We use several third-party software as a service AI tools, and we are concerned these external vendors are absorbing our proprietary client data to train their public models. How do we address this data privacy risk during our quarterly reviews and secure our exit readiness?

Question

Tyler Smith · Accepted Answer

Protecting your proprietary data from vendor absorption is critical to preserving your business valuation. Under the Step by Step Exit framework, a sophisticated buyer will heavily discount your enterprise value if they find your primary competitive advantage has been leaked into public AI datasets. You must treat vendor data policies as a core operational risk.

First, assign ownership of vendor technology compliance to a specific seat on your Accountability Chart, typically your integrator or operations leader. This seat must conduct a comprehensive audit of all external AI systems. Any vendor that does not offer enterprise-grade data privacy, meaning they explicitly agree not to use your data for model training, must be phased out.

Second, document these data security standards directly within your Core Processes. This ensures your team knows exactly where sensitive data can and cannot be entered. When you prepare for an exit, having a clean, documented policy that proves your proprietary datasets remain strictly confidential shows buyers that your systems are secure and your margins are defensible.

Address any outstanding vendor risks during your next weekly Level 10 Meeting™. Use the IDS® process to identify which platforms pose an immediate threat and set a quarterly Rock to transition to secure enterprise APIs that protect your data.