Making Confidential AI Possible with End-to-End Encrypted Vector Databases

Today’s “RAG” (Retrieval‑Augmented Generation) systems rely on vector databases, yet the vectors they store are fully invertible: with only the embedding you can reconstruct the original confidential text. In a live demo we will show how a few dozen lines of Python extract embeddings from an off‑the‑shelf ChromaDB instance and recreate sensitive source documents—then contrast this with an impossible inversion attack against the same workload running on CyborgDB, the first end‑to‑end‑encrypted vector database. The talk opens by quantifying the privacy gap that stalls 46 % of AI pilots in regulated industries. We dissect the attack surface of plaintext embeddings, illustrate a successful extraction + inversion attack, and measure how often real‑world deployments leak data. We then deep‑dive into the cryptographic design of CyborgDB (which uses OpenSSL). Benchmarks show only 15 % latency overhead versus plaintext search and 7× throughput uplift on GPU accelerators, making encrypted retrieval practical for production workloads. Audience members will leave with working open‑source code, Docker images, and clear architectural patterns for plugging confidential vector search into any OpenSSL‑based stack.