Access control for RAG and LLMs - live demo

As more businesses adopt Retrieval Augmented Generation (RAG) models to enhance their AI systems, managing access control becomes increasingly crucial. In a recent CNCF Live demo session, Alex Olivier walked viewers through how to implement authorization in RAG architectures using Cerbos PDP, an open-source authorization solution.

This blog unpacks the live, providing insights into RAG architecture, its security challenges, and how Cerbos ensures secure and efficient authorization. If you're looking for ways to install guardrails around your AI systems - read on.

Understanding RAG and its components

RAG is a powerful architecture for augmenting large language models (LLMs) with external data. Here’s a quick breakdown:

• The vector store stores vectorized representations of your data.
• The embedding model converts raw data into vector embeddings.
• The LLM generates responses based on augmented prompts.

Alex emphasized that most RAG setups lack context-awareness regarding user permissions. This creates a risk where AI agents might retrieve and serve data users shouldn't access.

Security challenges in RAG architectures

Alex highlighted several key risks that RAG architectures introduce. Chief among them being unauthorized data exposure. Without proper access controls, LLMs can inadvertently retrieve and provide sensitive information to users who lack the necessary permissions.

This risk is exacerbated by the need for dynamic data access, where LLMs must filter information in real time based on factors like user roles, geographic regions, and specific business rules.

Additionally, traditional role-based access control (RBAC) systems often struggle to scale in modern enterprises, where complex organizational structures and rapidly changing access requirements demand more flexible and granular authorization models.

Why access control matters + demo highlights

As Alex explained, Cerbos acts as a gatekeeper, allowing context-aware authorization decisions at various stages of the RAG pipeline, from initial query processing to final response generation.

Here’s how it works:

• When a user asks a question to an AI chatbot - Cerbos enforces existing permission policies to ensure the user has permission to invoke an agent.
• Before retrieving data, Cerbos creates a query plan that defines which conditions must be applied when fetching data to ensure it is only the records the user can access based on their role, department, region, or other attributes.
• Then Cerbos provides an authorization filter to limit the information fetched from your vector database or other data stores.
• Allowed information is used by LLM to generate a response, making** it relevant and fully compliant** with user permissions.

Conclusion

Implementing access control for RAG and LLM architectures is no longer optional—it's a necessity. Cerbos can be used for this purpose. It helps safeguard sensitive data, meet compliance requirements, and maintain AI performance.

Ready to enhance your RAG security? Explore Cerbos, and our documentation.