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Ask an AI vendor for evidence in six areas: intended use and limits, data practices, safety testing, human oversight, incident and change management, and contract exit terms. The useful answer is not a broad claim that a model is “safe”; it is documentation showing how the system performs and is governed in your proposed setting, with named responsibilities and remedies when it fails.
Use the questions below before purchase, during deployment approval, and at scheduled reviews. NIST treats AI acquisition as continuing work rather than a one-time decision, while OECD due-diligence guidance calls for tracking implementation, effectiveness, and incidents throughout the system’s life.
Start with your own use case and risk assessment
A vendor cannot give meaningful safety evidence until you define what you will do with the system. Write a short deployment statement before the first sales or technical meeting. It should identify the users, decisions or tasks affected, data involved, operating environment, acceptable error level, and what happens if the system is wrong.
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Ask whether AI is appropriate for that problem at all. Compare it with a non-AI process, a rules-based tool, or human-only workflow. NIST procurement guidance recommends defining the problem rather than prescribing an AI solution, performing an initial risk and impact assessment, and revisiting that assessment at decision points. Record unresolved risks instead of treating a vendor’s assurance as a substitute for your assessment.
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Questions for your internal team
- What exact task will the system perform, and which uses are prohibited?
- Could an incorrect output affect safety, employment, credit, healthcare, education, access to services, or legal rights?
- Who could be harmed, including people who are absent from the vendor’s test data?
- What error rate, delay, uncertainty, or loss of availability is tolerable?
- What human decision remains after the AI output, and what evidence must that person review?
Give the vendor this same description. Ask it to identify assumptions about users, language, geography, data quality, workload, and operating conditions that may not hold in your environment.
Questions about purpose, scope, and fit
Intended and prohibited uses
- What problem was the system designed to solve?
- What uses are outside its intended scope, unsupported, or contractually prohibited?
- Which decisions may the system recommend, automate, or make, and which require a human?
- What assumptions about user skill, setting, language, data, and operating conditions support the vendor’s claims?
- What risk and impact assessment supports this deployment, and which risks remain unresolved?
Evidence that the product fits
Request product documentation that describes intended users, supported versions, known constraints, and examples of unsuitable use. Ask the vendor to map its evidence to your deployment statement rather than sending a generic model card or marketing summary. If the vendor cannot explain why its tests resemble your workload, treat that as an evidence gap.
Ask what non-AI alternatives the vendor considered and why this system is preferable. A system that is technically impressive but unnecessary, unexplainable, or impossible to supervise may create more risk than it removes.
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Questions about data, privacy, and limitations
Data used to build and operate the system
- What data sources and data types were used to train, tune, retrieve, or otherwise build the system?
- What is known about the quality, relevance, provenance, licensing, and geographic coverage of those data?
- What third-party datasets, models, libraries, or services are included, and what documentation is available for each?
- Which populations, languages, formats, or operating conditions are poorly represented?
Your organization’s data
- Which prompts, files, images, audio, outputs, feedback, and logs will leave our environment?
- Are inputs or outputs retained? For how long, in which locations, and for what purposes?
- Are our data used to train or improve a shared model, and can that use be disabled?
- Who can access the data, under what role, and what subprocessors receive it?
- What are the deletion, export, backup, and legal-hold procedures when we request removal or end the contract?
Ask for a data-flow diagram and written retention and deletion terms. Confirm whether administrative metadata, abuse-monitoring records, support tickets, and telemetry are treated differently from prompts and outputs. Obtain the security controls relevant to your deployment, such as access management, encryption, tenant separation, logging, vulnerability handling, and breach notification.
Known limitations and failure modes
Ask the vendor to list documented failure modes, not just average performance. Require examples of hallucination, omission, unsafe instructions, bias, privacy leakage, prompt injection, or degradation under unusual inputs when those risks apply. Ask how performance differs for groups or settings unlike the evaluation data and what users should do when confidence is low or the output is contradictory.
Questions about safety and performance evidence
How the system was tested
- What tests were run, by whom, on which model, product, and data versions?
- What test sets, metrics, thresholds, and statistical methods were used?
- Were tests independent, internally reviewed, or performed by customers or outside assessors?
