Design and Implement Fine-Grained Authorization for RAG with Auth0 for AI Agents

Implement Authorization for RAG by using Auth0 FGA to model complex permissions based on the relationships between users, resources, and actions.

Cancel Apply

rate limit

Code not recognized.

About this course

Overview

Enhance the security of your Retrieval-Augmented Generation (RAG) pipelines with Auth0 Fine-Grained Authorization (FGA). While Large Language Models (LLMs) are powerful, they are often granted overly broad or unnecessary permissions, allowing them to perform actions or expose data that they should not. You will explore how to integrate Auth0 FGA with the Auth0 AI SDK to ensure your AI agents only retrieve and generate answers based on data the specific user is authorized to see, moving beyond the inadequacy of standard Role-Based Access Control (RBAC).

By the end of this course, you will be equipped to design a secure RAG pipeline that maps context to relationship tuples, ensuring robust data privacy in AI-driven interactions.

Who should take this course?

This course is designed for AI engineers, backend developers, and security architects who are building LLM-powered applications and need to implement precise, scalable authorization models to protect sensitive data retrieval.

Skills you’ll gain

  • Identify how RAG architectures improve LLM accuracy by retrieving external, domain-specific, or up-to-date data.
  • Identify why RAG pipelines should be secured with Fine-Grained Authorization (FGA) rather than rigid RBAC systems.
  • Understand the integration points of FGA within a standard RAG pipeline.
  • Understand how to secure your RAG tool using Auth0 FGA and the Auth0 AI SDK.
  • Recognize how to map RAG context effectively to FGA relationship tuples for precise access control.

Key info

  • Prerequisites: Familiarity with LLM concepts, RAG architectures, and Auth0 FGA.
  • Format: On-demand learning
  • Series: Auth0 for AI Agents
  • Duration: 10 minutes

About this course

Overview

Enhance the security of your Retrieval-Augmented Generation (RAG) pipelines with Auth0 Fine-Grained Authorization (FGA). While Large Language Models (LLMs) are powerful, they are often granted overly broad or unnecessary permissions, allowing them to perform actions or expose data that they should not. You will explore how to integrate Auth0 FGA with the Auth0 AI SDK to ensure your AI agents only retrieve and generate answers based on data the specific user is authorized to see, moving beyond the inadequacy of standard Role-Based Access Control (RBAC).

By the end of this course, you will be equipped to design a secure RAG pipeline that maps context to relationship tuples, ensuring robust data privacy in AI-driven interactions.

Who should take this course?

This course is designed for AI engineers, backend developers, and security architects who are building LLM-powered applications and need to implement precise, scalable authorization models to protect sensitive data retrieval.

Skills you’ll gain

  • Identify how RAG architectures improve LLM accuracy by retrieving external, domain-specific, or up-to-date data.
  • Identify why RAG pipelines should be secured with Fine-Grained Authorization (FGA) rather than rigid RBAC systems.
  • Understand the integration points of FGA within a standard RAG pipeline.
  • Understand how to secure your RAG tool using Auth0 FGA and the Auth0 AI SDK.
  • Recognize how to map RAG context effectively to FGA relationship tuples for precise access control.

Key info

  • Prerequisites: Familiarity with LLM concepts, RAG architectures, and Auth0 FGA.
  • Format: On-demand learning
  • Series: Auth0 for AI Agents
  • Duration: 10 minutes