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NIST’s AI Risk Management Framework (AI RMF) and its Generative AI Profile provide voluntary process guidance; they do not certify a supplier or replace statutory analysis. EU AI Act duties likewise depend on the system’s category, your role and where the system is used. The workflow below is designed to produce a defensible decision and an auditable record.
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What an AI vendor risk assessment must establish
A useful assessment answers six questions for one deployment:
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- What purpose will it serve, what decisions or workflows will it influence, and who may be affected?
- What data enters the service, where is it processed, how long is it retained, and can it be used for training or other customers?
- What could go wrong across privacy, security, safety, reliability, fairness, intellectual property and regulatory compliance?
- What evidence supports the vendor’s claims, and which uncertainties remain?
- Who owns each mitigation, who accepts residual risk, and how will changes or incidents trigger a reassessment?
A vendor can present strong policies yet be unsuitable for a particular use. A model that is acceptable for drafting internal text may be inappropriate for eligibility decisions, medical advice, employment screening or other high-impact uses. Score the use case and its data flows, not an abstract “vendor risk” number.
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Step 1: Create a complete system and supplier inventory
Make one inventory record for every purchased AI service, embedded feature and material model dependency. Include the following fields before requesting a price or approving a pilot:
| Inventory field | What to record |
|---|---|
| System identity | Vendor, product or feature name, model name and version when disclosed, account or tenant, business owner and technical owner. |
| Purpose and users | Intended task, prohibited uses, user groups, affected people, decision rights and required human review. |
| Deployment context | Production, pilot or internal experiment; hosting location; integrations; interfaces; automation level; and whether the service can change without a new release. |
| Data and flows | Input and output categories, personal or confidential data, credentials, source documents, destinations, retention, deletion path and cross-border transfers. |
| Third parties | Subprocessors, model providers, hosting providers and other parties with access to organizational content. |
| Governance | Applicable jurisdictions and sectors, your organization’s role, approval status, risk owner, review date and linked evidence. |
NIST’s Generative AI Profile recommends inventories of third parties with access to organizational content and approved service-provider lists. Keep the inventory versioned: preserve the original entry, amendments, and the reason for every material change.
Step 2: Define context, roles and legal scope
Before choosing controls, identify what your organization is doing. You may be a deployer using a supplier’s model, a provider placing an AI system on the market, an importer or distributor, an integrator combining a general-purpose model with your own product, or more than one of these at once. The same service can create different duties for each role.
Map jurisdictions and sector rules
List every place in which the system is offered, operated, hosted or affects people. Add sector requirements that apply to your data or decisions. This article cannot determine local requirements outside the European Union or sector-specific obligations; obtain jurisdiction-specific advice where the use is regulated.
Classify the system for EU use
The EU AI Act uses a risk-based structure. Determine whether the system is prohibited, high-risk, subject to transparency duties, a general-purpose AI (GPAI) model, or outside a relevant category. Then identify the provider, deployer, importer, distributor and any GPAI provider roles. The European Commission’s role-based guidance should be checked against the current consolidated law and applicable annexes.
Commission transparency guidance published 20 July 2026 addresses Article 50 obligations and states that those obligations apply from 2 August 2026. Commission GPAI guidance describes documentation duties for providers placing models on the EU market from 2 August 2025. Those dates do not mean every AI service has the same duty; classification and role control the result.
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Do not treat framework alignment as legal compliance
NIST AI RMF 1.0 is voluntary. ISO/IEC 23894:2023 is guidance for managing AI risk, not a statute or a universal certification requirement. A supplier’s statement that it follows either framework is useful context but does not prove that your deployment satisfies the EU AI Act, privacy law, security law or a sector rule.
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Step 3: Request evidence tied to the actual service
Send a structured request that names the product, model, intended use, data classes, jurisdictions and proposed retention period. Generic marketing material is not a substitute for service-specific evidence.
System behavior and limitations
- Purpose, supported capabilities, excluded uses and known failure modes.
- Model and feature versions, release process, deprecation policy and advance notice for material changes.
- Evaluation results relevant to your language, domain, user population and accuracy or safety threshold.
- Human-oversight expectations, escalation paths and ways to disable automation.
