Yes, but not with public models like ChatGPT or Google: protecting IP you can’t afford to leak means keeping the sensitive parts on infrastructure you control. Guest Herman Moore explains why he won’t put his patented IP into public AI tools, and Collin Thomas, Michael Wacht, and Matthew Sutherland break down the actual architecture needed for data privacy: local models, hardware requirements, and deciding what runs on-machine versus what gets sent out.
Understanding the difference between public models and local AI is critical for protecting sensitive business data. This discussion provides a clear framework for building a secure, private AI setup without sacrificing capability.