Third-party AI assurance · evidence practice
Vendor AI Risk Evidence
Vendor AI risk evidence is the documentation, test results, contractual commitments, system records, and review decisions used to evaluate claims about a third-party AI product in a specific use case. The goal is not document volume. It is enough relevant evidence to support, restrict, defer, or reject a use—and to revisit that decision when the product changes.
For technology risk, AI governance, procurement, security, privacy, compliance, model governance, internal audit, and business owners reviewing external AI products and embedded AI features.
Answer first
What counts as vendor AI risk evidence?
Evidence is information that helps evaluate a defined claim about the vendor AI system you actually intend to use. A questionnaire answer can record a claim. A model card can describe intended behavior. A contract can create obligations. A test can show observed behavior. None of those artifacts automatically proves every other claim about the product.
The evidence file should preserve scope: vendor, product, model or service version where known, enabled features, configuration, data boundary, intended use, review date, source, limitations, and the claim each artifact supports.
For broader due diligence, contracting, decision design, and ongoing monitoring, use the vendor AI risk guide. Use this page to make the supporting evidence reviewable.
- Supplier claim
- “Customer data is not used for training.” Useful as a statement to test, not as self-validating proof.
- Supporting evidence
- Plan-specific terms, data-flow documentation, configuration, subprocessors, retention terms, and relevant assurance records.
- Observed evidence
- Customer-side tests, logs, configuration captures, incident records, or behavior observed in the purchased environment.
- Decision evidence
- The dated record connecting the claim, evidence reviewed, unresolved gaps, owner, conditions, and reassessment trigger.
Evidence file anatomy
Build the file around six questions.
A useful vendor AI evidence file makes the use, dependency, proof, and decision reconstructable. Increase depth as data sensitivity, permissions, consequence, opacity, operational dependency, or regulatory exposure increases.
What exactly is being approved?
Record the vendor, product, AI feature, relevant version or service state, enabled configuration, workflow, users, owner, and intended decision.
Useful records: use-case brief, service description, identifiers, enabled features, approved and excluded uses.
What enters the service, and what can the AI reach?
Document data inputs, retention, training use, subprocessors, user permissions, connected systems, and any tool or retrieval access.
Useful records: data-flow diagram, DPA, retention terms, training-use statement, subprocessors, permission map.
What does the supplier say the AI can and cannot do?
Capture intended behavior, known limitations, prohibited uses, failure modes, and conditions that constrain reliable operation.
Useful records: model card, transparency note, intended-use statement, limitation register, reviewer guidance.
What evidence shows the capability is adequate for this use?
Use evaluations and local observations that match the workflow, population, configuration, permissions, and decision being reviewed.
Useful records: evaluation results, pilot results, samples, access-control tests, output review, independent testing.
What could invalidate the earlier review?
Track material model, retrieval, data, feature, integration, upstream-provider, and incident changes that can alter the evidence basis.
Useful records: release notes, change notices, incident terms, impact assessment, rollback path, retest record.
Who owns the controls, and what can the organization enforce?
Make evidence rights, shared responsibilities, support obligations, escalation, export, deletion, contingency, and exit conditions explicit.
Useful records: contract clauses, responsibility matrix, support terms, export/deletion process, contingency plan.
Evidence strength
Judge evidence by its connection to the claim.
Evidence becomes more useful when it is specific to the product and configuration, relevant to the actual use, current enough for the decision, and corroborated by testing or another independent source where the risk justifies it.
Assertion
A vendor statement, sales response, questionnaire answer, or policy summary records what is claimed. It identifies what needs support; it is not self-validating proof.
Documented support
Contract terms, technical documentation, model cards, architecture diagrams, and control descriptions add scope and specificity to the claim.
Observed evidence
Logs, configuration captures, release records, samples, incident records, and customer-side observations show what actually occurred.
Tested evidence
Relevant evaluations, control tests, pilot results, permission tests, and output testing directly challenge consequential claims.
Corroborated evidence
Independent assurance, external validation, regulator or auditor evidence, or multiple independent sources can reduce reliance on one evidence root.
The right evidence depends on the claim. A customer-side configuration capture can be more relevant to a configuration claim than a broad certification. Independence, scope, freshness, method, and directness should be judged separately.
Artifact limits
What does each vendor document actually prove?
