CyborgDB is a Confidential Vector Database proxy that transforms your existing databases into encrypted vector search engines. Instead of replacing your database infrastructure, CyborgDB sits between your application and your database, adding confidential vector search capabilities while keeping your data encrypted throughout the entire process.

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Why Confidential?

According to KPMG, 63% of enterprises say that confidentiality and data privacy are their top risk to AI adoption (1). In regulated sectors, this figure increases to 76%. Yet, when it comes to solutions which address these concerns, the market has yet to answer. This leaves a critical mass of companies unserved and unable to adopt AI in their workflows. They need Confidential AI.

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Where CyborgDB Fits in Your Stack

Think of CyborgDB as a specialized middleware layer for vector operations: CyborgDB RAG Overview Instead of switching databases, you keep your existing infrastructure and add CyborgDB as a proxy layer. This means:
  • No database migration required - Work with your current PostgreSQL, Redis, or other databases
  • Familiar development patterns - Use standard database connections and queries
  • Minimal infrastructure changes - Add encryption without rebuilding your data layer
  • Gradual adoption possible - Start with vector search, expand as needed

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The Vector Search Problem

Vector Search powers the most popular AI applications - RAG, RecSys, Semantic Search, etc. However, traditional vector databases have a fundamental security flaw: they use (and often store) vector embeddings in plaintext to enable fast similarity search. This creates a massive attack surface. Your sensitive data - converted to embeddings that still contain semantic meaning - sits unprotected in your database. For enterprise AI applications, this is unacceptable. CyborgDB solves this by enabling Approximate Nearest-Neighbor (ANN) search directly over encrypted vectors. Your embeddings never exist in plaintext, even during search operations.

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How CyborgDB Works

CyborgDB uses cryptographic hashing and symmetric encryption to enable vector search over encrypted data: The Process:
  1. Your app sends vectors → CyborgDB encrypts them using your private key
  2. Encrypted vectors stored → Your database stores only encrypted data, never plaintext
  3. Search queries encrypted → Query vectors encrypted with the same key
  4. Search over encrypted space → ANN search happens on encrypted vectors
  5. Results returned → Matching vectors and metadata, with vectors remaining encrypted
Key Innovation: Traditional vector databases decrypt data during search. CyborgDB performs similarity search directly on encrypted vectors, so your sensitive embeddings are never exposed in plaintext. This approach transforms any standard database (PostgreSQL, Redis, etc.) into a Confidential Vector Database without requiring you to change your existing infrastructure.

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Why Developers Choose CyborgDB

🔄 Works with Your Existing Stack
No need to migrate databases or rewrite applications. CyborgDB integrates as a proxy layer with PostgreSQL, Redis, and other databases you already use. 🔒 True End-to-End Encryption
Vector embeddings stay encrypted throughout their entire lifecycle - at rest, in transit, and during search operations. No plaintext exposure, ever. ⚡ Production-Ready Performance
GPU-accelerated search with CUDA support. Optimized algorithms deliver fast results without compromising security. 🛠️ Developer-Friendly APIs
Familiar programming interfaces and extensive framework integrations (LangChain, etc.) make adoption seamless within existing AI workflows. 📈 Flexible Deployment
Deploy as embedded libraries for maximum control, or as a service for easier scaling. Choose what works best for your team and infrastructure.

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Next Steps

Start Building

Get hands-on in minutesChoose your deployment path and build your first confidential vector search application

Choose Your Deployment

Embedded vs ServiceUnderstand the trade-offs between embedded libraries and service deployment

Database Integrations

PostgreSQL, Redis, MemoryLearn how CyborgDB transforms your existing database into a confidential vector store