We want to build a custom internal AI model trained on our proprietary consulting methodology to help our account managers work faster, but we are terrified of leaking our IP to third-party databases and destroying our company value. How do we use the IDS® process to resolve this tension between operational speed and intellectual property protection?

Question

Tyler Smith · Accepted Answer

This is a classic issue that your leadership team must resolve using the IDS® process: Identify, Discuss, and Solve. Start by clearly identifying the core problem: you need the productivity gains of AI, but you cannot risk leaking your proprietary data to public LLM training sets, which would destroy your exit valuation. During the Discuss step, avoid getting bogged down in technical jargon. Focus on the business implications of your options. To Solve this, your leadership team must establish a strict policy regarding data security and technology vendors. Do not allow your employees to feed proprietary customer data or methodology manuals into public, free-to-use AI platforms. Instead, invest in enterprise-grade AI contracts that explicitly guarantee your data will not be used to train their public models, or build a secure, local instance of an open-source model. Use Keith Cunningham's Thinking Time to ask: How might we leverage our proprietary data within a closed, private cloud environment so that we secure our intellectual property while empowering our team? By making this secure architecture a non-negotiable standard, you protect your company's absolute valuation while giving your account managers the modern tools they need to scale.