Available now on build.nvidia.com, the blueprint combines full encryption-in-use and NVIDIA accelerated computing to power secure, enterprise AI applications

NEW YORK--(BUSINESS WIRE)--

Cyborg today announced the availability of the Cyborg Enterprise RAG Blueprint, bringing full encryption-in-use to enterprise-grade retrieval-augmented generation (RAG). Available now on build.nvidia.com and GitHub, the blueprint enables organizations to deploy secure RAG workflows and vector embeddings with the CyborgDB encrypted vector store while maintaining best-in-class performance powered by NVIDIA Nemotron open models, NVIDIA NeMo Retriever microservices and NVIDIA accelerated computing.

“Today’s organizations want to unlock value from AI by centralizing their knowledge into a single vector database to make models more capable and context-aware,” said Nicolas Dupont, Founder and CEO of Cyborg. “That consolidation is fundamental, but it also creates a smaller attack surface with a much larger potential breach radius. Vector databases can therefore become an organization’s biggest liability or its greatest strength. Encryption-in-use addresses this paradox by enabling enterprises to embrace AI confidently without turning innovation into exposure.”

Security organizations like OWASP have warned that vectors and embeddings represent a fast-emerging area of vulnerability. Traditional vector databases will encrypt data at rest and in transit, but still process queries in plaintext. The Cyborg Enterprise RAG Blueprint offers a fundamentally different approach that eliminates the potential exposure of sensitive information with full encryption-in-use, ensuring plaintext never exists in memory, logs, caches or during search.

How the Cyborg Enterprise RAG Blueprint Works

Embedding Generation & Cryptographic Indexing: User data is parsed and converted into embeddings using an NVIDIA NeMo Retriever embedding model. These embeddings are cryptographically indexed via CyborgDB, producing encrypted tokens. Encrypted tokens are then stored in standard backing stores with vector search capabilities (e.g., Redis, PostgreSQL).

Encrypted Retrieval: At query time, prompts are embedded and sent to CyborgDB for cryptographic retrieval. NeMo Retriever reranking model reorders results by relevance, boosting answer accuracy and quality. Forward-secure indexing prevents reconstruction attacks on historical data

Key Management: The launchable notebook generates an encryption key once and stores it in base64 format on disk, which is then used as the index key. The enterprises fully control and own the encryption keys.

By integrating NVIDIA NIM microservices and NVIDIA cuVS GPU-accelerated search with CyborgDB’s encryption-in-use, the Cyborg Enterprise RAG Blueprint delivers complete data protection without compromising on enterprise-grade performance. The production-ready architecture supports multimodal capabilities, including PDF parsing, advanced table and chart extraction, hybrid search, and reranking with NVIDIA NeMo Retriever, achieving sub-10ms encrypted query performance.

Getting Started

The Cyborg Enterprise RAG Blueprint is available today with deployment guides on build.nvidia.com. Users can deploy the complete, enterprise-ready solution with CyborgDB's encrypted vector indexing and retrieval in minutes.

System Requirements:

Docker deployment: 2x NVIDIA H100 or 3x NVIDIA A100 GPUs minimum

Kubernetes deployment: 8xH100-80GB or 9xA100-80GB

Alternative: Use NVIDIA NGC-hosted NIM with 1 NVIDIA GPU for CyborgDB acceleration

OS: Ubuntu 22.04

What's Included:

NVIDIA AI software (NeMo Retriever, Llama Nemotron 3.3)

CyborgDB with NVIDIA cuVS GPU acceleration

NeMo Retriever multimodal PDF parsing and NeMo Guardrails

OpenAI-compatible APIs and sample UI

Production deployment configurations for Docker and Kubernetes

For more information, read the full blog on cyborg.co.

About Cyborg

Cyborg is pioneering a world where digital privacy is a foundational pillar of enterprise AI. Its flagship product, CyborgDB, is the first and only vector database proxy that delivers full encryption in use, ensuring vectors, metadata and keys remain encrypted at every stage. Designed for compatibility with existing databases, CyborgDB enables organizations to build and scale high-performance AI systems without compromising security or compliance. Guided by its mission to protect digital rights and data safety, Cyborg is unlocking the power of secure AI. Learn more at www.cyborg.co.

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Source: Cyborg