Extending AI Governance Beyond Your Borders: Are Partners and Vendors a Manageable Risk?

Written by Kristine Briggs | May 19, 2026 1:20:04 PM

“AI Governance” has become a huge buzz word and a source of FUD (fear, uncertainty and doubt) in pretty much every industry.

OIC Advisors has seen many companies struggling with “AI Governance” – especially finding the right balance between empowering teams with the responsible use of AI, while still maintaining some measure of governance to mitigate risk and protect the organization. We have seen a number of companies trying to get started, but for most it has been slow and difficult.

There are any number of governance frameworks publicly available, and OIC has also created an AI governance framework to simplify the exercise – a practical roadmap instead of a theoretical exercise. One often-overlooked area we encourage clients to consider early in the process is how AI governance should relate to their partners and vendors.

Large organizations especially have risk in this regard, because they often outsource entire functions to third-party partners or vendors. External partners may have direct or indirect access to sensitive data and AI tools. Many consulting organizations also use vendors as part of their consulting teams to their end-clients. So, where this is the case, it is critical to have some measure of governance, and explicit and communicated policies for tooland data use.

We’ve found it pervasive that few end users review, or understand, AI tool license agreements. Those “I Agree” buttons hide important details buried in Terms of Service (ToS) or End User License Agreements (EULA), including:

Usage Rights: Restrictions on whether you can use the output for commercial purposes or only for personal use. 
Ownership: Definitions of who owns the "input" (your prompts) and the "output" (the generated content). 
Data Training: Clauses stating that your data may be used to train and improve future versions of the AI model. 
Prohibited Content: Rules against generating illegal, harmful, or copyright-infringing material.
Liability: Disclaimers that the vendor is not responsible for inaccurate or offensive outputs.

As an example, what that means, especially in regulated industries, is that your proprietary company data could become part of a vendor’s broader dataset, violating your data protection requirements. Although we have seen a little progress, especially in Europe, many regulators are just beginning to tackle this issue.

So, our advice is simple:

Map your dependencies. Understand in what business processes your company leverages partners and vendors.
Trace the data. Understand in detail what data they have access to; this includes your company’s data and that of your end-clients.
Set clear boundaries. Develop policies specific to data protection and approved tools. Ensure they are communicated to existing partners and vendors, and build them into your vendor on-boarding processes.
Stay vigilant and verify. Communicate and regularly monitor vendors and partners for compliance with your processes and policies as part of your broader governance program.

Ensure your entire company understands that AI governance doesn’t stop at your internal teams -- it extends wherever your data and processes go.

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