Evaluate an AI vendor against the system you will actually deploy—not a logo, benchmark, or promise. Define the use and consequences first, map every model and data dependency, request evidence for conventional and AI-specific controls, test performance under representative conditions, and put incident, change, availability, and exit duties in the contract. The depth of review should match the harm a failure could cause: an internal drafting assistant does not need the same approval path as a system that influences eligibility, safety, employment, healthcare, or financial decisions.
What a defensible evaluation must establish
A procurement review should answer five questions:
- What is the system supposed to do? Identify the users, affected people, decisions, data, jurisdictions, integrations, and unacceptable uses.
- What is inside the vendor’s boundary? Document the model, hosting, storage, interfaces, subcontractors, external data sources, and your own controls.
- Can the vendor protect the service and your information? Examine confidentiality, integrity, availability, identity, encryption, development, vulnerability management, logging, recovery, and incident response.
- Does it work reliably for your task? Test representative inputs, groups, languages, edge cases, misuse, and changing conditions rather than accepting a headline benchmark.
- Can you control change and recover from failure? Establish monitoring, notification, human review, fallback, export, deletion, and termination arrangements.
These questions are related but not interchangeable. A vendor can have strong infrastructure security and still produce unreliable or unfair outputs, or offer accurate outputs while retaining prompts in a way your privacy obligations do not permit.
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Match the review to impact and exposure
Write a short use-case statement before sending a questionnaire. Include the intended purpose, users, affected individuals, input and output data, countries or regions, connected systems, decisions supported or automated, and the consequences of a wrong answer, a malicious answer, a disclosure, or an outage. Record which failures require a person to review the result, a manual process to take over, or an immediate stop.
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| Use profile | Typical exposure | Review emphasis |
|---|---|---|
| Low-impact internal assistant | Limited internal information; a wrong draft is easily corrected | Basic access control, data-use restrictions, testing for common errors, support and deletion terms |
| Customer-facing or operational workflow | Personal or confidential data; errors affect service, cost, or reputation | Detailed data-flow mapping, independent control evidence, representative testing, availability and incident commitments, human escalation |
| High-impact or safety-relevant system | Outputs influence eligibility, employment, credit, health, safety, rights, or access to essential services | Formal approval, documented limitations, subgroup and adversarial testing, mandatory human decision authority, rehearsed fallback, stronger audit and change rights, legal review |
Use this classification to decide who must sign off, how much evidence is required, how often the service is reviewed, and whether the proposed automation is acceptable at all. A voluntary framework can organize the work, but it does not decide whether a particular law, regulator, or internal policy permits the use.
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Organize the work with NIST AI RMF
NIST released the voluntary AI Risk Management Framework 1.0 on January 26, 2023. Its lifecycle functions are Govern, Map, Measure, and Manage; the companion Playbook provides suggested actions and references. NIST says the framework is being revised and has noted an April 7, 2026 concept note for a critical-infrastructure profile. Treat a vendor’s statement that it “uses NIST” as a starting claim, not proof of a control or outcome. Ask for artifacts that match your deployment.
The framework describes trustworthy AI as valid and reliable; safe; secure and resilient; accountable and transparent; explainable and interpretable; privacy-enhanced; and fair with harmful bias managed. These properties can conflict. For example, collecting more data may improve a narrow accuracy measure while increasing privacy exposure. Your review should document the trade-off and the reason it is acceptable for the use.
Govern
Assign an internal owner, approval authority, risk tolerance, escalation path, and record-keeping requirements. Decide who can approve a new use, a model change, or an exception. Make the vendor accountable for the commitments it controls and your organization accountable for configuration, user training, and decisions made with the output.
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Map
Describe the context, affected people, intended benefits, foreseeable harms, system boundaries, third-party components, and dependencies. Mapping should cover what the system does not support and the conditions under which its claims do not apply.
