Background: The Shifting Sands of Third-Party Risk
For CIOs and leaders in IT, Procurement, and Vendor Management, the age of AI has fundamentally reshaped how we view third-party risk. As Artificial Intelligence becomes deeply embedded across software development, IT support, and managed services, traditional controls focused purely on static data encryption and rigid Service Level Agreements (SLAs) are rapidly becoming insufficient.
For CIOs and leaders, the core challenge is that AI introduces elements of opacity, dynamic behavior, and shared accountability that didn’t exist before.
Our forthcoming articles in this series will delve into the critical operational, ethical, and regulatory risks. We begin, however, with the most immediate and tangible threat: AI Data Leakage and Privacy.
🔒 1. Data & Privacy Risks: The Immediate Threat
The biggest data threat isn’t the sophisticated hacker; it’s the helpful developer using an unapproved AI tool to meet a deadline. This dynamic turns data theft into what we call “data donation”—the unintentional leakage of proprietary code and client data to external, non-secure systems. This is the risk that sneaks in the back door.
The Immediate Threats We Must Address
From a procurement and compliance standpoint, these risks mandate immediate contractual action:
- Data Leakage via AI Tools: A primary concern is the use of generative AI tools (like GitHub copilots) that transmit proprietary source code or confidential business logic to public cloud models for processing. This instantly exposes your company’s “secret sauce” to unknown, external servers.
- Cross-Border Model Hosting: Your contract may dictate that data must reside in a specific country, but the AI models your vendor uses may be hosted elsewhere. If a vendor’s AI inference engine or training data is hosted in a region outside the contractual data residency boundaries, it can immediately violate data sovereignty clauses. It’s playing international hide-and-seek with compliance stakes.
- The PII Black Hole and Limited Traceability: Traditional audit trails simply stop dead when the data hits the AI layer. Data goes into the model, but it’s nearly impossible to tell if Personally Identifiable Information (PII) was used, cached, or logged during inference. This lack of visibility creates critical audit blind spots and a serious threat to traceability.
Actionable Advice: Mandate Transparency and Shift Liability
You cannot stop vendors from using AI, but you can, and must, govern it. Our strategy must be aggressive yet professional, demanding transparency to shift liability where it belongs.
1. Implement the AI Tool Disclosure Rider:
This should be a non-negotiable component of every Statement of Work (SOW). Require vendors to formally list every AI tool used on your project (from coding assistants to support models). They must certify the tool’s hosting location and its data training policy. If a vendor cannot provide these details, they cannot use the tool on your project. Period.
2. Demand Liability and PII Attestation:
Make vendors sign off that their internal policies strictly prohibit putting your confidential data or client PII into any non-approved public model. This paper trail is essential to shift the liability if an unintentional leak occurs.
3. Ask the Right Technical Questions in Quarterly Reviews:
Move beyond generic security questions. Force your vendor managers to get technical and demand specific answers:
- The Data Use Certification: “For every AI tool you use on our project, can you certify that our data is not used for training or improving the underlying model? And where are the associated inference logs physically hosted?”
- The Policy Check: “Do you have a dedicated, enforced internal policy that explicitly forbids your developers from feeding any client PII or proprietary code into public Large Language Models? How do you monitor for compliance?”
We must be specific, demand transparency, and refuse to rely on old, generic security clauses. Our contracts need to reflect the reality of the AI supply chain.
In our next article, we will tackle Episode 2: Who Owns the Algorithm? IP, Plagiarism, and the Shadow AI Developer—the intellectual property nightmare.
