Assess an AI vendor against your actual use case, not its marketing claims. Document the data, users, geography, autonomy and consequences of failure; map the laws that apply; demand dated, model-specific evidence; test the service in your own workflows; and make the vendor contractually accountable for incidents, changes and exit. NIST AI RMF and ISO/IEC 42001 can organize that work, but neither replaces use-case testing or binding obligations such as those in the EU AI Act.
What a defensible assessment must establish
A vendor is not “safe” or “compliant” in the abstract. A general-purpose model used to draft internal notes creates a different risk from the same model making benefits decisions, controlling industrial equipment or processing children’s health information. Your assessment should produce an auditable answer to five questions:
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- What system, model version, interface and deployment architecture are you buying?
- Who operates each part of it, and who can be affected by its outputs?
- Which laws, regulations and contractual duties apply in every relevant jurisdiction?
- What evidence shows that safety, security, privacy, fairness, transparency and human oversight work in your configuration?
- Who is responsible when the system changes, fails, leaks data or causes harm, and how can you stop using it?
Start with the maximum plausible harm rather than the vendor’s claimed average performance. A low-error rate can still be unacceptable when one failure can deny a legal right, expose confidential records or trigger a dangerous physical action.
Step 1: Define the system and its risk boundary
Create a written scope record before sending a questionnaire. Use the exact product name, model or model family, version, API or application tier, tenant configuration, connected tools and locations where processing occurs. Record whether the vendor hosts the model, supplies an API, embeds a third-party model or uses subcontractors.
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Describe the intended workflow
- Purpose: state what the system is allowed to do and what it must never do.
- Users: identify operators, reviewers, administrators and external users, including vulnerable groups.
- Affected people: list customers, employees, applicants, patients, students or members of the public who may be evaluated or influenced without directly using the system.
- Data: classify personal, confidential, regulated, copyrighted, biometric, health, financial and security-sensitive data. Note whether prompts, files, logs or feedback may be used for training.
- Autonomy: specify whether outputs are suggestions, automatically published content, ranked recommendations, or actions executed through tools.
- Geography: list the countries where the vendor, infrastructure, users and affected people are located.
- Consequences: describe the worst credible physical, financial, privacy, security, discrimination and fundamental-rights impacts.
Identify the vendor’s legal role
Ask the supplier to identify whether it is acting as an AI model provider, system provider, deployer, distributor, importer or service provider in each jurisdiction. The answer can change when you fine-tune a model, combine it with your own decision logic or place it in a regulated product. Preserve the vendor’s written classification and your reasons for accepting or rejecting it.
How the EU AI Act changes the assessment
For EU exposure, classify the use rather than relying on a vendor’s “low-risk” label. The European Commission describes high-risk systems by their potential effects on health, safety or fundamental rights. Your screening should ask whether the use is prohibited, subject to transparency duties, high-risk, or connected to a general-purpose AI model.
Prohibited, transparency-regulated and high-risk uses
A prohibited use cannot be made acceptable by adding a disclaimer or a human reviewer. For a transparency-regulated use, identify what users and affected people must be told and when they must be told it. For a high-risk system, map the applicable obligations to the provider, deployer and other parties, then verify that the controls operate in your deployment.
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Do not assume every system in a sensitive sector is automatically high-risk. The classification depends on the specific use, product function and legal scope. Conversely, a system that appears to be a harmless chatbot can become high-risk when it ranks people, influences access to essential services or controls safety-related equipment.
General-purpose AI obligations
European Commission materials state that obligations for general-purpose AI providers began applying on 2 August 2025, with duties varying by model and provider category. Ask which category the model falls into, which obligations the vendor says it meets, and what documentation supports that position. If you deploy the model inside a separate high-risk system, assess that integrated system independently.
The Commission’s current enforcement timeline also states 2 December 2027 as an application date for certain rules concerning high-risk areas. Dates and transitional arrangements can change, so record the legal source and the date on which you checked it rather than treating a calendar entry as a permanent exemption.
