How to assess your AI vendors for governance and risk
- Much of your AI risk comes from vendors, so assessing them is a core part of AI governance.
- Assess governance, risk, data practices, documentation, transparency, and certifications.
- Expect evidence, not just assurances; vague answers with no documentation are a risk signal.
- Make assessment a repeatable process and keep it connected to your own AI inventory and governance.
- Current as of June 2026. This is general information, not legal advice.
Why vendor assessment matters
When you deploy a vendor's AI, you often inherit obligations and risks even though you did not build the system. Under laws like the EU AI Act, deployers have real duties, and to meet them you need information from the provider. Beyond legal duties, a poorly governed vendor AI can produce biased, inaccurate, or unsafe outcomes that affect your customers and your reputation. Assessing vendors is how you manage the risk you take on from your AI supply chain.
What to assess
A thorough AI vendor assessment looks at several dimensions:
- Governance and accountability: Does the vendor have a clear AI governance practice and accountable ownership?
- Risk and classification: Has the vendor assessed the risk of the system, and can they tell you its risk classification under relevant frameworks?
- Data practices: What data was the system trained on, how is data handled, and are there provenance or bias concerns?
- Documentation: Can the vendor provide the documentation you need to meet your own obligations as a deployer?
- Transparency and oversight: How transparent is the system, and what human oversight does it support?
- Certifications and evidence: Does the vendor hold relevant certifications (such as ISO 42001) or provide evidence of responsible governance?
What evidence to expect
A well-governed vendor should be able to provide evidence, not just assurances: documentation about the system, risk and impact assessments, information supporting your deployer obligations, and ideally independent proof such as certification. A vendor that can only offer vague reassurance, with no documentation behind it, is itself a risk signal. The quality of a vendor's answers tells you a great deal about how well they actually govern their AI.
Making it a repeatable process
AI vendor assessment should be a standard, repeatable part of procurement and ongoing vendor management, not a one-off. Use a consistent set of questions, keep records of vendors' answers and evidence, and revisit the assessment as the vendor's system changes. This both manages your risk and creates the documentation you need to show your own governance.
The two-sided dynamic
There is a useful dynamic here. As enterprises assess their AI vendors more rigorously, vendors that govern their AI well, and can prove it, win more easily, while poorly governed vendors get filtered out. By assessing your vendors, you are both protecting yourself and pushing your supply chain toward better governance. A clear, standard assessment, ideally built around a definitive vendor questionnaire, is the tool that makes this work.
Connecting it to your own governance
Vendor assessment is not separate from your internal governance; it is part of it. The vendors you assess and the evidence they provide feed into your own AI inventory and risk picture. Keeping vendor assessments connected to the systems they relate to, rather than in a separate procurement silo, is what makes your overall governance complete and defensible.
Key terms
- AI supply chain
- The vendors, models, and third-party AI services an organisation relies on.
- Deployer
- An organisation that puts a vendor's AI into use, inheriting certain obligations.
- Vendor evidence
- Documentation and certifications a vendor provides about its governance.
- Repeatable process
- A standard, comparable assessment used across all AI vendors.