GraphRAG doesn't just give better retrieval performance than RAG. It's actually much more secure! Researchers just published a paper showing that knowledge graph-based AI systems are up to 80% harder to poison than conventional RAG. Let me explain why that matters. When most enterprise AI tools retrieve information, they find document chunks that look semantically similar to the query and feed them straight to the model. Simple. Fast. And very easy to manipulate — inject a well-crafted document into the knowledge base, and the AI will repeat it. The researchers at UESTC tested this systematically across multiple systems. Against flat RAG: attack success rates above 80%. Against graph-based systems: in many configurations, below 15%. The reason isn't a security feature. It's the architecture. Knowledge graphs don't store raw documents. They store entities, relationships, and verified facts — structured by how they connect to everything else in the graph. A poisoned document trying to influence the system has to survive that abstraction process. It has to form coherent connections. Isolated facts with no grounding in the existing knowledge structure don't make it through. We built AI Brain on a knowledge graph architecture — not primarily for security, but because it produces dramatically better retrieval performance for complex enterprise queries. You retrieve exactly what's needed, with context, without stuffing a context window full of loosely-similar chunks and hoping for the best. The security properties turned out to be a structural side effect. For our clients connecting proprietary deal data, research archives, and client transcripts into an AI layer — that distinction matters more than people realise. Link to the paper in the comments :)