It starts in a conference room that smells of stale coffee and whiteboard markers. The product manager, glowing from a keynote video, sketches a box on the board. "The user asks the chatbot a question about their order," she says, drawing an arrow. "And the bot just looks it up in our database and gives them the answer." She draws another box. "Simple."
The senior engineer in the corner doesn't say anything. He just closes his laptop with a quiet, final click. He knows this isn't simple. This is the beginning of a security nightmare that makes SQL injection look like a quaint relic.
The fastest way to make a language model useful is to give it your data. This is the central premise of Retrieval-Augmented Generation, or RAG, the architectural pattern that has taken over the industry. Why spend millions fine-tuning a model when you can just feed it your company's documents, your knowledge base, your customer relationship manager? The promise is intoxicating: an omniscient assistant, ready to serve.
So we point the model at the data. We open a firehose from our most sensitive, structured, and vital corporate asset directly into a probabilistic black box we barely understand. We are taking the crown jewels and handing them to a brilliant, unpredictable, and infinitely suggestible intern who has no concept of privacy, security, or consequence.
Traditional data security is built on rules. A user with the "support_agent" role can execute `SELECT order
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