Talk to Your Data, Expose Nothing: Zero Model Exposure Analytics

About this session

Talk to your data in any language and get accurate analytics back, while the model never writes a line of SQL or sees a single sensitive value. Natural language is now the front door to enterprise data, but the default text-to-SQL pattern wires an unpredictable model straight into your production database, which is why prompt injection tops the OWASP list for LLM applications and why so many AI projects stall the moment sensitive data is involved. This session walks through a different architecture: the model proposes an inspectable plan, a deterministic compiler validates it against a knowledge graph of permitted entities and joins before any query runs, and the data stays semantically encrypted underneath, so unprivileged users get the same aggregate answer from protected stand-ins. Security through architecture, not good behavior, and it holds even when an autonomous agent is the one asking. You'll leave with a concrete pattern you can apply to your own pipelines.