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The safest way to assess an AI vendor is to evaluate the proposed use case, data flows, system dependencies, evidence of controls, and ongoing obligations as one risk decision. A security questionnaire or compliance badge alone cannot establish that a particular AI deployment is safe or lawful. Define what the system will do, identify everyone and everything that can access your data, test the vendor’s claims against your requirements, and contract for monitoring, incident response, fallback, and exit.
NIST’s AI Risk Management Framework (AI RMF) provides a useful structure—“govern, map, measure, and manage AI risks”—but it is voluntary guidance, not a universal legal-compliance certificate. Your final decision must reflect your organization’s risk tolerance, jurisdiction, sector rules, contracts, and the people affected by the system.
What an AI-vendor assessment must establish
An assessment should answer five practical questions before procurement or production use:
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- Purpose: What decision, workflow, or service will the AI support, and who could be affected?
- Information: What data enters the system, what the system generates, where data is stored, and who can access it?
- Behavior: How accurate, reliable, secure, fair, and explainable is the system in the conditions in which you will use it?
- Accountability: Which party is responsible for controls, incidents, changes, errors, and legal obligations?
- Continuity: What happens if the service is unavailable, compromised, materially changed, or no longer acceptable?
Do not begin by comparing marketing claims. First document the intended deployment and the consequences of failure. A model used to draft low-risk internal text requires a different review from one that influences employment, credit, health, safety, access to essential services, or the handling of confidential customer records.
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The AI RMF is designed for risks across development, deployment, use, and evaluation. Its four functions are continuous lifecycle activities rather than a one-time, linear checklist. Use them to organize your work, then add the legal, contractual, security, and privacy requirements that apply to your organization.
Step 1: Define the use case, affected people, and risk tolerance
Write a use-case statement
Record the intended purpose in specific terms. Include the business owner, users, deployment environment, model-assisted task, expected benefit, and prohibited uses. “Use an AI assistant” is too vague; “summarize support tickets for a human agent using only approved customer records” is assessable.
- What inputs will the system receive?
- What outputs will it produce?
- Will a person review every output before action?
- Can the system call tools, send messages, modify records, execute code, or make decisions?
- What populations, customers, employees, or members of the public could be affected?
- What is the worst credible result of an error, misuse, outage, or data disclosure?
Set a risk tier before vendor selection
Create a written risk tier using consequences rather than the vendor’s product category. A useful internal scheme is:
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| Tier | Typical use | Assessment emphasis |
|---|---|---|
| Low | Non-sensitive drafting or brainstorming with human review | Basic data controls, acceptable-use restrictions, service availability, and output-quality checks |
| Moderate | Operational recommendations, internal knowledge retrieval, or processing confidential business data | Identity and access controls, retention, isolation, evaluation in your context, auditability, incident response, and supplier dependencies |
| High | Systems affecting rights, safety, livelihood, health, regulated records, or external actions | Strict data governance, human oversight, independent testing, fairness and impact analysis, resilience, fallback, contractual rights, and executive approval |
This table is a planning aid, not a legal classification. Your organization should define thresholds and obtain specialist advice where sector or jurisdictional rules apply. Document the risk you are willing to accept before reviewing vendor proposals; otherwise a persuasive demonstration can silently reset your standard.
Define measurable acceptance criteria
Translate the use case into requirements that can be tested. Examples include maximum tolerable error rates for specified tasks, mandatory human review, response-time targets, allowed data locations, retention limits, encryption requirements, logging, accessibility, language coverage, and a deadline for notifying you of incidents or material changes. Identify which requirements are mandatory and which are negotiable.
Step 2: Map the complete AI system and supply chain
Inventory components, not just the product name
Ask the vendor to describe every component relevant to your deployment:
- The model or models, including versions, routing logic, and fallback models.
- Application interfaces, software development kits, plug-ins, agents, and administrative consoles.
- Retrieval, fine-tuning, embedding, moderation, monitoring, and evaluation services.
- Data sources, preprocessing, training or tuning data, and customer-provided content.
- Cloud, hosting, support, analytics, payment, identity, and other subprocessors.
- Tools the system can call, such as search, code execution, databases, email, or business applications.
Request a current architecture or data-flow diagram. It should show where your content enters, how it moves between components, where it is stored, who can access it, and when it is deleted. Ask how the vendor will notify you when a component, subprocessor, model, hosting region, or data flow changes.
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Build a third-party inventory listing each supplier with access to your data or operating environment. For each supplier, record its role, data types, access method, geographic location where stated, contractual relationship, security contact, and dependency criticality. Include suppliers that the primary vendor does not market prominently; an embedded model, telemetry service, support platform, or content filter can create a material dependency.
