How to build user authentication into your gen AI app-accessing database

The article discusses the integration of generative AI agents into enterprise workspaces, focusing on the data privacy challenges and a proposed solution for securing sensitive user data while using these agents. Generative AI, particularly through Retrieval-Augmented Generation (RAG), offers potential benefits for businesses by enhancing the accuracy and context of responses using proprietary data from databases. However, this poses risks related to data privacy and unauthorized access. To address these concerns, the article introduces a user-centric security design pattern. This approach limits the AI agent’s access to data by using specific tool functions with user authentication. Here’s a summary of the proposed workflow: 1. Authentication and Workflow: - User Authentication: The application retrieves a user’s authentication token and includes it in request headers. - Request Handling: The AI agent uses predefined tool functions to interact with a retrieval service, which verifies the authentication token and queries the database accordingly. - Data Access: The retrieval service ensures that only data relevant to the authenticated user is accessed and returned. 2. Implementation Details: - Application Layer: Contains the AI agent and tool functions that attach user authentication tokens to requests. - Retrieval Service: Acts as a gatekeeper, verifying tokens and querying the database to ensure users access only their own data. The article uses a hypothetical example, Cymbal Air, an airline using RAG for handling flight bookings and user information. By limiting the AI agent’s access through user-authenticated tools, the design ensures that each user’s data remains private and secure. The article also mentions a demo application that showcases this design in action, built using LangChain and Google OAuth for authentication. Interested readers can download and adapt this demo for their own use.