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Evaluate an AI vendor against the way you will actually use its system, not against a badge, benchmark, or sales presentation. Define the decisions, people, data, integrations, jurisdictions, and consequences involved; then verify security controls, data practices, task-specific performance, resilience, incident handling, and exit terms with evidence that matches your deployment.
A low-impact internal drafting assistant and a system that influences eligibility, safety, employment, healthcare, credit, or other consequential outcomes need different levels of scrutiny. Use a proportional, documented review before adoption and repeat it when the model, service, data flows, or use case changes.
Start with intended use and impact
Write a short use-case statement before sending a questionnaire. It should identify what the system does, who operates it, who is affected, and what happens when it is wrong or unavailable. This statement becomes the boundary for every later answer and prevents a vendor from supplying evidence for a different product or deployment.
Record the deployment context
- List intended and prohibited uses, user groups, affected people, supported decisions, and any decisions the system makes automatically.
- Classify the information involved, including prompts, uploaded documents, identifiers, telemetry, feedback, logs, and derived data.
- Document jurisdictions, regions, integrations, internal systems, downstream recipients, and any subcontractors.
- Describe the consequence of a wrong output, a biased result, data exposure, manipulation, or service outage.
- Set the conditions that require human review, a manual fallback, suspension of the feature, or a complete stop.
Ask the vendor to draw the system boundary: model, application, APIs, storage, monitoring, support tools, training or fine-tuning services, data sources, and third parties. Ask what the vendor does not support. A supplier that cannot explain where data enters, is processed, retained, and leaves cannot provide a meaningful risk assessment.
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Scale scrutiny to impact
For a low-impact internal assistant, proportionate evidence may include documented data handling, access controls, incident contacts, basic availability commitments, and a limited pilot. A system affecting legal rights, physical safety, access to essential services, or employment warrants stronger independent assurance, representative testing, formal human oversight, tested continuity arrangements, detailed contractual controls, and ongoing monitoring. NIST AI RMF guidance is voluntary risk-management guidance; it is not a certification and does not replace applicable law or your own control requirements.
Use NIST AI RMF as a structure, not a vendor credential
NIST released AI Risk Management Framework (AI RMF) 1.0 on January 26, 2023. Its lifecycle functions are Govern, Map, Measure, and Manage. The companion Playbook provides actions and references for applying the framework. NIST says the framework is being revised and identifies an April 7, 2026 concept note for a critical-infrastructure profile.
“Uses NIST” is not proof that a supplier has implemented controls or achieved good outcomes. Ask which AI RMF outcomes apply to your deployment, what evidence supports them, who owns each control, and what remains unresolved. NIST describes trustworthy AI through several characteristics that must be considered in context: valid and reliable; safe; secure and resilient; accountable and transparent; explainable and interpretable; privacy-enhanced; and fair with harmful bias managed. These characteristics can conflict, so require the vendor to explain trade-offs rather than presenting one score as a complete answer.
Verify security evidence and scope
Assess confidentiality, integrity, and availability across the service and its data. Request current evidence whose scope matches the product, edition, region, interfaces, and configuration you will use.
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Evidence to request
- An independent assessment or audit report covering the relevant service, locations, systems, and review period.
- A description of exclusions, exceptions, control gaps, open findings, remediation dates, and management responses.
- Policies or summaries for identity and access management, privileged access, encryption, key handling, tenant separation, secure development, vulnerability management, logging, backup, recovery, and incident response.
- A current architecture and data-flow diagram showing model endpoints, storage, support access, subprocessors, and administrative paths.
- Penetration-test scope and remediation summary, with sensitive details exchanged through an appropriate secure process.
A certificate or audit report is evidence about a defined scope and period. It does not prove that every feature, deployment setting, region, or AI behavior is safe. Compare the report’s boundaries with the service you will buy. An assessment that excludes the model endpoint, customer-facing API, support environment, or selected region may leave your principal exposure unexamined.
Check conventional controls
- Identity and access: Ask how administrators authenticate, whether strong multifactor authentication is required, how privileges are approved and reviewed, and how access is revoked.
- Isolation: Ask how tenants, projects, indexes, files, prompts, outputs, and logs are separated and how isolation is tested.
- Encryption and keys: Identify encryption in transit and at rest, key ownership and rotation, backup protection, and procedures for a suspected key compromise.
- Secure development: Review code review, dependency management, secrets handling, security testing, release approval, and vulnerability disclosure processes.
- Logging: Confirm which administrative, authentication, data-access, model, and configuration events are recorded; how long logs are kept; and how customers can obtain relevant records.
- Backup and recovery: Ask what is backed up, how restoration is tested, and how backup copies are protected from unauthorized access or ransomware.
