Operational guide
How to Assess Third-Party AI Risk
A third-party AI risk assessment evaluates the supplier, product, AI features, intended enterprise use, data flows, subcontractors, security, performance, change practices, contractual allocation, monitoring, resilience, and exit options. It combines vendor due diligence with use-case analysis because supplier assurances cannot determine the risk of the organization's deployment context.
Direct answer
a third-party AI risk assessment: direct answer
The assessment determines which AI risks are controlled by the supplier, which remain with the customer, and whether evidence and contractual rights support the intended use. A security questionnaire or contract review alone is incomplete. Embedded AI can change after purchase, and the same product can create different exposure across use cases, configurations, data, integrations, and decisions.
A broader AI risk assessment tests how this practice fits the organization's wider ownership, control, and evidence baseline.
AI risk is contextual: the same capability can create very different exposure depending on its intended use, users, data, autonomy, affected decisions, and fallback arrangements. Enterprise assessment therefore needs a system-level scope, explicit assumptions, and a documented relationship between risk scenarios, controls, residual exposure, and acceptance authority.
Main guide
How to apply the topic in an enterprise
The sections below focus on scope, operating practice, and reviewable evidence—the elements needed to turn a useful concept into a dependable management process.
Assess the product in use
Document enabled AI features, intended purpose, users, data, outputs, decisions, integrations, configurations, deployment model, and business dependency. Separate supplier facts from customer configuration and operating controls so accountability does not disappear between parties. The scope should be explicit enough that two reviewers can reach a comparable view using the same facts, while still recording uncertainty that requires further investigation.
Retain validated architecture, data flow, feature settings, account type, owner, use restrictions, and inventory links. The assessment record should connect each material scenario to causes, consequences, affected stakeholders, existing controls, test results, residual risk, treatment actions, and an accountable risk owner. Confidence and missing information should be visible so a numerical score does not imply more certainty than the evidence supports.
Evaluate supplier capability and evidence
Review development and change practices, security, privacy, data use, model limitations, evaluation, incidents, subprocessors, resilience, support, and governance. Assess source quality, scope, period, independence, exceptions, and relevance instead of treating any assurance artifact as universal coverage. The scope should be explicit enough that two reviewers can reach a comparable view using the same facts, while still recording uncertainty that requires further investigation.
Record documents reviewed, unanswered questions, inconsistencies, compensating controls, commitments, and required remediation. The assessment record should connect each material scenario to causes, consequences, affected stakeholders, existing controls, test results, residual risk, treatment actions, and an accountable risk owner. Confidence and missing information should be visible so a numerical score does not imply more certainty than the evidence supports.
Govern change and dependency
Define notification rights, feature controls, model or data changes, incident communication, audit information, service continuity, data return, and exit support. Monitor supplier and product changes against approved use assumptions and reopen assessment before accepting material new functionality. The scope should be explicit enough that two reviewers can reach a comparable view using the same facts, while still recording uncertainty that requires further investigation.
Change notices, review decisions, monitoring, incidents, service tests, exit exercises, and action closure show ongoing oversight. The assessment record should connect each material scenario to causes, consequences, affected stakeholders, existing controls, test results, residual risk, treatment actions, and an accountable risk owner. Confidence and missing information should be visible so a numerical score does not imply more certainty than the evidence supports.
Framework
a third-party AI risk assessment: practical enterprise sequence
Use this sequence to assess a defined AI use case, prioritize material scenarios, and connect treatment decisions to owners and evidence.
01
Define the use case
Record features, purpose, users, data, outputs, integrations, decisions, and dependency. Record the accountable owner, source evidence, completion date, unresolved questions, and the decision or handoff produced by this step.
02
Map shared responsibility
Assign supplier and customer duties for data, controls, monitoring, and incidents. Record the accountable owner, source evidence, completion date, unresolved questions, and the decision or handoff produced by this step.
03
Evaluate supplier evidence
Review governance, security, privacy, performance, resilience, change, and assurance. Record the accountable owner, source evidence, completion date, unresolved questions, and the decision or handoff produced by this step.
04
Set contractual protections
Address data use, changes, incidents, evidence access, continuity, and exit. Record the accountable owner, source evidence, completion date, unresolved questions, and the decision or handoff produced by this step.
05
Test customer controls
Validate configuration, access, restrictions, review, monitoring, and fallback. Record the accountable owner, source evidence, completion date, unresolved questions, and the decision or handoff produced by this step.
06
Monitor material change
Track features, models, terms, subprocessors, incidents, and dependency. Record the accountable owner, source evidence, completion date, unresolved questions, and the decision or handoff produced by this step.
FAQ
Frequently asked questions
What is a third-party AI risk assessment?
A third-party AI risk assessment evaluates the supplier, product, AI features, intended enterprise use, data flows, subcontractors, security, performance, change practices, contractual allocation, monitoring, resilience, and exit options. It combines vendor due diligence with use-case analysis because supplier assurances cannot determine the risk of the organization's deployment context. The practical test is whether the organization can connect the subject to a defined scope, accountable decisions, operating controls, and evidence that can be reviewed.
Who should own a third-party AI risk assessment?
The business service owner is accountable for use and dependency, while procurement, vendor risk, security, privacy, legal, technology, data, and resilience teams own specialist reviews. Accountability should sit with someone able to make or escalate the required decision; contributors may supply evidence, operate controls, or provide specialist challenge without replacing that accountability.
What evidence supports a third-party AI risk assessment?
Evidence includes product and feature descriptions, data flows, contracts, supplier documentation, assurance reports, evaluations, configurations, incidents, service changes, monitoring, and exit plans. Evidence is stronger when it identifies the system or use case, owner, date, source, version, reviewer, applicable decision, and any exception or follow-up action.
How often should a third-party AI risk assessment be reviewed?
Assess before purchase or enablement, then review on a risk-based cycle and after material supplier, model, feature, term, incident, or use-case changes. Event-driven review is also needed when intended use, data, model or supplier behavior, affected processes, autonomy, ownership, or applicable requirements change materially.
How should leaders use the output from a third-party AI risk assessment?
Leaders should use findings to set approval conditions, contractual protections, technical controls, monitoring, contingency, supplier remediation, or rejection. The output should identify the decision required, accountable owner, priority, target date, dependencies, and proof of completion rather than ending as an isolated document.