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Videos · Technical Explainer·Jun 25, 2026·3:03 watch

How 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.

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Videos · Technical Explainer·Jul 9, 2026

Inside 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.

Cyborg Team
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Videos · Technical Explainer·Jul 2, 2026

Embeddings 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.

Cyborg Team
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Videos·Jun 18, 2026

Your AI Knowledge Base Is a Security Risk — Introducing CyborgDB

AI is forcing enterprises to centralize their most sensitive data into knowledge bases built for retrieval performance, not confidentiality — creating a single, high-value target. CyborgDB is the security layer for that knowledge base: an end-to-end encrypted vector database that keeps data encrypted at rest, in transit, and in use.

Cyborg Team