Secure Your RAG Pipelines with Auth0 Fine-Grained Access

Are your AI agents inadvertently exposing sensitive data? This learning path provides a specialized, technical solution for developers to implement robust, document-level security within Retrieval Augmented Generation (RAG) pipelines using Auth0 Fine-Grained Authorization (FGA).

This path moves logically from architectural theory to practical application and validation. You will first learn to distinguish why traditional RBAC is insufficient for AI agents, then design a relationship-based authorization model for unstructured data, and finally, apply these concepts in a hands-on coding lab to secure a RAG pipeline using the Auth0 AI SDK and Auth0 FGA.

Target Audience: This series is designed for identity engineers and AI agent developers who are familiar with Auth0 FGA fundamentals possess a working knowledge of the OpenFGA modeling language and relationship tuples. It is intended for those responsible for securing LLM interactions and ensuring data privacy within enterprise AI applications.

Skills Gained: Upon completing this learning path, you will be able to:

• Evaluate RAG architectures to understand how retrieving domain-specific data improves LLM accuracy.
• Understand why Fine-Grained Authorization (FGA) provides superior security for AI agents compared to standard Role-Based Access Control (RBAC).
• Map unstructured RAG context and data relationships to specific FGA relationship tuples.
• Integrate the Auth0 AI SDK to enforce authorization checks preventing the rendering of sensitive data in AI responses.
• Validate your skills through a hands-on lab and a comprehensivea4 skill badge assessment.