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Evaluate an AI vendor as a continuously changing system and supply chain—not as a product brochure. Start by documenting the intended use, affected people, data, decision stakes and acceptable failure modes. Map every model, dataset, hosting region, subprocessor and update path. Then require evidence for governance, security, privacy, validity, fairness, human oversight, resilience and incident response; test the exact configuration you would buy; put audit, notification, portability and exit rights in the contract; and monitor residual risk after launch. A vendor that passes a questionnaire but cannot support a high-impact decision safely should not pass procurement.
What does a safe AI-vendor evaluation cover?
A sound evaluation follows the AI system through its entire lifecycle, from selecting a use case to retiring the service. It combines documentary evidence, hands-on testing, contractual controls and operational monitoring. No framework or certification proves that a particular deployment is safe; your organization remains responsible for validating scope, version, geography, data flows and residual risk.
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Use a risk-based process. A tool that drafts internal meeting notes does not warrant the same controls as one that ranks job applicants, influences credit decisions, triages patients or moderates public content. The more a system can affect a person’s rights, money, safety, employment or access to services, the stronger your evidence, testing, human-review and exit requirements should be.
How do I define the use case and risk tier?
Write a short use-case record before speaking with vendors. It prevents a generic demonstration from hiding the conditions in which the system will actually operate.
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Record the intended and prohibited uses
- Business objective and the decision or workflow the system supports.
- Named users, administrators and affected people who may never interact with the system.
- Jurisdictions, languages, age groups and accessibility needs.
- Data classes, including personal, confidential, regulated, biometric, financial or children’s data.
- Whether the output informs, recommends, ranks, approves, denies or acts automatically.
- Acceptable error rates, unacceptable failure modes and the point at which a human must decide.
- Prohibited uses, such as making an unreviewed high-impact decision or inferring a sensitive trait.
- Your risk appetite, legal obligations and the option to solve the problem without AI.
Use a practical tiering test
| Risk signal | Questions to answer | Typical control consequence |
|---|---|---|
| Low impact | Would an incorrect output cause only minor rework, with no sensitive data or external decision? | Basic security and privacy review, representative accuracy tests and a named owner. |
| Material impact | Could errors create financial loss, customer harm, discrimination or a regulatory breach? | Documented validation, subgroup testing, human approval, incident deadlines and stronger audit evidence. |
| High impact | Could the system affect employment, credit, health, housing, education, safety or legal rights? | Mandatory control gates, independent review, robust appeal and correction paths, conservative automation limits and a tested fallback. |
| Unacceptable or unmanageable | Can you not identify the data flow, human authority, failure containment or lawful basis? | Do not deploy until the gap is closed or choose a non-AI alternative. |
Keep the tier tied to the specific deployment. The same model can be low risk for brainstorming and high risk when its score triggers an adverse action.
How do I map the AI system and its supply chain?
Ask for a current architecture and data-flow diagram, then verify it against the service you are buying. Your map should show:
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- Foundation, fine-tuned and task-specific models, including version identifiers and fallback models.
- Training, fine-tuning, retrieval and evaluation data sources; rights and provenance for each source.
- Prompt, response, embedding, telemetry and support data; retention and deletion paths.
- Hosting regions, transfer mechanisms, tenant isolation and backup locations.
- Subprocessors, cloud dependencies, open-source components, external tools and retrieval indexes.
- Authentication, authorization, secrets, logging, moderation and administrative access.
- Update, rollback, deprecation and emergency-disable paths, including who can trigger them.
NIST SP 1326 (published July 2026) frames supplier due diligence around foreign ownership, control or influence (FOCI), provenance, resilience, foundational cyber practices and supply-chain tiers. Ask the vendor to identify those factors for its own providers, not merely to list a brand name. A vendor that cannot disclose a critical subprocessor or hosting region leaves you unable to assess jurisdictional, continuity or concentration risk.
What evidence should an AI vendor provide?