Measure
Use repeatable tests and monitoring to evaluate validity, reliability, security, privacy, safety, explainability, and fairness as relevant to the use. Keep the test data, method, thresholds, results, and known limitations so a later reviewer can reproduce the decision.
Manage
Prioritize risks, apply controls, document residual risk, respond to incidents, recover service, and improve the system. NIST’s Core calls for post-deployment monitoring, contingency planning for high-risk third-party failures, and change management.
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NIST’s official AI Resource Center excerpt defines reliability as the “ability of an item to perform as required, without failure, for a given time interval, under given conditions” (quoting ISO/IEC TS 5723:2022). The same excerpt says, “AI risk management efforts should prioritize the minimization of potential negative impacts, and may need to include human intervention in cases where the AI system cannot detect or correct errors.” Apply both statements to the concrete conditions and consequences in your use case.
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Map the system and supplier boundary before asking for proof
Request a current architecture and data-flow description. It should identify:
- The model family, version or release identifier, inference endpoint, region, and hosting arrangement.
- Whether prompts, files, feedback, telemetry, logs, identifiers, embeddings, or derived data are stored or sent to another service.
- Training, retrieval, safety-filtering, monitoring, analytics, identity, payment, and support components supplied by other parties.
- Your responsibilities for configuration, access, content review, retention, key management, and incident response.
- Unsupported uses, rate or size limits, geographic restrictions, and dependencies whose failure can stop or degrade the service.
Ask the vendor to identify every subprocessor and external data source relevant to your tenant, explain the control each party has, and state how you will be notified of a material boundary change. A diagram is useful, but verify it against contract language, product documentation, and the actual configuration you will purchase.
Request security evidence that matches the deployment
Assess confidentiality, integrity, and availability across the application, model endpoints, administrative plane, storage, networks, and support channels. A certificate or audit report is evidence about a defined scope and period; it does not show that every feature, region, configuration, or AI behavior is safe. Compare the report’s scope and exclusions with the service and data flows you will use, and request remediation status for material findings.
| Control area | Questions to ask | Evidence to request |
|---|---|---|
| Identity and privileged access | How are administrators authenticated, approved, reviewed, and removed? Are production actions logged and separated by role? | Access-control policy, privileged-access workflow, review records, and relevant log samples with sensitive details redacted |
| Encryption and keys | What is encrypted in transit and at rest? Who controls keys, how are they rotated, and what happens if a key is revoked? | Architecture description, key-management controls, rotation and recovery procedures |
| Tenant separation | How are customer data, caches, indexes, logs, and backups isolated? Can one tenant’s content influence another tenant’s output? | Isolation design, testing summary, and remediation history for cross-tenant findings |
| Secure development | How are code, model, prompt, and configuration changes reviewed, tested, approved, and rolled back? | Development policy, release gates, dependency-management process, and change records |
| Vulnerability management | How are infrastructure, dependencies, interfaces, models, and exposed prompts tested? What severity targets and exceptions apply? | Assessment summary, remediation targets, exception process, and recent trend information |
| Logging and detection | What security, administrative, model, and data-access events are recorded? How long are they retained and who can investigate them? | Event catalogue, retention policy, detection and escalation procedures |
| Backup and recovery | What is backed up, how often, where, and how is restoration tested? Are backups encrypted and deleted on schedule? | Recovery objectives, restoration-test results, and dependency list |
| Subcontractors | Which parties can access data or operate a critical component, and how are they assessed and monitored? | Subprocessor register, oversight method, contractual flow-downs, and change-notice process |
| Incident response | Who declares an incident, who contacts you, what evidence is preserved, and how are containment and recovery coordinated? | Incident plan, escalation contacts, exercise summary, and notification commitments |
Ask about AI-specific attack paths
Ordinary software and infrastructure risks still apply, but an AI review must also address evasion, data poisoning, model extraction, and exposure of training data or other intellectual property through an endpoint. Ask how the vendor limits abusive querying, detects anomalous extraction attempts, protects training and retrieval sources, validates data and model updates, and evaluates prompt or input manipulation. Request the threat model, test scope, material findings, and the controls that remain your responsibility.
