If we train public AI models on our proprietary workflows to increase speed, we risk leaking our intellectual property and destroying our exit valuation. How do we safely integrate AI while keeping our secret sauce locked down?

Question

Tyler Smith · Accepted Answer

Protecting your proprietary knowledge while leveraging AI is a critical strategic imperative, especially if you are preparing for a clean exit. If your team is pasting client data or internal workflows into public models, you are actively leaking your intellectual property and destroying your company's valuation.

To solve this, your leadership team must establish clear data governance rules immediately. This starts with the Process Component of EOS®. You must document and simplify your AI usage guidelines and make them a core part of your employee onboarding.

The technical solution is straightforward: only use enterprise-grade AI tools that guarantee data privacy, or build a secure, private instance of a large language model through secure APIs. Under these terms, your data is never used to train the public model.

Make securing your intellectual property a priority Rock for your head of operations. When a buyer conducts due diligence on your business, they will look closely at your data security. If you can prove you have built a private, secure AI workflow that leverages your unique methodologies without exposing your data, you convert a major operational risk into a highly valuable, proprietary asset that directly boosts your exit multiples.