How do you build an offline AI assistant that respects enterprise data access controls?
Building access-controlled offline AI assistants requires integrating the AI system with existing identity and permission infrastructure. Each document in the RAG knowledge base is tagged with access control metadata from the source system such as SharePoint permissions, Confluence space restrictions, or file system ACLs. When a user queries the assistant, their identity and role are verified against the organization directory service. The retrieval step filters documents based on the user permissions before providing context to the LLM. The system enforces that the LLM can only generate answers based on documents the requesting user is authorized to access. Audit logs record every query, the documents retrieved, and the response generated. This prevents information leakage between departments or security levels while providing personalized AI assistance to each employee.