- Subcontractors: Identify each material provider, its function, location, access, and change-notification process.
Ask about AI-specific threats
Ordinary infrastructure security remains necessary, but AI systems add threats that should be tested explicitly. Ask how the vendor detects and mitigates:
- Evasion: Inputs deliberately changed to bypass classifiers, safeguards, or detection rules.
- Data poisoning: Malicious or low-quality training, tuning, retrieval, or feedback data that changes behavior.
- Model extraction: Repeated queries or other techniques used to reproduce model behavior or proprietary information.
- Training-data and intellectual-property exposure: Prompts, outputs, retrieved material, or endpoint behavior revealing confidential data or protected content.
- Prompt and tool abuse: Instructions that cause an agent or integration to disclose data, take an unauthorized action, or bypass policy.
Request threat models, test methods, representative findings, mitigations, residual risk, and customer responsibilities. Ask what happens when a safeguard fails and how quickly the vendor can disable, roll back, or constrain a problematic model or feature.
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Trace data and establish privacy terms
Follow every category of information through collection, processing, storage, access, sharing, deletion, and possible reuse. A privacy notice alone rarely answers the operational questions procurement and security teams need.
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Build a data-flow inventory
For each category, ask:
- Why is it collected, and is each field necessary for the intended function?
- Is it retained in prompts, uploaded files, indexes, telemetry, feedback systems, logs, backups, abuse-monitoring systems, or support tickets?
- Is it used to train, fine-tune, evaluate, or otherwise improve a model? Is that use enabled by default, optional, or prohibited by contract?
- Who can access it, including vendor staff, support personnel, subprocessors, model providers, and other customers?
- Where is it processed and stored, including backup and disaster-recovery locations?
- How are export, correction, deletion, and account closure handled, and when are copies removed from backups and derived systems?
Classify prompts, files, identifiers, telemetry, feedback, logs, and derived data separately. The answer for a customer’s uploaded file may differ from the answer for an anonymized usage metric or a model-generated embedding. Ask whether minimization, de-identification, aggregation, masking, access controls, or other privacy-enhancing methods are appropriate for each flow.
Put the important promises in the contract
- Permitted processing purposes and a clear prohibition on unapproved secondary use or training.
- Data categories, retention periods, deletion triggers, backup treatment, and evidence of deletion.
- Processing and storage locations, transfer mechanisms where relevant, and restrictions on changing regions.
- Subprocessor approval or notice, access limits, and flow-down obligations.
- Security and privacy incident notice, cooperation, preservation of evidence, and remediation duties.
- Customer access, export, audit or evidence rights proportionate to risk, and termination assistance.
Which legal duties apply depends on the data, parties, use, and jurisdictions. Have counsel assess privacy, sector, employment, consumer, intellectual-property, and AI-specific requirements for the actual deployment; NIST guidance does not determine contract enforceability or provide a universal legal answer.
Test performance for the job you will perform
Reliability is contextual. NIST’s official AI RMF excerpt quotes ISO/IEC TS 5723:2022: “Reliability is defined in the same standard as the ‘ability of an item to perform as required, without failure, for a given time interval, under given conditions’ (Source: iso/iec ts 5723:2022).” A broad benchmark or an impressive demonstration does not establish performance on your data, users, languages, workflows, or consequences.
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Demand testable evidence
- Test methodology, dataset provenance, sampling, labeling instructions, exclusion criteria, and evaluation date.
- Results for the tasks, languages, input formats, operating conditions, and user populations relevant to your deployment.
- Error types, false-positive and false-negative patterns where applicable, confidence behavior, known limitations, and examples of severe failures.
- Robustness to foreseeable distribution changes, malformed input, adversarial prompts, misuse, outages, and dependency failures.
- Performance differences across relevant groups and a process for investigating harmful bias.
- Any human-review assumptions, abstention behavior, rate limits, and operational constraints used to obtain the reported results.
Set acceptance thresholds before a pilot. Define which errors are tolerable, which require escalation, and which disqualify the system. For consequential uses, retain a human review path with authority to override or stop an action. NIST states that “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.”
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Design a useful pilot
- Use a representative, lawfully handled sample that reflects real inputs and difficult edge cases.
- Run the vendor’s system against a known reference set and record outputs, latency, failures, abstentions, and reviewer decisions.
- Measure quality separately for materially different populations, languages, input types, and operating conditions.
- Have domain reviewers classify severity, not just correctness; a rare unsafe error may matter more than many harmless mistakes.
- Test degraded modes: unavailable model, failed integration, stale retrieval data, rate limiting, malformed input, and a required manual handoff.
- Document acceptance results, unresolved limitations, compensating controls, and the owner who approves residual risk.
