AI can change a vendor relationship in ways that are easy to miss: where data flows, how it’s transformed, how outputs are generated and which downstream providers are involved. Because those changes affect business outcomes and control expectations, AI belongs in vendor oversight and governance — not as a standalone IT review.

In practical terms, AI oversight belongs inside your third-party risk management approach because your organization is responsible for the outcomes of what vendors do with your data and processes.

Oversight of vendors’ AI use is a structured effort to make it visible, comparable and governable across your vendor population. It helps leadership make decisions that are consistent, defensible and repeatable.

How to assess vendor AI risk: Three essentials

When organizations assess vendor AI risk, they’re typically trying to clarify three fundamentals:

1. Where and how AI is used: Is AI used in the user-facing experience (e.g., chat or recommendations), in back-office processing (e.g., document intake, classification or routing) and/or in decision workflows (e.g., prioritization logic or eligibility-related steps)? Is it optional, default or always on? Knowing where AI is embedded helps you understand what outcomes it can influence.
2. What data is touched in AI workflows: Which data elements are used — including whether Personally Identifiable Information (PII) and/or electronic Protected Health Information (ePHI) are involved — and what safeguards exist? This includes access controls, monitoring and the vendor’s practices for preventing sensitive data from being used in inappropriate tools or contexts.
3. Model training and fine-tuning implications: This is one of the most important governance considerations. AI features can change what data is processed and how it’s handled, especially when tools are integrated into workflows. If a vendor uses client data (or outputs derived from client data) to train or fine-tune models beyond the contracted service, the risk profile changes materially. Clarity here is essential for privacy expectations, contractual requirements and trust.

Why oversight matters: the third-party AI risk themes that tend to surface

Vendor AI oversight isn’t about creating paperwork. It’s about surfacing issues beyond the use of data noted above that commonly drive risk and escalation:

• Storage and retention implications. AI workflows can create new retention footprints (or ambiguity about retention) that need to be understood and managed.
• Security and protection expectations. Particularly where sensitive data is involved, you need confidence that access controls, monitoring and safeguards are appropriate for AI-enabled processing.
• Transparency, explainability and ethical use. Depending on the use case, you may need visibility into bias and fairness practices, how explanations are produced and how outcomes are tested and monitored.
• Governance and oversight. Mature vendors do more than merely “enable” AI. They can describe how AI use cases are approved, controlled and monitored over time. Without this, AI features can expand informally, controls become inconsistent and changes go undocumented, increasing privacy, security and compliance risk.
• Fourth parties and subcontractors. AI services often depend on other providers. Understanding downstream involvement helps ensure governance extends beyond the primary vendor.

A practical framework: Six categories that make vendor AI oversight manageable

To make vendor AI oversight scalable, organize due diligence into a small set of categories that stakeholders can understand and apply consistently.

In practice, we organize vendor AI oversight into six program areas so teams can route questions to the right owners and evaluate vendors consistently:

1. Technology and infrastructure
2. Process and governance
3. People and culture
4. Risk and compliance
5. Customer and business impact
6. Technical and data risk

This structure supports consistent vendor comparisons, repeatable scoring and clearer decisions — especially when vendor populations are large and decentralized adoption is a reality.

A minimum question set that delivers meaningful visibility

You don’t need dozens of questions to get started, but you do need the right ones. A bare-minimum set often begins with questions like these:

• Are you using AI with machine learning (ML), including generative AI, in any aspect of the products/services you provide to us?
• Are you using AI/ML to process, transform, analyze and/or make decisions using our data? If yes, describe the use cases and safeguards.
• Do you use our data or derived outputs to train or fine-tune any AI/ML model beyond contracted services without our explicit authorization?
• What controls prevent PII, ePHI and other sensitive data from being entered into public or non-approved AI tools, and what technical controls exist where feasible?
• Do contracts with third-party AI providers ensure they will not train on our data without our explicit written authorization?
• Do you have an AI governance policy covering approval, acceptable use, data handling and security controls for AI/ML systems used in delivering services to us?

If a vendor’s answer is unclear or incomplete, treat it as higher risk until clarified. That one principle alone prevents downstream surprises.

As the program matures, augment this minimum set with additional questions based on vendor type, data sensitivity and use case.

How to implement oversight of vendors’ AI use

The difference between a one-time questionnaire and ongoing governance is implementation.

Strong programs for monitoring vendors’ AI use are managed, ongoing processes that typically include these steps:

• Confirm the vendor inventory and scope.
• Have a baseline set of questions but also tailor the question set, as needed.
• Distribute the questions, track responses and manage follow-ups.
• Score results to identify control gaps and assign a risk tier (critical, moderate, low or minimal) relative to the expected control baseline for the use case.
• Escalate and route issues to the right stakeholders (e.g., privacy, security and/or legal).

As a baseline, reassess at least annually — and also whenever there are material changes (new AI features, new data types, workflow changes or new subcontractors/fourth parties).

To support decisions and remediation, consider dashboards and related reporting.

Because AI features and dependencies evolve, repeat oversight of vendors’ use regularly.

The key takeaway: Be proactive in monitoring how your vendors use AI

Your oversight process does not need to be perfect on day one. Starting is what’s most important.

You need enough structure to make vendor AI use visible and governable — with an audit trail leadership can stand behind.