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Step 2: Request a versioned evidence pack
Require documents that identify the product, model version, test period, population, environments, limitations and author. A generic corporate policy or undated slide deck is not evidence about your tenant.
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|---|---|---|
| Intended use | System description, model card or equivalent, prohibited uses and known limitations | Confirm that your workflow, languages, users and connected tools are within scope. |
| Risk and impact | Risk assessment, fundamental-rights or impact assessment, hazard analysis and residual-risk register | Check assumptions, severity ratings, mitigations, owners and open findings. |
| Data provenance | Training-data sources and licensing approach, customer-data handling, retention and deletion terms | Verify that your data is excluded from training when promised and can be deleted on schedule. |
| Evaluation | Accuracy, robustness, safety, bias, privacy and red-team reports | Inspect test population, methodology, thresholds, failures and whether results cover your model version. |
| Security | Architecture diagrams, access controls, encryption, vulnerability management and independent assurance reports | Look for tenant isolation, key management, privileged access review and remediation dates. |
| Privacy | Processing description, retention schedule, subprocessors, transfer mechanisms and data-subject support | Match the document to your data classes and jurisdictions, not merely to the vendor’s standard service. |
| Human oversight | Reviewer workflow, escalation rules, override controls and training materials | Test whether a reviewer can understand, pause, reverse and document an output or action. |
| Operations | Monitoring dashboards, incident history, change logs, business-continuity plan and recovery tests | Check alert thresholds, response times, unresolved incidents and evidence of restoration exercises. |
| Supply chain | Subprocessor and infrastructure-provider list, locations and responsibility matrix | Identify parties that can access data or change the service and how you are notified of changes. |
| Regulatory interaction | Relevant regulator correspondence, conformity or registration records and audit findings | Check scope, dates, exclusions and whether the record concerns this product rather than another service. |
For every item, record who authored it, when it was issued, the exact model and configuration covered, the test population, exceptions, corrective actions and expiry or review date. A certificate without scope and exclusions is not proof that your implementation is covered.
What “safe” should mean in the test plan
NIST’s trustworthiness characteristics are a useful checklist: validity and reliability; safety; security and resilience; accountability and transparency; explainability and interpretability; privacy enhancement; and fairness with harmful bias managed. Treat these as test dimensions, not marketing adjectives.
Build representative scenarios
Use real, de-identified cases and deliberately difficult cases. Include multilingual inputs, ambiguous instructions, missing fields, extreme values, adversarial prompts, prompt-injection attempts, attempts to retrieve another tenant’s data, and workflows in which a confident-looking error would cause the greatest harm. Test both normal outputs and tool calls or downstream actions.
Set pass/fail criteria before testing
- Define acceptable error, abstention and escalation rates for each task.
- Set a zero-tolerance rule where any disclosure, prohibited action or unreviewed high-impact decision is unacceptable.
- Specify fairness measures and minimum sample sizes for relevant groups; document where a metric is statistically unreliable.
- Define maximum time to detect, contain, notify and remediate an incident.
- Require a human override that is technically available, adequately staffed and not penalized by workflow design.
Test the complete lifecycle
NIST says trustworthiness considerations should be addressed during pre-design, design and development, deployment, use, and test and evaluation. Run acceptance tests before launch, monitor production behavior, and repeat the tests after a material model, data, policy, prompt, tool, infrastructure or geography change. Keep the original outputs, test data version, evaluator and decision for each release.
Security and privacy checks that catch practical failures
Data isolation and leakage
Verify that one customer’s prompts, files, embeddings, logs and support records cannot be retrieved by another customer. Attempt cross-tenant access with ordinary and administrator accounts. Check whether support staff, plug-ins, analytics tools or model-training pipelines can access content.
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Prompt injection and tool abuse
Where the system reads webpages, email, documents or code, insert instructions that try to override policy, exfiltrate secrets or trigger an unauthorized transaction. Confirm that untrusted content is separated from system instructions, tools use least privilege, high-impact actions require confirmation, and every call is logged with an attributable identity.
