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Cyborg Vector Search is a Confidential Vector Database that enables you to search and analyze vectors in a privacy-preserving manner, without revealing the contents of the vectors themselves.

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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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Why Vector?

Vector Search is at the heart of the most popular AI applications - RAG, RecSys, Semantic Search, IR, etc. - and the market is overcrowded with Vector DBs. All of these share a key commonality: they need to store their indexed contents (vector embeddings) in plaintext to enable Approximate Nearest-Neighbor (ANN) search. This creates a significant attack vector for that data, making confidentiality a near-impossibility. To solve this, we need a brand new approach to Vector Search - Confidential Vector Search. Cyborg Vector Search is the first solution to implement this. By leveraging cryptographic hashing and symmetric encryption, Cyborg Vector Search enables ANN search over encrypted space. This means that vector embeddings are not decrypted during search, and remain encrypted throughout their lifecycle. This greatly reduces the attack surface while guaranteeing the confidentiality of inference data.

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How It Works

Cyborg Vector Search is built on a novel architecture that keeps confidential inference data secure through a zero-trust design. It leverages cryptographic hashing and forward-privacy to maintain vector embeddings end-to-end encrypted throughout their lifecycle, including at search time. Cyborg Vector Search RAG Overview This makes it easy to integrate with existing AI workflows, providing high-performance indexing and retrieval that can be GPU-accelerated with CUDA. Cyborg Vector Search works with databases such as Postgres and Redis, transforming traditional databases into Confidential Vector Databases.

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Backing Stores

Cyborg Vector Search supports a variety of backing stores, including:
  • PostgreSQL
  • Redis
  • Memory (for testing and development)
In addition, Cyborg Vector Search supports managed services such as:
  • AWS RDS
  • AWS ElastiCache
  • Azure Database for PostgreSQL
  • Azure Cache for Redis
  • Google Cloud SQL
  • Google Cloud Memorystore
We are continuously adding support for more backing stores. If you have a specific backing store in mind, please let us know.

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Deployment Models

Cyborg Vector Search supports a variety of deployment models, including: 1. Embedded Library In this form, Cyborg Vector Search is packaged as a standalone module (Python or C++) which contains all of the encrypted indexing and querying logic. The module is run locally and can connect to any of the supported backing stores. Recommended for evaluation, small-scale deployments, and custom integrations. 2. Microservice (Docker Image) Coming June 2025 The Cyborg Vector Search microservice is a self-contained Docker image that can be deployed locally or in a Kubernetes cluster. It enables scalable and repeatable deployments, and also connects to any of the supported backing stores. REST API as well as client SDKs (Python, JS/TS, C++ and Go) will be available. Recommended for development and production deployments. 3. Microservice w/ DB (Docker Image) Coming Q3 2025 This evolved microservice integrates a backing store within the Docker image, enabling a self-sufficient Confidential Vector DB deployment without external backing stores. REST API as well as client SDKs (Python, JS/TS, C++ and Go) will be available. Recommended for development and production deployments. 4. Serverless Release Date TBD The fully-managed Cyborg Vector Search offering will provide the lowest barrier to adoption - simply generate an API key and install a client SDK. Recommended for everything from evaluation to large-scale deployments.
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