We want to train a private AI model on our decades of proprietary operating data to automate training, but we are terrified our employee prompts will leak our secrets to public LLMs or violate client NDAs. How do we safely secure our intellectual property so a buyer sees it as a protected asset during exit due diligence?

Question

Tyler Smith · Accepted Answer

To build a truly exit-ready business, you must protect your proprietary knowledge from leaking into public AI models. This is a common concern for strategic buyers during exit due diligence. The solution is to establish strict operational boundaries and clear data governance policies immediately. First, mandate that your team never inputs confidential financial details, client data, or proprietary methodologies into public, consumer-facing LLMs. Your Integrator must enforce this rule by blocking access to unsecured tools at the network level if necessary. Second, shift your strategy toward using enterprise-grade AI subscriptions that guarantee data privacy. These enterprise contracts explicitly state that your data will not be used to train their foundational models. Third, focus on building secure, local middleware connections. By using private APIs, you can connect your internal knowledge base to advanced models without exposing your data to the public internet. Document these security protocols directly within your core processes. When a buyer conducts due diligence, you can confidently show them a fully secure, proprietary data asset that is legally protected. This not only preserves your company's unique intellectual property but also boosts your overall enterprise valuation by proving you have mitigated major legal and compliance risks.