Public LLM vs Private LLM — what’s the real risk? 

Every prompt entered into a public LLM externalises organisational knowledge. Because public AI providers often have unclear data retention and training policies, businesses risk losing control over proprietary information while gaining little visibility into how that data is used. 

 Public LLM risks: IP leakage via prompts, unclear data retention policies, compliance exposure, shadow AI adoption, and no per-call audit trail. Public AI systems are externally controlled platforms with their own commercial incentives. 

 Private LLM advantage: deployed behind the organisation’s firewall — full AI capabilities while keeping data, knowledge, and competitive advantage secure and under organisational control. AI sovereignty rather than AI dependency. 

 The rapid adoption of public LLMs mirrors the shadow IT problems of previous decades — but at a much faster scale, with much higher-value IP at stake.