- Output provenance, logging and mechanisms for correcting or deleting generated content.
Data, privacy and intellectual property
- Every input and output category collected, retention period, backup retention and deletion verification.
- Whether prompts, files, feedback or outputs train, fine-tune or improve a shared or customer-specific model.
- Subprocessors, processing locations, transfer mechanisms and access by support personnel.
- Ownership and license terms for inputs, outputs, model weights, feedback and customer content.
- Procedures for data-subject requests, legal holds, subpoenas and deletion at contract exit.
Security and resilience
- Authentication, tenant isolation, authorization boundaries, encryption and privileged-access controls.
- Vulnerability disclosure and remediation targets, penetration testing scope, dependency management and secure development practices.
- Incident detection, notification timing, investigation cooperation and customer communications.
- Backups, recovery objectives, availability commitments, denial-of-service protections and continuity testing.
- Failure behavior, rate limits, abuse controls and a safe fallback when the model or integration is unavailable.
Governance and regulatory support
- Technical, risk and user documentation supplied for your role and intended deployment.
- Monitoring, drift detection, model-change records and customer notification channels.
- Evidence that supports any conformity assessment, registration or transparency statement required in your jurisdiction.
- Contract language allowing evaluation of relevant third-party processes and standards, subject to reasonable confidentiality and security limits.
NIST’s Generative AI Profile specifically emphasizes acquisition due diligence for intellectual property, privacy, security and other risks; ongoing assessment and alerting; and contract clauses that permit evaluation of third-party processes and standards.
Step 4: Verify claims instead of checking policy boxes
For each important claim, record the exact document, version, publication date, scope, test population and limitations. A policy saying “we do not train on customer data” is incomplete unless it identifies the products covered, exceptions, feedback channels, retention of abuse-monitoring data and the contractual commitment.
Use an evidence-quality test
- Relevance: Does the evidence cover the exact product, model, region, tenant type and use case?
- Recency: When was it issued, and what changed since then?
- Independence: Is it a vendor assertion, an internal test, an independent assessment or a regulator-facing document?
- Coverage: Does it address the risk you scored, including exceptions and residual uncertainty?
- Reproducibility: Could another reviewer locate the same evidence and understand the result?
Ask for test methodology and limitations, not only a headline score. A benchmark that excludes your language, domain or adversarial inputs may have little decision value. Record unanswered questions as findings with owners and deadlines rather than silently treating them as low risk.
Step 5: Rate risks with a repeatable method
Use the organization’s established risk scale where one exists. At minimum, rate potential impact and likelihood, then adjust for exposure, detectability, reversibility and the people affected. Consider both direct harm and cascading effects through downstream decisions.
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Trustworthiness dimensions to examine
- Validity and reliability: Does the system perform its stated task consistently in the deployment context?
- Safety: Can outputs cause physical, psychological or operational harm?
- Security and resilience: Can attackers manipulate inputs, extract data or disable the service?
- Accountability and transparency: Can users and affected people understand who is responsible and what the system does?
- Explainability and interpretability: Can reviewers investigate a material output or decision?
- Privacy: Are collection, use, retention, access and deletion lawful and controlled?
- Fairness: Do performance differences or outputs create unjustified disparate effects?
These dimensions reflect NIST’s trustworthiness characteristics. They are prompts for analysis, not a substitute for a legal test.
Document uncertainty and the decision threshold
Write the rationale beside every rating: evidence relied on, assumptions made, unknowns, affected groups and why the risk is acceptable or not. A high-impact use with poor detectability or low reversibility should require stronger evidence and controls than a low-impact, easily corrected use.
Choose one of five decisions
- Approve: Evidence and controls meet the defined threshold.
- Approve with conditions: Use is limited by data, users, geography, human review, volume or time-bound remediation.
- Seek evidence: Pause approval until specified documents or tests arrive.
- Limit or redesign: Remove sensitive inputs, reduce automation or change the purpose.
- Reject: Residual risk exceeds tolerance, contractual protections are unavailable or the use is legally impermissible.
Name the decision owner and the person authorized to accept residual risk. Do not leave acceptance implied by a purchase order or a pilot launch.
