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Ask an AI vendor for evidence about the system’s purpose, limits, data, testing, human controls, accountability, incident response, monitoring, changes, and exit options. Then judge that evidence against your specific deployment, affected people, legal duties, and risk tolerance. A general statement that a model is “safe” or “responsible” is not enough.
Start with your use case, not the vendor’s product description
Write down what you intend to do before the first vendor presentation. State the decision or task the system will support, who will use it, who may be affected, what data it will receive, how often outputs will be produced, and what happens when the system is wrong. Include whether a person can review an output before it causes an external consequence.
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NIST procurement guidance recommends defining the problem before prescribing an AI solution, considering non-AI alternatives, and completing an initial risk and impact assessment before purchase. Revisit that assessment at major decision points, such as a pilot, launch, model change, expansion to a new population, or a serious incident.
- Decision: What action could the output influence?
- People: Who benefits, who bears risk, and who may be unable to challenge an error?
- Operating conditions: What language, geography, accessibility needs, connectivity, staffing, and time pressure apply?
- Failure consequence: Is a mistake inconvenient, financially damaging, discriminatory, unsafe, or legally significant?
- Alternatives: Could a rules-based process, ordinary software, or human workflow solve the problem with less risk?
Give the vendor this use-case description and ask it to identify which assumptions in its claims match your environment. Evidence from a different language, population, task, or level of human review may not transfer.
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Questions about purpose, scope, and fit
What is the system designed to do?
Ask the vendor to describe the intended purpose in operational terms, not marketing language. Request a list of supported uses, prohibited uses, known edge cases, and conditions under which performance is not established. Ask why AI is appropriate for your problem and what non-AI approaches the vendor considered.
Request the risk and impact assessment that supports the proposed deployment. It should identify foreseeable harms, affected groups, dependencies, unresolved risks, and controls. Ask which assumptions are built into the assessment: user expertise, data completeness, language, workload, supervision, and the consequences of an incorrect result.
What is outside the intended scope?
Get explicit boundaries in writing. A text-generation system might be suitable for drafting but unsuitable for unsupervised legal advice. A classification tool might work for a narrow population but lack evidence for another. Ask what the vendor will do if your organization wants to expand the use later. A new purpose should trigger a fresh risk review rather than an informal assumption that the original approval still applies.
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Questions about data, privacy, and limitations
What data built and operates the system?
Ask for the sources, types, collection methods, time periods, geographic coverage, and quality controls for training, fine-tuning, retrieval, and operational data. Request documentation for third-party datasets and components, including known licensing, provenance, representativeness, and historical-bias issues. You do not necessarily need proprietary model code, but you do need enough information to judge whether the evidence fits your use.
What happens to our inputs and outputs?
Clarify whether prompts, files, feedback, outputs, telemetry, and logs are retained; whether they are used to improve a model; who can access them; and which subprocessors handle them. Ask for retention periods, deletion procedures, backup treatment, isolation between customers, encryption, access controls, and procedures for responding to a security or privacy request.
Ask whether the vendor can segregate sensitive data, disable training on your content, and prove deletion when the relationship ends. Confirm how the vendor handles data in support tickets, debugging sessions, human review queues, and automated monitoring. Put these terms in the contract rather than relying on a sales explanation.
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What limitations should users expect?
Request a plain-language list of known failure modes, including inaccurate outputs, missing information, unwanted correlations, unsafe recommendations, prompt-injection exposure, language or accessibility gaps, and performance differences among relevant groups. Ask which limitations were discovered through testing and which remain uncertain. A vendor should explain how users are warned, what safeguards reduce the risk, and when a human must take over.
Questions about safety and performance evidence
Which tests were run?
Ask for the evaluation methods, test sets, metrics, thresholds, results, model or software version, test dates, testers, and operating conditions. Request results that resemble your intended deployment: the same task, languages, data formats, user population, workload, and level of human intervention. A headline accuracy number without its denominator, test design, or failure breakdown is weak evidence.