Read exclusions as carefully as controls
Evidence may exclude a beta feature, a particular region, a support environment, a newly acquired service, or customer-managed settings. Record each exclusion, decide whether it affects your use, and obtain a compensating control or approval for the residual risk. Do not treat a broad assurance statement as coverage for an unexamined deployment.
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Trace data and negotiate privacy terms
Start with an inventory of what leaves your systems. Separate the categories because each may have a different purpose, retention period, access path, and legal basis.
| Data category | Questions |
|---|---|
| Prompts and instructions | Are they stored, inspected by support, used for training or service improvement, or shared with another processor? |
| Uploaded files and retrieved documents | Where are originals, extracted text, embeddings, indexes, and backups held? Can they appear in another customer’s result? |
| Outputs and feedback | Are outputs retained or used to tune a model? Who can view user ratings, corrections, or flagged content? |
| Telemetry and logs | Which identifiers, device details, usage events, and error payloads are collected, and can they contain sensitive content? |
| Derived data | Are profiles, embeddings, scores, labels, or risk signals created, and can you access or delete them? |
For every category, ask why it is collected, whether it is necessary, who can access it, where processing occurs, how long it is retained, how deletion propagates through indexes and backups, and which subprocessors receive it. Ask whether the service can operate with minimization, de-identification, aggregation, private networking, customer-managed keys, or disabled feedback collection. Have privacy and legal specialists determine which duties apply to the actual data, parties, purposes, and jurisdictions; the AI RMF does not answer that legal question.
Put the promises in the contract
- Define the service, model versions, regions, environments, integrations, and approved purposes.
- List permitted processing and prohibit training, advertising, profiling, or other secondary uses unless expressly authorized.
- Set retention periods, deletion triggers, backup treatment, access rules, and assistance with data-subject or customer requests where applicable.
- Identify subprocessors, require notice of additions or material changes, and provide an objection or termination path when negotiated.
- Allocate responsibility for security and privacy incidents, including notice, cooperation, evidence preservation, containment, remediation, and cost discussions.
- Require appropriate security evidence and notice when its scope, controls, or data locations materially change.
Test performance and reliability for the real job
Vendor benchmarks can be useful context, but they rarely represent your users, languages, documents, workflow, or consequences. Before a pilot, write acceptance thresholds and a test plan. Use a representative, lawfully collected evaluation set that includes normal cases, edge cases, ambiguous inputs, adversarial prompts, and cases where the correct action is to abstain or escalate.
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|---|---|---|
| Task quality | Correctness, completeness, relevance, citation or extraction accuracy, and abstention behavior | Predefined pass threshold and examples of unacceptable errors |
| Failure severity | Frequency and consequence of false positives, false negatives, fabricated content, unsafe advice, or data leakage | Human-review rule and maximum tolerable residual risk |
| Coverage | Languages, dialects, document types, user groups, accessibility needs, and operating regions | Subgroup results and documented limitations |
| Robustness | Performance under malformed input, prompt injection, distribution shift, load, outages, and dependency failure | Fallback trigger and recovery procedure |
| Operations | Latency, throughput, rate limits, error rates, and support response during representative load | Capacity plan and service-level commitments |
Have qualified reviewers label results using a written rubric, preserve the test set and configuration, and record confidence intervals or other uncertainty measures when they are meaningful. Compare the vendor’s methodology with yours; a high score on an unrelated benchmark is not evidence that your task meets its threshold. Evaluate whether the system is valid and reliable under the specific conditions in which you will operate it.
Keep a human decision path where consequences warrant it
Define when a person must review, what information the reviewer receives, how disagreement is resolved, and how a user can appeal or correct an output. Human review is not meaningful if the person cannot understand the task, lacks time to challenge the result, or is instructed to accept the model by default. For high-impact uses, decide in advance when automation is prohibited rather than relying on an operator to notice every failure.