Step 6: Convert findings into contract and operating controls
Put material safeguards in the agreement, order form, data-processing terms, security schedule and operating procedures—not only in a questionnaire.
Contract clauses to address
- Approved purposes, prohibited uses and restrictions on sensitive or regulated data.
- Retention, deletion, backups, training or reuse, ownership and intellectual-property indemnity positions.
- Security controls, access boundaries, vulnerability handling and cooperation with assessments.
- Incident notification deadlines, investigation support and evidence preservation.
- Advance notice of model, feature, hosting, subprocessor or data-use changes, with a right to suspend or terminate for material risk.
- Subprocessor disclosure, location changes and flow-down obligations.
- Service levels, continuity commitments, export formats, deletion certificates and exit assistance.
- Cooperation with lawful regulator requests and provision of documentation required for your role.
NIST recommends contract clauses that allow evaluation of third-party GAI processes and standards and contingency processes for failures or incidents in high-risk third-party systems. Make the clause operational: specify notice channels, response times, evidence formats and the owner who decides whether to pause use.
Operating controls after signature
- Restrict access through approved accounts, least privilege and documented administrator roles.
- Use input filtering, redaction or tokenization where sensitive data is unnecessary.
- Require human review for defined output classes and prohibit automatic action when confidence or provenance is insufficient.
- Log prompts, outputs, user, model version, approvals and overrides in a manner consistent with privacy and retention rules.
- Provide staff training on acceptable use, confidential data, hallucination checks, escalation and incident reporting.
- Maintain a tested fallback for critical workflows.
Step 7: Build a versioned compliance evidence file
The evidence file should let an independent reviewer reconstruct what was known when the decision was made. Store it with access controls and an immutable or otherwise tamper-evident history where your records policy requires that.
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Recommended record structure
| Record | Minimum contents |
|---|---|
| System profile | Purpose, users, data flows, integrations, model and version, roles, jurisdictions and risk tier. |
| Evidence register | Document title, issuer, version, date received, URL or repository location, scope, reviewer and review date. |
| Risk register | Risk statement, affected people, impact, likelihood, detectability, reversibility, rationale, treatment and residual rating. |
| Decision memo | Approval option, conditions, assumptions, unresolved issues, decision owner, risk accepter and expiry or review date. |
| Contract map | Risk-to-clause mapping, negotiated exceptions, service-level terms, notice rights and exit provisions. |
| Monitoring log | Model or subprocessor changes, incidents, complaints, tests, alerts, reassessments and resulting decisions. |
NIST’s Generative AI Profile recommends maintaining records of third-party content changes, including sources, timestamps and metadata. Capture the original file as well as a stable reference; a vendor web page can change without preserving the text you reviewed.
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Use a finding format that drives action
For each finding, write: “Because [condition], [risk] may affect [people or asset] through [failure path]. Evidence is [source and date]. We will [mitigation] by [deadline]. [Named owner] will verify it. Residual risk is [rating] and accepted by [authority].” This separates facts, analysis, treatment and acceptance.
Step 8: Monitor and reassess throughout the relationship
Approval is a point in the lifecycle, not a permanent status. Set a review cadence proportionate to use-case risk and event-triggered reassessment.
Events that should trigger review
- Model, system prompt, safety layer, hosting region or data-use change.
- New subprocessor, acquisition, ownership change or material contract amendment.
- Security incident, privacy complaint, harmful output, bias signal or unexplained performance drift.
- Expansion to new users, data categories, jurisdictions, decisions or levels of automation.
- New legal guidance, classification, conformity route or regulator request.
- Repeated outages, degraded service, altered rate limits or loss of a fallback.
What to monitor
- Quality and safety metrics relevant to the approved purpose, including subgroup or language differences where appropriate.
- Policy and contract compliance, access logs, deletion jobs, retention exceptions and human-review completion.
- Vendor notices, model cards, release notes, subprocessor lists, incident reports and vulnerability advisories.
- User complaints, near misses, overrides and cases in which the system was used outside its approved scope.
NIST’s profile recommends ongoing monitoring and dynamic risk assessments for third-party generative-AI risks. Record every review outcome, including a decision to continue unchanged, and preserve superseded evidence rather than overwriting it.