How realistic and repeatable are the evaluations?
Ask how the vendor established that its tests represent real conditions. Request information about sampling, exclusions, missing data, confidence limits where relevant, and whether independent reviewers checked the process. Find out how often tests are repeated, what changes trigger retesting, and which result would block launch, require remediation, or lead to withdrawal.
Was foreseeable misuse tested?
For systems exposed to untrusted users or consequential workflows, ask about adversarial inputs, prompt injection, data poisoning, abuse, privacy leakage, unsafe content, rate-limit evasion, and stress or outage conditions. Ask what safeguards were tested, how often they fail, and how quickly the vendor patches a discovered weakness. If a risk is irrelevant to your deployment, document why rather than accepting a generic test package.
What evidence can you independently verify?
NIST’s AI Risk Management Framework Playbook calls for testing, evaluation, validation, and verification processes for third-party systems while recognizing that a vendor need not disclose proprietary algorithms. Ask for reproducible procedures, redacted test artifacts, audit reports, evaluation data dictionaries, or customer-validation methods that let your team check important claims without demanding trade secrets.
Questions about human oversight and accountability
Who is responsible for each decision?
Create a responsibility map covering the vendor, your procurement team, system owner, administrators, frontline users, reviewers, security staff, and incident manager. Ask the vendor to name its owner for design, release approval, monitoring, vulnerability response, customer communication, and remediation. Your organization should name corresponding owners before production use.
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What can a human see and control?
Ask what information a reviewer receives with each output, including confidence indicators, source references, warnings, version details, and relevant input context. Determine whether a reviewer can question, edit, override, reject, pause, or stop the system. Define when review is mandatory, what expertise is required, how disagreements are escalated, and how workload or automation bias could make nominal oversight ineffective.
Can affected people challenge an outcome?
Ask how a person affected by an output can obtain an understandable explanation, report an error, request human review, and receive correction within a stated time. The route should not depend on technical knowledge or access to the vendor’s internal team. OECD AI principles emphasize accountability suited to the actor’s role and context and information that helps people challenge an adverse output.
Who investigates and pays for remediation?
Clarify who preserves logs, reconstructs events, interviews users, assesses impact, informs affected people, corrects records, and pays for agreed remediation. Define what records your organization can access and how long they are retained. Require a process for independent review when the vendor’s own investigation could present a conflict.
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Questions about incidents, monitoring, and changes
How will incidents and vulnerabilities be reported?
Ask for the definition of an incident, reporting channel, acknowledgement time, investigation milestones, severity levels, corrective actions, and customer-notification commitments. Include newly discovered bias, privacy leakage, security vulnerabilities, unsafe behavior, service degradation, and failures of human-control mechanisms—not only outages.
What will be monitored after launch?
Agree on measures for accuracy, error types, drift, subgroup performance, abstention, latency, availability, abuse, complaints, and override rates, as appropriate to the use. Ask what deployment-context evidence the vendor will share and how your organization can audit it. Monitoring should continue after a successful pilot; production conditions can differ from laboratory tests.
How are changes controlled?
Require advance notice of model, data, feature, interface, infrastructure, pricing, or subprocessor changes that could alter risk. Define which changes require customer testing, approval, rollback, or a fresh impact assessment. Ask whether you can pin a version, run parallel validation, roll back, suspend traffic, and obtain a change log. A “continuous” service still needs controlled release management.
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What happens during an outage or unsafe result?
Request contingency procedures for degraded performance, unavailable safety filters, compromised credentials, corrupted data, and vendor failure. Identify manual alternatives, maximum tolerable downtime, escalation contacts, and conditions for pausing use. NIST guidance recommends contingency planning for mission-critical systems and decommissioning when risk exceeds the organization’s tolerance.
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What support and knowledge transfer are included?
Specify training for administrators and users, documentation updates, response coverage, accessibility support, security contacts, and service-level commitments. Ask whether your team can obtain configuration records, prompts or policies, evaluation results, incident histories, and other materials needed for independent audit or maintenance.
