A few years ago, retrieval-augmented generation (RAG) was a research pattern. Now it’s the default architecture for enterprise AI: legal teams running it over privileged matter files, engineering teams wiring it into internal code search, support functions plugging it into customer-specific knowledge bases. The mechanic is the same in every case. A vector store sits between a person and a language model, and the model gets to see whatever the store decides to hand back.
This is where the security conversation tends to go sideways. Most published guidance on securing RAG goes straight to controls. Encrypt the . . .
Leave a Comment