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NIST’s Generative AI Profile (AI 600-1, published July 26, 2024) recommends use-case-based supplier assessment and inventorying third parties with access to organizational content. Apply that advice to integrations as well as the core model.
Ask about provenance and supply-chain exposure
For models, datasets, code, and services, ask how provenance is recorded and how vulnerabilities are handled. Determine whether the vendor can identify affected versions, revoke a compromised component, and provide an alternative. NIST SP 1326, finalized July 8, 2026, offers an ICT-supplier due-diligence lens covering foreign ownership, control, or influence; provenance; resilience; foundational cyber practices; and supply-chain tiers. It can supplement an AI review where supplier concentration or geopolitical exposure matters.
Step 3: Evaluate security and resilience
Check preventive controls
Request evidence that the service protects confidentiality, integrity, and availability for your deployment. Your review should cover:
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- Tenant isolation and controls preventing one customer’s prompts, files, retrieval indexes, or outputs from reaching another.
- Encryption in transit and at rest, key-management options, and administrative access to keys where relevant.
- Network boundaries, private connectivity, allowlists, logging, and restrictions on outbound tool calls.
- Secure development, code review, dependency management, vulnerability scanning, penetration testing, and remediation targets.
- Protection against prompt injection, data exfiltration, malicious files, abuse of tools, denial of service, and unauthorized model or configuration changes.
Ask for the scope, date, methods, and limitations of each assessment. A policy describing multifactor authentication is not the same evidence as records showing that the control is enabled for the environment you will use.
Evaluate resilience and recovery
Find out how the vendor handles regional outages, model failures, corrupted indexes, ransomware, lost credentials, and dependency failures. Ask for recovery objectives, backup and restore practices, service-status communication, disaster-recovery testing, and customer responsibilities. For a high-risk workflow, establish an operational fallback that does not depend on the unavailable AI service.
Assess vulnerability and incident handling
Obtain the vulnerability-reporting channel, severity definitions, response targets, customer notification process, and post-incident reporting commitments. Clarify whether you will receive indicators of compromise, affected data categories, containment actions, root-cause findings, and remediation status. Define how quickly the vendor must notify you and which events qualify as a security incident, privacy incident, material service change, or model-safety event.
Step 4: Examine privacy and data use
Classify every data element
List the information the system may receive, including prompts, uploaded files, retrieved records, feedback, telemetry, support tickets, account details, and generated outputs. Mark confidential, personal, sensitive, regulated, proprietary, and publicly available data separately. Determine whether the system can infer sensitive information even when the input appears harmless.
Get precise answers about retention and secondary use
Ask, in writing:
- Is customer content used to train, fine-tune, evaluate, or improve any model?
- Are prompts and outputs retained, for how long, and in which systems?
- Can administrators disable retention or delete content and derived artifacts?
- Who can access content for support, abuse review, quality assurance, or legal requests?
- Are backups, logs, embeddings, caches, and human-review queues covered by deletion commitments?
- How are data-subject requests, correction, access, restriction, and deletion handled?
- Can data remain in required regions, and what happens during support or disaster recovery?
Do not treat “we do not sell data” as a complete answer. Your decision depends on the exact processing purposes, retention periods, access paths, subprocessors, transfer mechanisms, and contractual terms for your data.
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Connect privacy analysis to the deployment
Assess privacy risk in context. A service that is acceptable for anonymized test text may be unsuitable for identifiable case records. Decide whether de-identification, minimization, redaction, segregation, or a private retrieval layer is required before data reaches the vendor. Test the controls with realistic but approved data, and document residual risk where re-identification, memorization, inference, or accidental disclosure remains possible.
Step 5: Test trustworthiness, quality, and safety
Require use-case-specific evaluation
Ask for evaluation results that match your language, users, data, workflows, and failure costs. Generic benchmark scores rarely establish performance in your environment. Request test sets, sampling methods, metrics, confidence intervals or uncertainty information where available, known failure modes, and conditions outside the tested range.
| Domain | Questions to ask | Evidence to request |
|---|---|---|
| Validity and reliability | Does the system produce useful, consistent results for the defined task? | Task-specific test results, error analysis, version history, and monitoring plan |
| Security and resilience | Can attackers manipulate inputs, extract data, abuse tools, or disrupt service? | Threat model, red-team or penetration-test scope, mitigations, and recovery exercises |
| Privacy | Can content leak, be retained unexpectedly, or reveal sensitive inferences? | Privacy assessment, retention controls, access logs, deletion tests, and incident history |
| Fairness and impact | Do error rates or harmful outputs differ across relevant people or groups? | Subgroup evaluation, limitations, mitigations, and deployment-specific impact analysis |
| Transparency and accountability | Can users understand limitations and know who is responsible for decisions? | System documentation, user notices, escalation paths, and responsibility matrix |
| Intellectual property and legal risk | What rights apply to inputs, outputs, training sources, and third-party components? | Contract terms, provenance information, restrictions, indemnity language where offered, and legal review |
NIST’s AI RMF and GenAI Profile treat these domains as related dimensions of trustworthy AI. They are not a universal checklist that replaces analysis of your particular use, law, or contract.