Retention, deletion and transfers
Ask exactly how long prompts, outputs, safety logs, backups and abuse-monitoring records remain available. Test a deletion request and verify deletion from searchable stores and the stated backup process. Map processing and support locations to your transfer and confidentiality obligations; do not infer location from the vendor’s headquarters.
Fairness, transparency and human oversight
Request subgroup results for the people your workflow affects, not only an overall accuracy number. Examine false-positive and false-negative differences, refusal patterns, language quality and accessibility. If the vendor cannot disclose sensitive attributes used for evaluation, ask how it measured disparate effects and what limitations follow.
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Frameworks: useful structure, not automatic compliance
| Reference | What it provides | What it does not prove |
|---|---|---|
| NIST AI RMF 1.0 | Voluntary lifecycle guidance for managing AI risk. It was released on 26 January 2023 and organizes work around governing, mapping, measuring and managing risk. | It is not a law, certification or guarantee that a vendor’s controls work in your use case. |
| ISO/IEC 42001 | An AI management-system standard that can structure policies, responsibilities, continual improvement and audit activities. | A certificate does not cover every product, location, model, subcontractor or deployment configuration, and it does not replace performance testing. |
| EU AI Act | Binding obligations within its scope, with requirements that vary by prohibited use, transparency category, high-risk system and general-purpose AI provider or model category. | Compliance with a voluntary framework or a certificate does not satisfy an obligation unless the applicable law recognizes the specific evidence and all required controls are met. |
NIST’s own description is explicit: “The NIST AI Risk Management Framework (AI RMF) is intended for voluntary use and to improve the ability to incorporate trustworthiness considerations into the design, development, use, and evaluation of AI products, services, and systems.” Its Govern guidance says, “Legal and regulatory requirements involving AI are understood, managed, and documented.” Those statements support a disciplined process; they do not turn alignment into a legal safe harbor.
The NIST AI RMF Generative AI Profile, NIST-AI-600-1, was released on 26 July 2024. Use it to sharpen generative-AI questions, while keeping the underlying legal analysis and contract responsibilities specific to your service.
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Step 3: Convert evidence into contractual accountability
Put the controls you rely on into the agreement, order form, data-processing terms and security schedule. Assign each obligation to a named party; “the customer is responsible for compliance” is incomplete when the vendor controls the model, logs or infrastructure.
- Permitted use and data: define approved purposes, ownership of inputs and outputs, confidentiality, training restrictions and rights to remove data.
- Retention and deletion: state periods, deletion procedures, backup treatment and evidence supplied after completion.
- Subprocessors: require a current list, locations, equivalent controls and advance notice with an objection or termination mechanism.
- Security: specify access control, encryption, vulnerability disclosure, penetration testing, logging and remediation commitments.
- Incidents: set notification deadlines, required facts, update cadence, cooperation, preservation of evidence and regulator support.
- Model changes: require notice before material changes to model, training data, safety policy, tools, hosting or performance characteristics, with a retest or exit right.
- Human oversight: define who reviews outputs, who can stop automated actions and who handles complaints or appeals.
- Audit and evidence: secure reasonable audit rights, independent reports, test results and access to records needed to verify compliance.
- Service continuity: cover uptime, rate limits, disaster recovery, export formats, portability, fallback procedures and termination assistance.
- Liability: align indemnities, caps and exclusions with foreseeable privacy, security, intellectual-property, discrimination and safety exposure.
Step 4: Operate a continuous monitoring program
Approval is a starting point. Establish a review interval and event-driven reviews for model upgrades, new training data, new subprocessors, incidents, regulatory changes, a new country, a new user group or increased autonomy.
Maintain an evidence register
Link every material claim to a document, owner, issue date, scope, expiry date and retest result. Mark evidence as current, expiring, superseded or rejected. Require the vendor to identify changes that could invalidate a prior test.
