Third-Party Risk in the Age of AI Vendors

Vendor Risk Has a New Dimension

When third-party risk management programs were originally designed, the threat model was straightforward: a vendor stores or processes your data, and if they have a breach, your data is exposed.

That model still applies. But AI has introduced a second dimension that most TPRM programs were not built to handle.

The question is no longer just "is this vendor secure?" It is also "what is this vendor's AI doing with our data?"

Why Traditional TPRM Misses AI Risk

Most vendor risk programs rely on annual questionnaires and periodic SOC 2 reviews. Both of these tools are designed to assess a vendor's security posture at a point in time.

AI risk does not work that way. A vendor can add a new AI capability — one that processes your data in ways your original risk assessment never contemplated — between your annual reviews.

The three gaps we see most often:

1. No AI disclosure requirement in vendor contracts Most BAAs and vendor agreements were written before generative AI was commercially significant. They do not require vendors to disclose when they add AI models to their products.

2. Questionnaires that do not ask the right questions Standard security questionnaires ask about encryption, access controls, and incident response. They rarely ask: "Which AI models process our data? Where are those models trained? Who has access to model outputs that contain our information?"

3. Risk classifications that do not account for AI exposure A vendor that handles low-sensitivity operational data may suddenly carry high AI risk if they add an LLM that trains on historical workflow data — including data from your organization.

Closing the Gap

The organizations doing this well are doing a few specific things differently.

Add AI disclosure clauses to contracts. Require vendors to notify you before deploying AI models that process your data. Build this into your standard BAA and MSA templates now.

Update your questionnaire with AI-specific questions. At minimum, ask: Does your product use AI or machine learning? If yes, does any AI model process our data? Where is model training data stored and who has access? What governance does your organization have over AI outputs?

Create an AI vendor sub-registry. Within your vendor inventory, tag vendors that use AI to process your data. Track model types, data exposure, and governance status separately from standard security classifications.

Review your highest-risk vendors proactively. You do not need to re-assess every vendor at once. Start with the vendors in your critical and high-risk tiers, and add AI governance to their next review cycle.

Skopos Supports AI-Ready Vendor Risk Programs

The Skopos platform is built to handle exactly this evolution. Our vendor registry supports custom risk classifications — including AI capability flags. Our questionnaire engine ships with an AI governance template. And our review workflow is designed to handle continuous risk, not just annual checkpoints.

If you are building an AI-aware vendor risk program, talk to our team or explore Skopos.