Request an evidence pack with dates, scope, version and exceptions. Marketing claims are not substitutes for operating evidence.
| Evidence | What to check |
|---|---|
| System and model cards | Intended purpose, boundaries, training or fine-tuning methods, known limitations, supported languages, version and change history. |
| Data provenance and rights record | Sources, licences or permissions, quality controls, sensitive-data handling, deletion requests and retention rules. |
| Evaluation plan and results | Task accuracy, calibration, robustness, edge cases, misuse, hallucination, refusal behavior and results for relevant subgroups and conditions. |
| Security architecture | Identity and access controls, encryption, tenant isolation, secrets management, logging, vulnerability management and secure development practices. |
| Privacy materials | Processing purposes, roles, locations, transfer safeguards, data-subject rights, retention, deletion and whether customer content trains models. |
| Independent assurance | Audit or assessment report, assessor, period, covered services, exclusions, findings, exceptions and remediation dates. |
| Resilience and incident records | Recovery objectives, backup and restore tests, service availability, material incidents, root causes and corrective actions. |
| Operational artefacts | Sample audit logs, access reviews, release approvals, red-team summaries, escalation contacts and support procedures. |
For every document, record the service edition, model version, geographic region, data class and assessment period. An assessment of a public API may not cover an enterprise tenant, a new model or a private deployment.
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Which questions should I ask about data and privacy?
Get precise answers about what enters the service, where it goes and who can use it. Put answers that matter to your risk decision into the contract.
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Data lifecycle
- What prompts, files, outputs, embeddings, identifiers and telemetry are collected?
- Where is each category processed and stored, including support and backup systems?
- What is the default retention period, and can you configure shorter retention or zero retention?
- Is customer content used to train, fine-tune, evaluate or improve any shared model? Is the setting technically enforced or only promised in policy?
- How are deletion, correction, access and export requests handled, including copies in logs and backups?
Rights, quality and sensitive data
- Can the vendor show provenance, permissions and quality checks for training, retrieval and evaluation data?
- How are personal, confidential, regulated and children’s data detected, minimised and isolated?
- Which party is the controller, processor or equivalent, and which subprocessors receive data?
- What safeguards govern cross-border transfers and government or law-enforcement requests?
Do not accept “we do not train on your data” as a complete answer. Clarify whether humans, support tools, abuse-monitoring systems, embeddings or future product features can access the content, and whether the promise applies to every region and model you will use.
How should I assess security and operational controls?
Evaluate controls that prevent unauthorised access and controls that limit damage when prevention fails.
Preventive controls
- Strong administrator and service-account authentication, least privilege, segregation of duties and regular access reviews.
- Encryption in transit and at rest, customer-managed keys where required, and protected secrets.
- Tenant isolation, input and output filtering, rate limits, abuse detection and secure interfaces for tools or retrieval systems.
- Secure software development, dependency tracking, vulnerability scanning, patch deadlines and penetration testing.
Detective and recovery controls
- Tamper-resistant logs that show users, prompts, model versions, tool calls, policy decisions and administrative actions.
- Monitoring for data leakage, prompt injection, anomalous use, supply-chain compromise and model regressions.
- Documented incident response, customer notification timelines, forensic support, backup restoration and disaster recovery exercises.
- Defined recovery time and recovery point objectives, support coverage and remedies when service levels are missed.
Ask for scope and exceptions in every audit or penetration-test report. A clean report that excludes the inference service, customer isolation or the region you will use may provide little assurance.
How do I test a vendor’s claims?
Run a controlled acceptance test against the exact model, configuration, geography, data class and integrations in your proposed purchase. Do not rely solely on a vendor’s benchmark.
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- Build a representative test set. Include normal cases, rare edge cases, realistic language and formats, expected refusals, accessibility needs and the decisions the system will influence. Keep a protected set for final acceptance.
- Measure useful behavior. Check accuracy, calibration, groundedness, latency, throughput and availability against your documented thresholds. Record false positives and false negatives separately when their harms differ.
- Probe adversarial behavior. Try prompt injection, jailbreaks, malicious files, data-exfiltration prompts, poisoned retrieval content, abusive requests and attempts to make the system reveal secrets or system instructions.
- Test privacy leakage. Use canary records and membership-style probes where lawful. Verify that one tenant cannot retrieve another tenant’s content and that deletion removes data from the paths the vendor claims to cover.
- Check fairness and subgroup performance. Compare error and refusal patterns for relevant demographic, language, disability and geographic groups. Investigate small-sample uncertainty rather than hiding it behind one aggregate score.
- Test distribution shift and updates. Replay cases after a model or retrieval update, vary input quality and language, and verify rollback. Require advance notice where a change could alter safety or performance.