Track operational indicators
- Reliability, abstention and escalation rates by task and relevant group.
- Unsafe, discriminatory, misleading or policy-violating outputs.
- Security incidents, prompt-injection attempts, access-control failures and unresolved vulnerabilities.
- Privacy complaints, deletion failures, unauthorized retention and data-transfer exceptions.
- Drift in inputs, outputs, workload, language mix or user population.
- Human-review completion, override frequency and response times.
- Open findings, corrective-action age and evidence approaching expiry.
Define thresholds that trigger containment, rollback, additional review or suspension. A dashboard without an owner and response playbook is reporting, not control.
How to compare vendors consistently
Use the same scenarios, evidence requests and scoring scale for every supplier. Weight the dimensions according to harm: a clinical or employment use should give more weight to rights, safety and oversight than to cosmetic features.
| Comparison axis | Questions to score |
|---|---|
| Use-case and regulatory fit | Does the vendor understand the intended purpose, affected people, jurisdictions and provider/deployer roles? |
| Independent evidence | Are reports current, product-specific, independently reviewed and clear about limitations? |
| Safety and reliability | Does performance meet your pre-set thresholds under normal, edge and adversarial conditions? |
| Security and privacy | Are isolation, access, retention, deletion, transfer and incident controls demonstrable? |
| Fairness, transparency and oversight | Can people understand, challenge, override and document consequential outputs? |
| Monitoring and response | Will you receive timely alerts, useful telemetry and cooperation during an incident? |
| Contract and exit | Are audit, change notice, liability, portability, fallback and termination rights workable? |
| Total operational burden | What staffing, review, integration, testing and evidence-maintenance work remains with you? |
Do not award a high score for a missing value. Record “not established” and treat it as a decision risk until the vendor supplies verifiable evidence or you reduce the system’s scope.
Common assessment mistakes and fixes
Accepting a questionnaire as proof
A questionnaire records an assertion. Validate important answers with documents, demonstrations, technical tests, contractual terms and, where appropriate, an independent assessment.
Relying on an ISO certificate or NIST alignment badge
Check certificate dates, audited locations, exclusions, covered products and the certification body. Then test your model version and tenant configuration. Framework alignment is useful evidence, not a substitute for legal analysis or operational proof.
Testing only average accuracy
Measure harmful failure modes, subgroup effects, privacy leakage, adversarial behavior, tool authorization and human override. A high average score can hide a catastrophic tail.
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Leaving exit until an incident
Confirm export formats, deletion evidence, replacement procedures, retained logs, rate-limit contingencies and the people who will operate a fallback. If you cannot stop safely, your supplier risk is higher than the initial assessment suggests.
A practical approval checklist
- Write the use-case, data, user, geography, autonomy and maximum-harm record.
- Obtain the vendor’s role and legal classification for each relevant jurisdiction.
- Screen the use under applicable AI, privacy, sector, employment, consumer, safety and intellectual-property rules.
- Collect dated, versioned evidence and log scope, assumptions, exceptions and owners.
- Run representative, adversarial and subgroup tests with pre-defined thresholds.
- Review human oversight, appeal, incident and business-continuity procedures in a live demonstration.
- Resolve high-severity findings or prohibit the use; document residual risk and approval authority.
- Sign terms covering data, security, subprocessors, incidents, changes, audit, liability and exit.
- Launch with monitoring, named owners, alert thresholds and a rollback or suspension plan.
- Reassess on the scheduled date and whenever a trigger event occurs.
Decision rule
Approve a vendor only when the evidence is specific to the system you will operate, the tests meet thresholds that reflect the consequences of failure, legal duties are assigned to the right parties, and you can detect, contain and exit a failure. If a critical claim remains unverified, narrow the use, add controls, obtain stronger evidence or choose another supplier. That standard remains necessary whether the vendor cites NIST AI RMF, ISO/IEC 42001, an EU AI Act assessment or none of them.
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