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Run a controlled pilot using representative, approved data. Include ambiguous requests, malformed files, adversarial prompts, attempts to retrieve another user’s information, unsupported languages, long context, unavailable tools, and conflicting source documents. Record both the model answer and the surrounding application behavior: permissions, logs, citations, refusal handling, escalation, and human review.
Define stop conditions before testing. Stop or redesign the deployment if the system exposes another person’s data, performs an unauthorized external action, cannot be constrained to approved sources, or produces an error pattern whose consequences exceed your risk tolerance.
Step 6: Verify evidence instead of accepting assurances
Use an evidence hierarchy
Evaluate each claim against the strength and relevance of its evidence:
- Direct demonstration: You observe the control or test it in the proposed configuration.
- Detailed assessment results: A dated report explains scope, method, findings, limitations, and remediation.
- Operational records: Logs, change records, incident metrics, recovery exercises, or deletion-test results support that the control operates.
- Independent assurance: A qualified third party reports on a defined scope and period.
- Policy or representation: The vendor states what it intends to do, without operating evidence.
The lower levels can still inform a decision, but they should not be treated as equivalent. Ask whether evidence covers the exact service, region, model, tenant configuration, and period you will use. Check exceptions and complementary customer controls.
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NIST assessment guidance asks whether controls are implemented correctly, operate as intended, and produce the desired security and privacy outcomes. Use those three questions for every important claim. A documented deletion policy may be correctly written yet fail to remove backups or embeddings; a monitoring system may operate yet fail to detect the attack that matters to your workflow.
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Record gaps and compensating controls
Maintain a register with the requirement, evidence reviewed, result, owner, due date, residual risk, and compensating control. Examples of compensating controls include excluding sensitive data, requiring human approval, disabling tool execution, restricting access by role, routing through a redaction service, or using a non-AI fallback. Obtain formal acceptance for risks that remain unresolved; do not hide them in procurement notes.
Step 7: Compare vendors on decision-relevant criteria
When several vendors remain viable, use the same questions and scoring scale for each. Weight the criteria according to the consequences of failure, not according to how polished a vendor’s documentation looks.
| Criterion | What to compare | Warning signs |
|---|---|---|
| Use-case fit | Supported tasks, languages, integrations, performance, and operational limits | Benchmark claims unrelated to your task; unclear unsupported-use boundaries |
| Data handling | Input use, retention, deletion, region, access, training, and subprocessors | Vague answers; controls available only on an unpriced or unavailable edition |
| Security | Identity, isolation, encryption, logging, secure development, and vulnerability response | No scope or date for testing; customer cannot review material findings |
| Evaluation quality | Methods, representative data, subgroup analysis, limitations, and release comparisons | Only aggregate benchmark scores; no failure analysis |
| Transparency | Architecture, model changes, documentation, notices, and accountability | Unannounced changes or inability to identify the deployed version |
| Continuity | Availability, recovery, fallback, portability, and exit support | No usable export, no tested recovery, or no notice before discontinuation |
| Legal and supply chain | Rights, provenance, jurisdiction, supplier access, and contractual remedies | Unclear ownership or dependency chain; terms override stated controls |
Use a simple scale such as meets, partially meets, does not meet, or not evidenced. Keep narrative notes for why a score was assigned. A high total score should not override a failed mandatory requirement.
Step 8: Put governance and protections into the contract
Define responsibilities and permitted use
Contracts should identify the service, approved use cases, data categories, roles, security requirements, privacy processing terms, subprocessors, regions, and customer obligations. State who owns configuration, access reviews, output validation, user training, regulatory notices, and decisions made with AI assistance.
Control changes
Require notice of material changes to models, training or data-use practices, subprocessors, hosting regions, interfaces, safety controls, and service limits. Set a review trigger for changes that could alter accuracy, privacy, security, or legal exposure. Preserve the ability to test a new version before it handles production data.
