A short look at what we're building at Cyborg this quarter — confidential AI, encrypted vector search, and what's coming next.
CYBORGInside CyborgDB: How End-to-End Encrypted Vector Search Works
Most vector databases encrypt at rest and in transit, then decrypt to plaintext during search — exactly when the data is most exposed. CyborgDB assumes the server may be compromised. This deep-dive walks through how it maintains end-to-end encryption across the entire vector lifecycle, including during search.
CYBORGEmbeddings Aren't One-Way: A Real Vector Database Inversion Attack
A common assumption is that embeddings are safe, abstract, and one-way. They're not. Jim Armbruster runs a live attack on a production-style RAG pipeline — reconstructing social security numbers, passwords, and medical diagnoses from embeddings stored in plaintext with near-exact accuracy.
CYBORGHow CyborgDB Secures the AI Knowledge Layer
Modern AI runs on retrieval, so organizations centralize their most sensitive data into vector databases — making them the AI knowledge layer and a whole new class of risk. Kevin Kopczynski walks through how CyborgDB solves this architecturally, encrypting embeddings into cryptographic tokens that stay searchable while your data stays fully encrypted.