How to compare two vendors without losing the use-case focus
When multiple suppliers can meet the functional need, score them against the same deployment assumptions and evidence standard. A lower price or broader feature list does not offset an unacceptable data-use or exit risk.
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| Comparison axis | Questions to ask |
|---|---|
| Use-case fit | Does the service support the purpose, users, languages and accuracy or safety needs, and are limitations documented? |
| Data and intellectual property | What inputs are permitted, retained, reused or used for training; who owns outputs; and how are deletion and exit handled? |
| Security and resilience | How are tenants, identities and keys protected; how are vulnerabilities and incidents handled; and what happens during outage or model failure? |
| Transparency and accountability | Are evaluations, limitations, human-oversight expectations, change notices and explanations available for the use? |
| Governance and evidence | Will the supplier support assessments, monitoring, regulator-facing documentation and contractually permitted review? |
| Regulatory fit | Can the supplier support the documentation, transparency or conformity route required for each role and jurisdiction? |
Use weighted scoring only after setting minimum gates. For example, a supplier that cannot contractually restrict training on confidential inputs may fail a data-protection gate regardless of its feature score.
Frameworks and legal sources: what each can and cannot do
| Source | What it provides | How to use it |
|---|---|---|
| NIST AI RMF 1.0 | Voluntary lifecycle outcomes organized as Govern, Map, Measure and Manage. | Structure governance, context analysis, measurement, treatment and monitoring; verify the current revision status before relying on it. |
| NIST AI 600-1 Generative AI Profile | Generative-AI-specific suggested actions for supplier due diligence, third-party monitoring, recordkeeping and contingency planning. | Turn procurement questions and monitoring expectations into evidence requests and controls. |
| ISO/IEC 23894:2023 | AI risk-management guidance for organizations developing, producing, deploying or using AI-enabled products, systems and services. | Align risk-management terminology and lifecycle practices; do not present it as statutory approval. |
| EU AI Act | Risk-based, role-dependent legal obligations, including system-specific documentation and transparency requirements. | Classify the system, identify each role and check the current text, annexes and implementation guidance. |
| EU AI Act Article 43 route | Conformity-assessment requirements that depend on the applicable system and legal route. | Do not assume every AI system requires the same third-party assessment. |
NIST states that the AI RMF was developed over 18 months with contributions from more than 240 organizations. That history explains its breadth, not a guarantee that a supplier or deployment is trustworthy.
Common failure modes and corrections
“The vendor is certified, so approval is automatic.”
Certifications and framework claims cover a defined scope and date. Map that scope to your product, tenant, model, data and use case, then verify gaps.
“The questionnaire is the assessment.”
A questionnaire is an evidence request. Add testing, contract review, risk rationale, owners, conditions and monitoring records.
“A pilot has no compliance implications.”
Pilots still expose data, users and affected people. Use synthetic or minimized data, define an end date, restrict access and document the decision to proceed.
“The model never changes.”
Even if weights are fixed, a vendor can change prompts, filters, retrieval sources, subprocessors, hosting or terms. Require change notices and reassessment triggers.
“Unanswered questions are low risk.”
Unknown retention, training use, incident timing or deletion capability is uncertainty. Record it, seek evidence, constrain the use or reject it when the uncertainty exceeds tolerance.
A practical approval checklist
- Inventory entry is complete and dated.
- Purpose, users, affected people, human review and prohibited uses are explicit.
- Data flows, retention, training or reuse, subprocessors and locations are documented.
- Applicable roles, jurisdictions, classifications and sector rules are identified.
- Vendor evidence has been reviewed for scope, date, independence and limitations.
- Risks are rated with written rationale and uncertainty.
- Mitigations, owners, deadlines and residual-risk acceptance are recorded.
- Contract terms cover data, security, incidents, changes, subprocessors, continuity and exit.
- Monitoring metrics, event triggers and review cadence are set.
- Evidence, decisions and superseded versions are retained in a versioned repository.
Final decision record
Close the assessment with a signed decision that names the approved purpose and boundaries, evidence reviewed, open conditions, residual risks, owners, review date and escalation path. If the use expands or the supplier changes, reopen the record rather than appending an informal note. This discipline turns a one-time procurement exercise into a defensible compliance process.
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