Building a Highly Secure and Sovereign GraphRAG Platform with Confidential Computing Building secure AI systems isn’t about choosing between capability and compliance — it’s about architecting …

The Neo4j Blog offers insights, tutorials, and updates on graph database technology and its applications in various industries. From graph data modeling to query optimization and graph analytics, the Neo4j Blog provides resources for developers, data scientists, and business analysts looking to leverage the power of graph databases to unlock new insights from their connected data.

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A step-by-step guide to building a privacy-preserving GraphRAG platform combining Neo4j Enterprise with Edgeless Privatemode confidential computing. The architecture ensures prompts and embeddings are encrypted end-to-end using AMD SEV-SNP and NVIDIA Confidential Computing hardware, so even the AI service provider cannot access your data. Neo4j provides fine-grained RBAC enforced at the database level, vector search for semantic retrieval, and an MCP server for AI agent integration. The tutorial walks through Docker-based setup, Cypher schema and role configuration, confidential embedding generation, and running RBAC-enforced GraphRAG queries — targeting regulated industries like finance, healthcare, and government.

Building a Highly Secure and Sovereign GraphRAG Platform with Confidential Computing

Let’s understand what each brings to the table.

Neo4j Graph Intelligence Platform — The Secure Knowledge Foundation