- Were foreseeable misuse, adversarial inputs, abuse, privacy attacks, and stress or load conditions tested?
- What limitations, failed tests, and excluded cases are documented?
Request the evaluation methods, test-set descriptions, results, and limitations relevant to your proposed use. A score without the population, task definition, baseline, and operating conditions is not enough to predict deployment performance. Ask whether the vendor’s tests resemble your language, workflow, volume, latency requirements, and human-review process.
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Release gates and repeat testing
- What finding would stop launch, require remediation, or trigger decommissioning?
- How often are tests repeated, and what event triggers an unscheduled evaluation?
- How are model, data, feature, safety-filter, or infrastructure changes reflected in test results?
- Will the vendor provide deployment-context evidence after launch, not only laboratory results?
NIST’s AI Risk Management Framework Playbook calls for testing, evaluation, validation, and verification processes for third-party systems while recognizing that vendors need not disclose proprietary algorithms. OECD’s 2026 responsible-AI due-diligence guidance similarly emphasizes documenting test sets, metrics, and tools and monitoring assurance criteria in conditions similar to deployment.
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Named responsibilities
- Which vendor role owns design, safety, security, deployment support, monitoring, and incident decisions?
- Which customer role approves use, supervises operators, investigates errors, and accepts residual risk?
- Who can pause the system, change a configuration, or require a rollback?
- Who pays for investigation, notification, correction, and other remediation after a vendor-caused failure?
Put names or accountable functions—not only department names—in the contract and operating procedures. Clarify the boundary between a vendor’s platform responsibility and your responsibility for data, prompts, configuration, user training, and decisions based on outputs.
What a human can see and do
- Can a reviewer inspect the input, output, relevant context, confidence or uncertainty indicators, and system version?
- Can the reviewer question, override, edit, reject, or stop an output without penalty or hidden automation?
- When is escalation to a qualified person mandatory?
- Are decisions and overrides logged well enough to reconstruct what happened?
Challenge and redress
Ask how a person affected by an output can obtain an understandable explanation, request correction, and challenge the result. OECD AI principles call for information that helps people adversely affected by an AI system challenge its output. Establish who receives the complaint, response times, evidence retained, and what remedy is available.
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Questions about incidents, monitoring, and changes
Incident response
- How are vulnerabilities, harmful outputs, bias findings, outages, and newly identified risks reported to customers?
- What notification timelines apply, and what information will an incident notice contain?
- Who investigates, preserves evidence, communicates with affected parties, and verifies corrective action?
- How will the vendor coordinate with your security, privacy, legal, and safety teams?
Ask for the incident-response plan, severity definitions, contact channels, emergency escalation path, and examples of the records customers receive. Include a requirement to share relevant incident information with stakeholders and to document corrective actions.
Post-deployment monitoring
- What performance, safety, security, drift, usage, and fairness indicators are monitored?
- Which indicators will you be able to see, and at what frequency?
- What deployment-context evidence will the vendor share, and what threshold triggers investigation or suspension?
- How are user feedback, complaints, near misses, and overrides incorporated into monitoring?
Change management
- How much notice will you receive before model, data, feature, safety-filter, subprocessor, pricing, or interface changes?
- Which changes require a fresh risk assessment, acceptance test, customer approval, or rollback plan?
- Can you pin a version, run a parallel validation period, suspend use, or roll back safely?
- How long will security fixes and older versions be supported?
Require a change log and a procedure for reassessing the deployment when conditions change. A “minor” update can alter accuracy, refusal behavior, latency, data location, or the evidence needed for a regulated process.
Questions about contract, continuity, and exit
Support and knowledge transfer
- What implementation support, operator training, safety training, and documentation are included?
- Can your staff obtain records needed for independent maintenance, audit, investigation, or regulatory response?
- What service levels, support hours, escalation routes, and recovery objectives apply?
- What happens if a key subprocessor, model, region, or feature becomes unavailable?
Portability and discontinuation
- Can you export prompts, outputs, feedback, configurations, audit logs, and other records in usable formats?
- Are interfaces, data schemas, and licensing terms interoperable enough to change providers?
