As Retrieval-Augmented Generation (RAG) systems become more prevalent in modern AI applications, ensuring robust authorization frameworks is critical to maintain data privacy, security, and compliance. This talk explores the unique challenges and best practices in designing secure authorization mechanisms tailored for RAG systems. We will dive into real-world scenarios where sensitive data is at risk and examine how Role-Based Access Control (RBAC) and Relationship-Based Access Control (ReBAC) using OpenFGA can be implemented in RAG architectures with LangChain.

Attendees will learn how to secure the interaction between the retrieval components and generative models while maintaining the system’s efficiency and accuracy. Whether you're an AI architect, security professional, or developer, this session will provide actionable insights to safeguard the future of AI-driven applications.