Control changes and drift
Ask what changes to the model, system prompt, safety filters, training or retrieval data, dependencies, rate limits, or hosting region trigger notice, re-testing, or approval. Require release identifiers or another way to correlate an output with the version that produced it. Monitor quality, subgroup differences, complaints, overrides, latency, outages, and security events after launch. A change that improves an average score can still worsen a critical failure mode.
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Verify resilience, incident handling, and exit
Reliability includes more than uptime. A service can be reachable while returning degraded, stale, unsafe, or unauditable results. Ask the vendor to define its service commitments and the conditions under which they do not apply.
| Operational area | Terms or evidence to establish |
|---|---|
| Availability | Measurement method, maintenance exclusions, dependency exclusions, regions covered, and remedies for sustained failure |
| Support | Severity definitions, response and update times, escalation contacts, and availability of critical support |
| Recovery | Recovery-time and recovery-point objectives, backup scope, restoration tests, and customer communication |
| Incidents | Notification deadline, required contents, evidence preservation, cooperation, containment, and post-incident report |
| Fallback | Manual procedure, alternate model or provider, queued work, safe shutdown, and who can invoke each option |
| Exit | Export format, assistance, deletion certification, transition period, and treatment of backups and derived data |
Test the plan, not just the wording. Run a tabletop or technical exercise for a vendor outage, a compromised credential, a poisoned data source, a model regression, and a serious privacy incident. NIST’s Generative AI Profile recommends third-party incident plans, rehearsals, serious-incident notification, clear ownership, and service-level terms covering incident response, response times, and availability of critical support.
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- Identify the exact service, model or release, regions, environments, integrations, approved users, and approved purposes.
- Attach the data-flow and responsibility descriptions, including subprocessors and external dependencies.
- Specify data categories, permitted processing, training or secondary-use restrictions, location, retention, deletion, access, and backup handling.
- Require current, scope-matched security documentation and notice of material control, scope, or location changes.
- Allocate responsibility for security, AI, privacy, and availability incidents; set notice, cooperation, preservation, remediation, and escalation expectations.
- Define availability measurement, support response, recovery objectives, continuity arrangements, and negotiated remedies.
- Require notice and review for model, prompt, data, dependency, or service changes that could affect risk or performance.
- Establish audit or evidence rights proportionate to impact, including access to relevant test summaries and remediation status.
- Set export, deletion, transition assistance, and termination rights before production use.
- Have counsel assess enforceability and the privacy, sector, and AI rules that apply in every relevant jurisdiction.
Make approval continuous after launch
Assign one internal owner with authority to pause the service. Set a review cadence based on impact and exposure, and trigger an unscheduled review after a material incident, model change, new data type, new region, major complaint, or change in the decision being supported.
| Cadence | Checks |
|---|---|
| Every use or configuration change | Confirm approved purpose, data categories, permissions, model version, human-review rule, and rollback path |
| Monthly or risk-equivalent operational review | Review error and override rates, latency, outages, support cases, security alerts, privacy events, and drift indicators |
| Quarterly or agreed assurance review | Check vendor evidence, subprocessor and region changes, open findings, incidents, test results, and contract compliance |
| At renewal | Reassess benefit, residual risk, legal context, pricing or service changes, portability, and whether continued use remains justified |
Keep an evidence file containing the approved use, architecture, questionnaire answers, contracts, test data and results, decisions and exceptions, incident records, monitoring reports, and renewal approvals. This turns a one-time purchase check into an auditable risk-management process.
Compare vendors on evidence, not badges
When several suppliers remain viable, score the same criteria and weight them by impact. Do not let one certification or benchmark decide the result.