Common vendor artifacts are useful when the claim matches their scope. Treat each artifact as evidence for a bounded purpose, not as a universal assurance stamp.
| Artifact | Can support | Does not establish by itself |
|---|---|---|
| Model card / transparency note | Intended use, model or feature description, evaluation summaries, known limitations, safety guidance. | How your specific configuration behaves, whether your controls operate, or whether contractual promises are enforceable. |
| SOC 2 / assurance report | Controls and testing within the report’s system boundary, period, criteria, exceptions, and auditor scope. | AI quality, fairness, factuality, suitability for your use case, or controls outside the examined boundary. |
| Vendor questionnaire | Structured supplier assertions, ownership statements, process descriptions, and pointers to supporting records. | Independent verification. Answers remain assertions until supported where the risk requires support. |
| Evaluation / benchmark | Observed results for the tested model, data, tasks, metrics, conditions, and period. | Performance on untested workflows, populations, versions, configurations, or future releases. |
| DPA / contract terms | Enforceable obligations when the executed agreement clearly covers the service and use. | Whether technical behavior actually conforms to the terms or whether local configuration creates additional exposure. |
| Customer pilot / local test | Observed behavior in a bounded customer workflow using selected data, settings, permissions, and evaluation criteria. | General performance outside the test boundary or behavior after a material vendor change. |
Evidence quality is claim-specific. Record what an artifact covers, what it excludes, how current it is, and whether another artifact depends on the same underlying analysis.
Worked review
Can a material AI change occur without a usable review window?
A contractual promise to “notify customers of material changes” sounds reassuring. The assurance question is whether the organization can identify the change, evaluate its impact, restrict use if necessary, and preserve the decision before exposure increases.
- Request
- Executed terms, change-notification language, named notice channel, release history, version policy, and examples of prior notices.
- Check scope
- Does “material change” include upstream model substitutions, retrieval changes, permission changes, data-use changes, and features enabled by default?
- Check timing
- Is notice provided before deployment, after deployment, or only when the vendor decides customer action is required?
- Check actionability
- Can the customer delay adoption, disable the feature, restrict affected workflows, retest, or roll back?
- Gap
- The contract may create a notice obligation without creating a usable review path. An inbox address alone does not assign an internal owner or define what happens when the change arrives.
- Required record
- Add a named notice recipient, internal change-review owner, material-change criteria, release/change log, local retest record, and a temporary restriction path.
Decision
Proceed with conditions for the bounded use. Contract language is useful but not sufficient on its own. Approval requires an operational notice-to-review path, explicit ownership, and reassessment when the upstream model, retrieval, permissions, data handling, or output behavior changes materially.
Primary sources
Use current guidance within its scope.
The sources below support the third-party-risk, AI supply-chain, documentation, change, monitoring, and model-risk concepts used here. They do not create one universal vendor-evidence checklist for every organization.
The 2023 U.S. interagency third-party risk guidance remains the existing general guidance while federal banking agencies seek comment on a proposed replacement issued September 11, 2026. Treat the 2026 document as proposed—not final—until the agencies complete the process.
- U.S. banking · current
Interagency Guidance on Third-Party Relationships: Risk Management
Federal Reserve / FDIC / OCC, 2023. Covers risk-based third-party relationship management across planning, due diligence, contracting, monitoring, and termination.
- U.S. banking · proposed
Proposed Third-Party Risk Management Guidance
OCC with FRB, FDIC, NCUA, Sept. 11, 2026. Proposed replacement guidance emphasizes tailoring third-party risk management to reasonably assessed risk.
- NIST AI RMF
NIST AI RMF 1.0 — GOVERN 6
Addresses AI risks arising from third-party software, data, and supply-chain issues, including contingency processes for failures or incidents.
- Generative AI
NIST AI 600-1: Generative AI Profile
2024. Extends AI RMF practices for generative AI, including third-party assessment, supplier agreements, monitoring, incident coordination, and supply-chain considerations.
- Model risk · scope
OCC Bulletin 2026-13 — Revised Model Risk Management Guidance
Discusses vendor and other third-party products for in-scope models while excluding generative AI and agentic AI from that guidance’s scope.
- EU · where applicable
EU AI Act Article 25 — Responsibilities along the AI value chain
Use with related provider/deployer provisions. Applicability depends on the system, organizational role, and facts.
Evidence depth should be proportionate to the use, data, permissions, consequence, criticality, opacity, and dependency being reviewed. Apply the laws, policies, supervisory expectations, contractual obligations, and technical criteria that actually govern the organization and use case.