- Exercise human escalation. Confirm that reviewers can see the evidence behind an output, override it, correct records, document an appeal and stop automated actions. Measure whether escalation remains usable under peak load.
- Document residual risk. Save test data, configuration, results, defects, compensating controls and a named approver. A failed critical test is a gate, not merely a low score.
How do I compare AI vendors on a common basis?
Give every finalist the same scenario, test set, questions and evidence deadline. Score dimensions separately, apply weights that reflect your use case and set non-negotiable gates. A high total cannot compensate for unlawful data use, missing incident notification or an inability to contain an unsafe high-impact decision.
| Comparison axis | Evidence or test | Decision question |
|---|---|---|
| Safety and misuse resistance | Abuse probes, refusal tests, rate limits and containment design. | Can foreseeable misuse be blocked or limited? |
| Security and assurance | Architecture, test reports, access reviews, vulnerability and incident records. | Are controls independently examined and operating? |
| Privacy and data rights | Data-flow map, retention settings, training terms, deletion and export tests. | Can you lawfully control and remove your data? |
| Validity and robustness | Representative accuracy, calibration, edge cases, drift and update replay. | Does it work under your conditions, not just a public benchmark? |
| Fairness and transparency | Subgroup results, limitations, explanations and user notices. | Can affected people understand, challenge and correct an outcome? |
| Human oversight and recourse | Override, appeal, escalation, reviewer training and audit trails. | Can a person stop harm before it becomes irreversible? |
| Resilience and response | Availability history, recovery tests, support commitments and notification process. | Will the service and your fallback work during an incident? |
| Supply-chain provenance | Model, data, subprocessor, ownership and dependency disclosures. | Can you identify and manage hidden concentration or jurisdiction risk? |
| Interoperability and exit | Export format, APIs, documentation, deletion certificate and transition support. | Can you switch providers without losing records or control? |
| Total cost and support quality | Usage, storage, evaluation, support, migration and control costs. | Is the safer option affordable to operate, not just to purchase? |
Keep a decision log showing scores, evidence links or document identifiers, exceptions, compensating controls and the person who accepted residual risk. Re-score when the use case, model, data, law or supplier changes.
Which AI contract clauses protect the buyer?
Translate evaluation findings into enforceable duties. Policies can change without notice; a contract should define what happens when risk changes.
| Clause area | Terms to address |
|---|---|
| Permitted use and boundaries | Approved purposes, prohibited uses, human decision authority, customer responsibilities and documented configuration. |
| Data ownership and confidentiality | Customer-content ownership, confidentiality, no unauthorised disclosure and restrictions on internal access. |
| Training and retention | Whether content may train or improve models, retention limits, deletion from primary and backup systems, and evidence of completion. |
| Security baseline | Required controls, vulnerability remediation, penetration testing, access reviews, encryption and subcontractor obligations. |
| Incident and vulnerability notice | Defined triggers, clock starts, notification channels, cooperation, updates, root-cause analysis and customer remedies. |
| Audit and evidence | Access to relevant reports, questionnaires, logs or independent assessments, with limits that do not make verification impractical. |
| Model and service changes | Advance notice, material-change definition, testing period, rollback, opt-out or termination for unacceptable regression. |
| Subprocessors and supply chain | Current list, notice of additions, objection rights, flow-down duties and responsibility for provider failures. |
| Service levels and continuity | Availability, support response, recovery objectives, maintenance notice, backup testing and remedies. |
| Liability and insurance | Allocation for confidentiality, privacy, security, intellectual-property and high-impact harms, plus applicable coverage. |
| Portability and exit | Usable export formats, API access, documentation, migration assistance, deletion certification, suspension and transition time. |
Require interoperability and documented exit procedures before production. A technically excellent service can become an unacceptable risk if you cannot retrieve records, preserve audit history or move a workflow after a price increase, acquisition, incident or market exit.
How do I govern and monitor the service after launch?
Assign a business owner, technical owner, security and privacy contacts, and a risk approver. Set a review cadence appropriate to the tier, then add event-triggered reviews.
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Track operational signals
- Incidents, near misses, complaints, appeals, overrides and control exceptions.
- Accuracy, calibration, hallucination or refusal rates, subgroup performance and drift.
- Prompt-injection attempts, data-leakage alerts, abuse, vulnerability findings and access anomalies.
- Latency, availability, recovery performance, update regressions and support response.
