A junior analyst asks your private AI about quarterly numbers, and it returns board-level financials nobody cleared them to see. That is not a hypothetical. It is the single most common reason enterprise RAG pilots fail their security review, and almost no one budgets for it. Here is what our team keeps finding. You deploy an open-weight model on a GPU cluster inside your own VPC. You wire it to a vector database. You index 15 years of SharePoint. Sovereign, private, fully in your tenant. Then the AI inherits 15 years of Active Directory permission debt: nested security groups, orphaned distribution lists, cross-OU inheritance chains nobody fully understands. Tag each document chunk with a flat access list and it collapses the moment real enterprise identity hits it. RBAC-aware RAG, retrieval that actually respects time-based, conditional, and hierarchical permission inheritance, is the hard and unglamorous part. No vendor does it well out of the box. What most ship is read-only integration: it queries permissions but never inherits your group policies, so ABAC and conditional access quietly fall away. The jurisdiction layer underneath is just as misread. Hosting on a US hyperscaler's Frankfurt region does not make you GDPR-compliant. In March 2026 Austria's data protection authority fined a Vienna fintech EUR 450,000 for routing credit scoring through a US AI API, an unlawful transfer under the CLOUD Act conflict. Banning ChatGPT does not work. One in five organizations has already had a shadow AI breach, costing $670K more than a traditional incident. Building a sovereign alternative people will actually use does. If you are a CISO or CTO scoping private LLM deployment, the permission model is where to start, not the GPU. Save this for your next architecture review. #SovereignAI #PrivateLLM #EnterpriseAI #AIGovernance #DataSovereignty