- What happens to backups, derived data, and support records when the contract ends?
- What is the tested process for a safe shutdown, transition, or human fallback?
NIST procurement guidance recommends continued vendor engagement and training, interoperability, and open licensing terms where possible to reduce lock-in. Ask the vendor to demonstrate export and deletion procedures before signing rather than discovering limitations during an emergency.
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Use one evidence matrix to compare vendors
Give every genuine candidate the same deployment-specific request. Record whether each answer is documented, independently verified, contractually binding, and applicable to your version and region.
| Comparison axis | Evidence to request | What to record |
|---|---|---|
| Intended-use fit | Defined use and limits, assumptions, alternatives considered, and risk assessment | Open risks and prohibited uses |
| Safety and quality | Evaluation methods, test sets, metrics, results, limitations, deployment-relevant tests, and retesting plan | Version, conditions, thresholds, and failed cases |
| Data governance | Data lineage and quality information, retention and deletion terms, security and privacy controls, and subprocessor details | Data flows, locations, access, and deletion evidence |
| Accountability | Named roles, human review and override controls, challenge route, records, and remediation process | Decision owner, escalation path, and remedy |
| Lifecycle oversight | Monitoring, incident reporting, change notices, support, rollback, and decommissioning plan | Notice periods, triggers, and response times |
| Portability | Data export, documentation, interoperability, licensing, and exit terms | Export format, timeline, cost, and tested fallback |
This matrix is a practical synthesis of NIST procurement and third-party risk guidance and OECD due-diligence guidance, not a published certification or scoring standard. Weight the axes according to your use: a system influencing safety-critical decisions needs stronger evidence and controls than an internal drafting assistant.
Red flags that require a pause
- The vendor answers with a certification logo or general “responsible AI” statement but will not provide deployment-relevant methods and limitations.
- It refuses to identify data retention, subprocessors, deletion procedures, or the model and product version you will use.
- It cannot name an accountable incident contact or explain who can stop the system.
- Testing covers only favorable benchmarks, with no misuse, adversarial, subgroup, or production-context evidence where those risks matter.
- Terms allow unilateral changes without notice, prohibit meaningful auditing, or make export and deletion impossible.
- The vendor claims that its framework use proves legal compliance or that the system is trustworthy in every context.
Pause procurement until the gap has an owner, a due date, and an explicit acceptance decision. A risk may be manageable with a control; an undocumented risk cannot be managed reliably.
Turn answers into procurement conditions
- Define acceptance criteria. Set required test results, human-review controls, data restrictions, availability, incident notice, and exit capabilities before evaluating proposals.
- Validate evidence. Check that documents match the contracted product, version, region, configuration, and date. Ask for demonstrations or sample records where practical.
- Run a controlled pilot. Use representative data with safeguards, log errors and overrides, test fallback procedures, and include the people who will operate and be affected by the system.
- Write obligations into the contract. Cover retention, deletion, security, audits, incident response, change notice, support, remediation, portability, and termination.
- Set review points. Reassess after material changes, incidents, new use cases, major performance drift, or changes in law. Keep a decision record showing who accepted residual risk.
- Prepare the stop plan. Maintain a human or alternative process, preserve necessary records, and test suspension, rollback, and transition before they are urgent.
Framework and legal limits
NIST describes the AI Risk Management Framework as voluntary. Its use can structure procurement and risk management, but it is not proof of legal compliance and does not guarantee that a system is trustworthy. NIST also notes that trustworthiness characteristics can involve trade-offs and vary with context and the perspectives of affected people.
For organizations operating in or serving the European Union, the European Commission published guidance on 20 July 2026 describing the scope of AI Act Article 50 transparency obligations and stating that those obligations apply from 2 August 2026. The duties that apply depend on whether you are a provider, deployer, buyer, or another actor, as well as on the system and use. Verify current requirements for the actual jurisdiction and deployment with qualified legal advice.
The procurement test is therefore practical: can you show what the system is for, what it can and cannot do, how it was tested, who controls it, how failures are handled, how changes are governed, and how you will leave? If any answer is “we do not know,” make that uncertainty visible and resolve it before relying on the system.
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