AI chatbots are transforming organizational interactions by providing personalized support and automating workflows, but they also demand robust authorization mechanisms to protect sensitive data. This guide explores various techniques and providers for implementing granular authorization in AI chatbots, using Pinecone, Supabase, and Microsoft Copilot as examples. Pinecone's metadata filtering for AI applications allows vectors to be tagged with metadata, enabling precise access control based on user roles or departments, while Supabase's row-level security (RLS) is suited for applications requiring strict control over individual database records. Microsoft 365 Copilot and Purview exemplify a comprehensive approach, integrating metadata filtering, identity-based access control, and usage rights enforcement across Microsoft services, ensuring consistent data governance. The choice of authorization strategy depends on the application's data type and security needs, with Pinecone ideal for vector-based data, Supabase for relational data, and Microsoft Copilot for enterprise-level integration. Combining these strategies with a robust authentication system like Descope ensures scalable and secure AI chatbot deployment by managing users, roles, and access control through JWT-based tokens.