Assess change management and monitoring
Model behavior can change without a traditional application release. Ask what changes to models, prompts, system instructions, training or retrieval data, safety filters, dependencies, pricing tiers, regions, or subprocessors trigger customer notice, re-evaluation, or consent.
Require a version or release identifier where feasible, a description of material changes, rollback or pinning options, and a channel for reporting harmful behavior. Establish post-launch measures for quality, drift, incidents, complaints, bias indicators, latency, availability, and policy violations. Set review thresholds that trigger investigation, retraining, a configuration change, human-only operation, or suspension.
Assign an internal owner and a review cadence. At each review, compare the approved use with the current use, data flows, model and service versions, supplier evidence, open findings, incidents, performance results, and legal requirements. Renew approval only after confirming that the risk context has not materially changed.
Verify resilience, incident handling, and exit
Service commitments
Request availability commitments, support hours, severity definitions, response and restoration targets, escalation contacts, maintenance notice, recovery-point and recovery-time objectives, backup-restoration evidence, and any negotiated remedies. Ensure commitments cover the components your workflow actually depends on, including model endpoints, retrieval stores, moderation services, and critical integrations.
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Failure and incident scenarios
Walk through what happens if the vendor, model, integration, identity provider, or third-party data source fails. Ask who declares an incident, who communicates with your team, what information is preserved, how customers receive indicators of compromise, and how the service is safely restored. 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.
Continuity and exit
- Define a manual or alternate workflow for the failures that could cause material harm.
- Specify export formats, included metadata, portability of prompts, files, configurations, indexes, evaluation records, and audit logs.
- Set deletion obligations for production, caches, logs, backups, derived data, and subprocessors after termination.
- Negotiate transition assistance, notice periods, termination rights, and continuity support where a sudden shutdown would create unacceptable risk.
- Test a small export, restoration, and manual fallback before relying on the service for a critical process.
Compare vendors on evidence, not marketing
When evaluating more than one supplier, use the same use-case statement, test set, questionnaire, and contract requirements. Weight the dimensions by impact and exposure; do not declare a universal winner because one supplier has a certificate or a higher headline benchmark.
| Comparison axis | Questions to resolve | Evidence to retain |
|---|---|---|
| Assurance and control scope | Does independent evidence cover the product, region, interfaces, model endpoint, and configuration you will use? What exclusions and findings remain? | Current report or assessment, scope statement, exceptions, remediation plan, architecture diagram |
| Data use and privacy | What is collected, retained, trained on, shared, accessed, located, exported, and deleted? | Data-flow inventory, privacy terms, subprocessor list, deletion procedure, contract clauses |
| Task performance | How does the system perform on your inputs and populations, and how severe are failures? | Methodology, representative test results, error analysis, limitations, pilot records |
| Incident and continuity | Who responds, when are you notified, what support is available, and how do you operate during an outage? | Incident plan, contacts, SLA, recovery objectives, rehearsal or restoration evidence, fallback procedure |
| Change and exit | How are model or dependency changes disclosed and tested, and can you export and delete what you need? | Change-notice terms, version records, retest triggers, export test, deletion confirmation, transition terms |
Contract and procurement checklist
- Identify the exact service, versions, regions, interfaces, integrations, and approved uses.
- Specify data categories, permitted processing, training and secondary-use restrictions, retention, deletion, location, and subprocessors.
- Require appropriate security documentation and notice of material changes to scope, controls, or service architecture.
- Allocate responsibility for security and AI incidents, including notice, cooperation, evidence preservation, investigation, and remediation.
- Define availability, support response, recovery objectives, continuity arrangements, and negotiated remedies where appropriate.
- Require notice and review for model, data, dependency, or service changes that could affect risk or performance.
- Establish audit or evidence rights proportionate to the use case and exposure.
- Set export, deletion, termination, and transition requirements before production deployment.
- Have counsel assess enforceability and applicable privacy, sector, and AI laws in every relevant jurisdiction.
A practical approval record
Keep one decision record that links the use case to evidence and residual risk. It should contain:
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- System boundary, data-flow map, suppliers, subprocessors, integrations, and human decision points.
- Security evidence, scope gaps, AI-threat testing, open findings, and compensating controls.
- Privacy purposes, retention and deletion terms, access model, location, and contractual restrictions.
- Acceptance tests, severe-error examples, fairness or group-performance analysis, and pilot sign-off.
- Availability, recovery, incident, fallback, export, and termination arrangements.
- Monitoring metrics, change-notification triggers, review dates, accountable owners, and conditions for suspension.
This record lets procurement, security, privacy, legal, and governance teams reach a common decision and revisit it when the service or the use changes.
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