Set incident, audit, and cooperation terms
Specify notification timelines, information to be supplied, cooperation during investigation, evidence preservation, remediation updates, and communication responsibilities. Where appropriate, negotiate assessment rights or equivalent independent evidence. NIST’s GenAI Profile recommends contract clauses that allow evaluation of third-party generative-AI processes and standards.
Plan exit and portability
Define how to export prompts, outputs, configuration, indexes, logs, and other records needed for continuity or legal obligations. State deletion timelines, certification or evidence of deletion, transition assistance, and the treatment of backups. Test the export and a replacement workflow before the service becomes business-critical.
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Step 9: Monitor after approval
Vendor approval is the beginning of oversight, not the end. Assign owners and review at a frequency proportionate to risk.
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- Service and security: outages, vulnerabilities, incidents, access anomalies, dependency changes, and recovery performance.
- Model behavior: accuracy, refusal patterns, hallucinations, drift, harmful outputs, subgroup differences, and user complaints.
- Data governance: retention, deletion, access reviews, new data sources, exports, and policy exceptions.
- Supplier status: subprocessors, ownership or control changes, geographic exposure, financial stability, and concentration risk.
- Change management: model versions, prompts, retrieval indexes, tools, guardrails, and application code.
Set thresholds that trigger investigation, suspension, rollback, or a new assessment. Keep an inventory of active AI systems and review whether each still serves its approved purpose. NIST’s AI RMF Manage function is continuous because risks and operating conditions change.
Failure scenarios and practical responses
The vendor will not disclose subprocessors
Treat the dependency as unknown rather than assuming it is harmless. Ask for a role-based list, notification process, access limitations, and equivalent independent evidence. If the vendor cannot provide enough information for your risk tier, restrict data, use a different architecture, or reject the deployment.
The vendor provides a certification but no use-case evidence
Record the assurance artifact as evidence of a defined scope only. Request performance, privacy, security, and resilience testing for your configuration. A framework alignment or report does not prove that every use case is safe or legally compliant.
Performance is acceptable in a demo but poor in production
Compare production inputs with the evaluation set, check retrieval quality and version changes, and analyze errors by task and affected group. Add human review, constrain the workflow, improve source data, or stop the use case if the residual risk remains above your threshold.
The service changes models without warning
Use contractual change-notice rights, pin versions where available, maintain regression tests, and require re-approval for material changes. Keep a rollback or alternate provider when continuity matters.
An incident occurs and the service is unavailable
Activate the documented fallback, preserve logs and evidence, notify internal stakeholders according to your incident plan, and restrict further data transmission if compromise is suspected. After restoration, compare the vendor’s account with your records, identify affected data and decisions, and update controls before resuming normal use.
How to use NIST guidance without overstating it
The NIST AI RMF 1.0 was developed collaboratively over 18 months with more than 240 contributing organizations. NIST’s AI Resource Center states that a revision is in progress. The framework is voluntary and does not, by itself, satisfy every law, regulation, contract, or sector obligation.
Use the AI RMF Core to organize governance, context mapping, measurement, and risk management. Use AI 600-1, the Generative AI Profile published July 26, 2024, for generative-AI-specific procurement and operational actions. Use the AI RMF Playbook’s Manage guidance to apply organizational risk tolerance to third-party AI systems. Use SP 1326, finalized July 8, 2026, when ICT-supplier due diligence requires deeper review of ownership, provenance, resilience, foundational cyber practices, or supply-chain tiers.
These resources improve the quality of questions and evidence review. They do not make a vendor compliant by adoption alone. Confirm the obligations that apply to your role, geography, sector, data, customers, and intended use, and involve privacy, security, procurement, legal, and business owners in proportion to risk.
Quick Recap
A compact approval checklist
- The use case, users, affected people, prohibited uses, and failure consequences are documented.
- Mandatory security, privacy, performance, fairness, legal, and continuity requirements are defined.
- The architecture, data flows, models, tools, subprocessors, and access paths are inventoried.
- Retention, training use, deletion, regions, support access, and data-subject handling are confirmed in writing.
- Security, privacy, resilience, and vulnerability evidence is current, scoped, and relevant to the proposed configuration.
- Use-case testing covers normal, edge, adversarial, and out-of-scope conditions.
- Known limitations, residual risks, compensating controls, and approval owners are recorded.
- Contracts address changes, incidents, assessment rights, cooperation, continuity, portability, and deletion.
- Monitoring metrics, thresholds, review dates, fallback procedures, and decommissioning steps are assigned.
- The final decision is approved by the people accountable for the business, security, privacy, legal, and operational risks.
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