Can you leave without losing control of your data?
Ask how to export inputs, outputs, metadata, logs, configurations, and audit records in usable formats. Confirm deletion from active systems, backups, support tools, and subprocessors after termination, subject to documented legal retention duties. Check whether interfaces, model outputs, and licensing terms permit migration to another provider or an internal process.
Which dependencies could threaten safe operation?
List critical subcontractors, cloud regions, identity services, data feeds, hardware, open-source components, and specialist staff. Ask what happens if one becomes unavailable, changes terms, or suffers a breach. Assign responsibility for finding alternatives and testing the fallback before it is needed.
Compare vendors with the same evidence request
Use one deployment-specific questionnaire and require each bidder to answer it in the same format. The following axes help a procurement team compare evidence rather than presentation quality.
| Comparison axis | Evidence to request |
|---|---|
| Intended-use fit | Defined purpose and limits, use-case assumptions, alternatives considered, and risk assessment |
| Safety and quality | Evaluation methods, test sets, metrics, results, limitations, deployment-relevant tests, and retesting triggers |
| Data governance | Data lineage and quality information, retention and deletion terms, security and privacy controls, and subprocessor details |
| Accountability | Named roles, human-review and override controls, challenge route, records, investigation, and remediation process |
| Lifecycle oversight | Monitoring measures, incident reporting, change notices, contingency plans, support, and decommissioning procedure |
| Portability | Data export, documentation, interoperability, licensing, version control, and exit terms |
This is a practical synthesis of NIST procurement and third-party risk guidance and OECD due-diligence guidance, not a published scoring standard. Weight the axes according to your use case, and record why a missing item is acceptable rather than silently treating it as low risk.
A due-diligence process that continues after signature
- Describe the deployment: document purpose, users, affected people, data, workflow, alternatives, and consequences of error.
- Set evidence requirements: identify tests, documents, controls, contractual terms, and access your organization needs before approval.
- Run a structured vendor review: send identical questions, hold technical and operational sessions, and record unanswered items.
- Validate in a controlled pilot: use representative data, predefined stop criteria, human review, security testing, and an incident drill.
- Approve with conditions: assign owners, set monitoring thresholds, document residual risks, and put vendor duties in the contract.
- Operate and monitor: review performance, complaints, overrides, incidents, changes, and subgroup impacts at scheduled intervals.
- Reassess or retire: repeat the risk assessment after material changes or incidents; pause, roll back, replace, or decommission when controls no longer match the risk.
NIST’s procurement guidance states that “acquiring a tool that includes AI is not a one-time decision; testing the application over its lifespan is crucial.” Treat that as an operating obligation, not a one-time checklist item.
Red flags in a vendor’s answers
- Only broad claims such as “enterprise-grade,” “bias-free,” or “fully safe,” with no use-case evidence.
- Performance numbers without test conditions, version, date, population, or failure breakdown.
- Refusal to explain data retention, subprocessors, deletion, or use of customer content.
- No named owner for incidents, no customer notification process, or no usable logs.
- Human oversight described as a policy statement without reviewer access, authority, time, or training.
- Silent model updates, no version pinning, and no rollback or suspension mechanism.
- Contract terms that prevent data export, independent validation, or safe discontinuation.
A limitation is not automatically disqualifying. A candid vendor that identifies a narrow operating envelope and offers compensating controls may be safer than one offering sweeping assurances. The decision should document residual risk and who accepts it.
Frameworks and legal duties are not interchangeable
NIST describes its AI Risk Management Framework as voluntary. Using it can organize procurement, testing, monitoring, and accountability, but it is not proof of legal compliance or a guarantee that a system is trustworthy. NIST also notes that trustworthiness characteristics can involve trade-offs and vary by context and by the perspectives of affected people.
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The practical standard is straightforward: require evidence matched to your use, make responsibility and human control explicit, monitor the system in operation, and preserve a realistic way to stop or replace it.
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