Building a Highly Secure and Sovereign GraphRAG Platform with Confidential Computing

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.