notice, and reassessment are defined.
- Control evidence: Whether security, privacy, and fairness measures address the material risks of your deployment.
Record unanswered questions and unresolved risks alongside the evidence. A vendor with a polished policy but little deployment-relevant evidence may be harder to govern than one that clearly explains its limits and provides a credible process for addressing them.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use NIST and ISO as frameworks, not proof of safety
NIST describes AI RMF 1.0 as a voluntary framework intended to help manage AI risks and incorporate trustworthiness into the design, development, use, and evaluation of AI systems. Its trustworthiness characteristics include validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. NIST advises considering these across pre-design, design and development, deployment and use, and testing and evaluation.
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NIST says the AI RMF is a living document, and its current resources report that AI RMF 1.0 is being revised. Check the current edition and resources when making a procurement decision. NIST’s AI RMF FAQ says the framework is intended to help developers, users, and evaluators better manage AI risks that could affect individuals, organizations, society, or the environment.
NIST publishes a crosswalk between AI RMF and ISO/IEC FDIS 42001. A crosswalk maps overlapping practices; it does not establish that a vendor holds an ISO certification, that a particular system is safe, or that legal obligations have been met. Similarly, a framework mapping or certificate can be useful evidence about a process, but is not a guarantee of outcomes.
Check legal duties separately from vendor assurances
Legal requirements depend on the applicable law, the system, the jurisdiction, and each organization’s role. A procurer or deployer may have responsibilities that are distinct from a provider’s duties; one organization can occupy different roles for different systems. Do not treat a NIST mapping as legal compliance, and get qualified legal advice where the decision depends on the law.
EU AI Act context for general-purpose AI
The European Commission says obligations for providers of general-purpose AI (GPAI) models placed on the market after 2 August 2025 entered into application on that date. The Commission’s provider guidelines explain its interpretation and are non-binding. The Commission says actors making significant modifications may need to comply as providers, while those making minor changes do not. Determine the model’s status and each actor’s role against the current legal text and guidance rather than assuming every vendor or deployer has the same obligations.
Additional duties for systemic-risk GPAI providers
Article 55 of the EU AI Act sets specific duties for providers of GPAI models with systemic risk: perform and document standardized model evaluations, including adversarial testing; assess and mitigate systemic risks; track, document, and report serious incidents and corrective measures; and ensure adequate cybersecurity for the model and physical infrastructure. These duties are not a universal checklist for every AI vendor. The consolidated EU text cited here is dated 27 July 2026, so confirm the current legal text and guidance for a live decision.
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