LLMs are the technology underlying most modern AI assistants, coding tools, customer service bots, and document analysis systems. They are trained on enormous corpora of text, including books, websites, code, and scientific papers, and learn statistical patterns in language that allow them to generate coherent, contextually appropriate responses to natural language inputs.
Enterprise LLM deployments typically fall into two categories: using foundation models through APIs (where the model is hosted by a third party) or deploying fine-tuned or custom models (where a foundation model is adapted using proprietary data).
Both approaches raise data governance considerations. API-based deployments involve sending potentially sensitive data to third-party model providers, creating questions about how that data is stored, logged, and used. Fine-tuning on proprietary data raises questions about data residency, consent, and what happens to that data if the model is later accessed or reverse-engineered. For organizations subject to privacy regulation, LLM deployments require clear policies on what data can be sent to or used to train external models.