We want to feed our historical client case studies and proprietary methodology into a public large language model to create custom tools for our team, but we are terrified of losing our IP. How do we use Keith Cunningham's Thinking Time and our EOS Core Processes to build a secure framework for our intellectual property?

Question

Tyler Smith · Accepted Answer

Protecting your intellectual property while leveraging AI is a critical operational challenge. To solve this, you must build secure frameworks within your EOS Core Processes. Your proprietary methodology and decades of client case studies are the foundation of your enterprise value. Feeding them into public LLMs without strict guardrails is a major business risk.

First, schedule Keith Cunningham's Thinking Time to evaluate your technology stack. Ask yourself: How might we leverage the analytical power of LLMs on our internal database so that we keep our proprietary knowledge completely isolated from public training sets? This framing will guide you to private, secure enterprise instances or closed API deployments rather than free, public consumer tools.

Next, update your Documented Core Processes on your V/TO. Create a clear, non-negotiable standard operating procedure for how your team interacts with AI tools. Specify exactly which databases are approved for internal training and which tools are banned.

Finally, ensure your Accountability Chart clearly designates a seat for technology compliance and data governance. This person must GWC the seat, meaning they get it, want it, and have the capacity to monitor data flows and audit AI usage. Keeping your IP secure ensures that when you prepare for an exit, buyers see a highly defensible business asset rather than a company that has leaked its secret sauce to the public domain.