RAG over-retrieval / entitlement-failure register
Public register of incidents in which an AI assistant, agent, or RAG system surfaced content a principal was not entitled to see, or amplified access.
Read full article →Insights on AI retrieval security, RAG authorization, and data governance.
Public register of incidents in which an AI assistant, agent, or RAG system surfaced content a principal was not entitled to see, or amplified access.
Read full article →Purview classifies, labels, and governs your data estate. It does not decide, at query time, whether a user may retrieve a document from AI you build.
Read more →A RAG pipeline can enforce access inside the vector query or in the app after results return. Each has a distinct failure mode. Here is what breaks.
Read more →Embeddings throw away the permissions your source systems already track. Here is the recipe to carry document-level permissions into a RAG pipeline.
Read more →Gateco is not a RAG framework. It is the authorization layer you insert at the retrieval step of LangChain or LlamaIndex. Here is where it goes, and why.
Read more →Vector databases retrieve by embedding similarity. They don't know who's asking or check permissions. That is the RAG security gap, and it is wide.
Read more →Get started with Gateco on the Free tier: 1,000 secured retrievals per month.