Confidential Computing Summit 2025: The Missing Piece for Confidential AI
Nicolas Dupont's talk from the 2025 Confidential Computing Summit on why confidential vector databases are the missing layer in any serious confidential AI stack.
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.