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| Evidence scope and independent assurance | Current, independent evidence covering the product, region, environment, and controls you will use, with exclusions and remediation explained |
| Data practices | Clear purposes, no unapproved secondary use, defined retention and deletion, controlled access, known locations, and manageable subprocessors |
| Task performance | Representative results for your population and conditions, known limits, failure severity, abstention behavior, and repeatable testing |
| Incident and continuity | Specific notice and response duties, measurable support and availability commitments, tested recovery, and workable fallback |
| Change and exit | Version or release visibility, notice and retest triggers, monitoring support, exportable data, deletion, and transition assistance |
A supplier that cannot answer a question is not automatically unacceptable; the gap may be low impact or resolvable with a control in your environment. Record the uncertainty, compensating measure, owner, and deadline instead of converting an unanswered question into an assumption.
Questions to send in an AI-vendor questionnaire
Use and boundaries
- What uses, users, decisions, data types, regions, and integrations does the proposed service support?
- What uses are prohibited or untested?
- Which model, version, retrieval sources, safety layers, and subprocessors are in the proposed path?
- Which controls are yours and which must we operate?
Security
- How do you manage privileged access, tenant isolation, encryption, keys, secure development, vulnerabilities, logging, backups, and recovery?
- How do you test evasion, poisoning, model extraction, prompt manipulation, and exposure of training or customer data?
- What independent assessment covers this exact service, region, and environment, and what is excluded?
Privacy
- What prompts, files, outputs, feedback, telemetry, identifiers, logs, and derived data are collected?
- For each category, what is the purpose, retention, location, access path, subprocessor, training use, and deletion method?
- Can minimization, de-identification, aggregation, private connectivity, or disabled improvement use be configured?
Performance and operations
- What methodology and data support your quality claims, and how do results vary by group, language, input, and operating condition?
- What limitations, failure modes, abstention behavior, rate limits, and change triggers should we plan for?
- What are your availability, support, recovery, incident-notification, export, and deletion commitments?
Common review failures and how to correct them
Accepting a framework name as proof
Failure: treating “aligned with NIST” as a completed assessment. Correction: map each relevant claim to evidence, a control owner, a test result, and a residual-risk decision. NIST AI RMF is voluntary guidance, not a certification.
Checking infrastructure while ignoring model behavior
Failure: approving encryption and access controls without testing hallucination, bias, unsafe outputs, extraction, or prompt manipulation. Correction: include AI-specific threats and task-level failure tests alongside ordinary security review.
Using a generic benchmark
Failure: assuming a high published score predicts your workflow. Correction: test representative data, affected groups, edge cases, misuse, and the cost of each error before setting acceptance thresholds.
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Failure: relying on a marketing page for retention, training use, deletion, or subprocessors. Correction: make purposes, locations, access, retention, deletion, notice, and incident duties binding terms.
Planning only for uptime
Failure: having no safe response to degraded quality, a dependency failure, or a serious incident. Correction: rehearse manual operation, alternate paths, safe shutdown, communications, restoration, and exit.
Approving once and forgetting change
Failure: allowing a model, prompt, data source, or region to change without retesting. Correction: require notice and release identification, monitor outcomes, and reopen approval when the risk context changes.
Quick Recap
A practical decision workflow
- Classify impact. Document affected people, decisions, data, jurisdictions, and consequences.
- Map boundaries. Obtain the architecture, data flows, dependencies, subprocessors, and responsibility split.
- Set requirements. Write security, privacy, performance, availability, human-review, monitoring, and exit criteria before reviewing answers.
- Collect evidence. Request scope-matched assessments, policies, test summaries, remediation details, contracts, and operational commitments.
- Run representative tests. Measure quality, failure severity, subgroup coverage, robustness, latency, and abstention behavior against predefined thresholds.
- Decide residual risk. Record gaps, compensating controls, owners, deadlines, and the authority that accepts the remaining risk.
- Contract and pilot safely. Limit data and users, keep a human path, monitor results, and retain a rollback or manual procedure.
- Approve, pause, or reject. Production approval should depend on evidence and tested controls, not on a vendor’s general reputation.
- Monitor and renew. Review incidents, drift, changes, complaints, evidence, and legal context throughout the relationship.
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