- Subprocessor, hosting-region, ownership, model-version and policy changes.
Define reassessment triggers
Reopen the assessment after a material model or retrieval change, new data class, new geography, new integration, security incident, repeated fairness complaint, degraded service, subprocessor change, acquisition, deprecation or change in law. Suspend or narrow use when measured risk exceeds tolerance; use a documented fallback rather than waiting for a vendor’s next release.
Which frameworks can structure the evaluation?
Frameworks provide a common vocabulary and work plan, but they do not certify a vendor or replace deployment-specific testing.
NIST AI Risk Management Framework
NIST AI RMF 1.0 is a voluntary framework released January 26, 2023. Its four functions—govern, map, measure and manage—fit procurement, testing and operations. The NIST AI RMF Core states: “Risk management should be continuous, timely, and performed throughout the AI system lifecycle dimensions.”
ISO/IEC 23894:2023
ISO/IEC 23894:2023, Edition 1, was published in February 2023 and is listed as a 26-page standard. It provides customizable guidance for organizations that develop, produce, deploy or use AI products, systems and services. Its abstract notes: “The application of this guidance can be customized to any organization and its context.” Customization means you still need to document your own use, controls and acceptance criteria.
UK AI Risk Management Toolkit
The UK Department for Science, Innovation and Technology published its AI Risk Management Toolkit on September 8, 2026. It is aimed at teams designing, procuring, operating or delivering AI products and emphasizes understanding and justifying risks and treatment strategies. Treat it as practical guidance, not a safety guarantee.
Canadian implementation guidance
Innovation, Science and Economic Development Canada’s 2024 implementation guide for managers describes six principles: “Safety, Accountability, Transparency, Fairness & Equity, Human Oversight & Monitoring, and Validity & Robustness.” They are useful prompts for evidence requests and acceptance tests, even when your organization is outside Canada.
What mistakes make vendor evaluations unreliable?
- Accepting a generic certification. Fix it by checking scope, service edition, period, exclusions, exceptions and remediation.
- Testing a demo instead of the purchased configuration. Use your region, model version, integrations, data class and realistic workload.
- Scoring without gates. Make unlawful processing, absent notification, untestable isolation or unsafe automation stop conditions.
- Ignoring non-users and downstream decisions. Include people affected by rankings, recommendations or records, not only the operator.
- Assuming the model is static. Contract change notice, replay tests, rollback and reassessment triggers.
- Leaving human review as a slogan. Test reviewer authority, evidence visibility, workload, training, appeal and the ability to stop an action.
- Planning no exit. Verify exports, deletion, documentation, migration assistance and a workable fallback before signing.
- Failing to record exceptions. Name the risk owner, compensating control, expiry date and approval for every unresolved gap.
What should the final approval record contain?
Keep one decision file that a new reviewer can understand without a vendor meeting. Include the use-case record and tier; architecture and supply-chain map; evidence register; test plan and results; comparison scores; contract departures; monitoring metrics; fallback and exit plan; open risks; and named approvals. State exactly which model, version, region, configuration and data classes were approved, along with the date and conditions. Approval is for that bounded deployment—not a blanket endorsement of the vendor’s other products.
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Can a SOC 2 or ISO certificate prove that an AI model is safe?
No. Such reports can support evidence about specified organizational or security controls, but they may exclude model behavior, training-data rights, subgroup performance or your deployment configuration. Read the scope, testing period, exceptions and remediation status, then perform use-case testing yourself.
What should we do if a vendor will not disclose training-data provenance?
Treat the missing provenance as a risk finding, not as proof that the data is unlawful. Ask for a rights summary, categories and quality controls, independent assurance or contractual indemnity where appropriate; restrict sensitive or high-impact use until the uncertainty is acceptably controlled, or choose a provider that can provide the evidence.
How often should an AI vendor risk assessment be repeated?
Use a tier-appropriate scheduled review, but do not wait for the calendar after a material model, data, subprocessor, hosting, ownership, incident or legal change. A trigger-based reassessment is especially important for systems whose outputs can directly affect people.
Is a vendor questionnaire enough for a small organization?
A short questionnaire can be a useful first screen, but it should lead to evidence checks and a small acceptance test. Prioritize the controls that could cause irreversible harm—data use, access, human approval, incident notification, continuity and export—rather than trying to reproduce a large enterprise audit.
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