Vendor AI risk is a fast-growing blind spot in the enterprise AI portfolio. 70% of organizations are still building out how they evaluate it, even as 80% of enterprise software vendors are embedding AI into products already running across the business.
The challenge is that AI vendors behave differently from what most evaluation processes were built around, in ways that aren’t always visible until something goes wrong. A vendor can ship a fundamentally different product through a routine update. A vendor with no AI features of its own can still introduce AI risk through the interfaces it exposes to other AI agents. System behavior drifts between reviews in ways that periodic validation alone won’t catch.
This guide gives cross-functional teams a concrete framework for evaluating third-party AI across five risk categories, what good vendor disclosure looks like, and how to maintain oversight as vendor AI evolves between formal reviews.
We also cover:
- Why the same AI vendor can present completely different risk profiles depending on use case
- The transparency signals that separate AI vendors worth trusting from those worth scrutinizing
- How Trustible structures third-party AI oversight in practice