What if you could harness the power of AI without compromising data confidentiality?
Today, we’re excited to introduce CyborgDB, the first Confidential Vector Database designed for enterprises and ISVs operating in highly regulated industries. Built from the ground up to protect sensitive inference data, CyborgDB enables you to integrate vector search into your AI stack while keeping vector embeddings end-to-end encrypted, even during inference.
Why We Need Confidential AI
AI adoption is accelerating, but so are concerns about data privacy. According to KPMG, over 60% of enterprises cite confidentiality as a primary barrier to deploying AI. In highly regulated industries - such as healthcare, finance, and government - that number jumps to 76%.
While many teams address these concerns by encrypting data at rest or in transit, the reality is that most AI workloads still process data in plaintext. This includes one of the most common patterns in modern AI systems: vector search.
Vector embeddings - whether derived from patient records, financial histories, or legal documents - encode semantically rich representations of sensitive information. Yet virtually all vector databases today require these embeddings to be decrypted during indexing and querying in order to support approximate nearest-neighbor (ANN) search. The result is twofold:
- Security risk: inference data is exposed at its most vulnerable point
- Compliance risk: data processed in plaintext may violate internal policies or regulatory requirements like HIPAA, GDPR, PCI DSS, or FINRA, depending on jurisdiction and use case
To safely adopt AI in regulated environments, organizations need more than encryption at rest. They need end-to-end confidentiality, where protection extends across the entire inference workflow - from embedding to retrieval.
The Missing Piece: CyborgDB
CyborgDB is the first Confidential Vector Database, purpose-built to enable secure, compliant AI in regulated environments. It transforms any supported database (such as PostgreSQL or Redis) into a fully confidential vector store, allowing teams to build AI workflows like semantic search, RAG, and recommendation systems without exposing vector embeddings at any point in their lifecycle.
Unlike traditional vector databases, which decrypt embeddings for indexing and querying, CyborgDB introduces a zero-trust architecture where vectors remain encrypted at rest, in transit, and even during inference. This is achieved through a novel combination of cryptographic hashing, symmetric encryption, and forward privacy techniques, allowing for approximate nearest-neighbor (ANN) search directly over encrypted space.
The result is a fundamentally more secure and compliant approach to vector search:
- No plaintext exposure at inference time, reducing attack surface
- Built with compliance in mind, aligning with HIPAA, GDPR, PCI DSS, and FINRA constraints
- Developer-friendly APIs, with easy integration into Python and C++ AI workflows
CyborgDB is designed for teams building secure AI applications at scale:
- High-speed search, with optional GPU acceleration via NVIDIA cuVS for large-scale workloads
- Plug-and-play compatibility with your existing infrastructure - no need to replace your current database
- Flexible deployment options, including a lightweight core library (Python and C++) and a containerized microservice for scalable deployments (coming June)
Whether you're building clinical search tools, secure financial analytics, or confidential government systems, CyborgDB offers a path to production-ready AI without compromising security or compliance.
Get Started in Minutes
We’ve made it simple to try CyborgDB and build your first Confidential AI application. To get started, install CyborgDB Lite (our evaluation version):
# Install cyborgdb-lite
pip install cyborgdb-lite
Follow our Quickstart Guide for full step-by-step instructions.
You can also explore the API Reference or get in touch if you have questions.
Stay tuned - we’ll be publishing deep-dives on CyborgDB’s architecture, use cases, and more in the coming weeks.
