Use Case - Retrieval-Augmented Generation | AuthZed

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Use Case

Retrieval-Augmented Generation

Bring access control to every step of your RAG pipeline

Build AI applications that respect data boundaries and scale to billions of documents with proven authorization infrastructure.

AI responses that leak unauthorized data are a security risk

Adding data sources can improve an AI system's accuracy and relevance, but each new data source brings its own authorization requirements. Without proper permission controls, your RAG pipeline risks surfacing confidential data in AI responses.

Enterprise customers connect their data sources expecting their existing permissions to be respected, but implementing each permission model is complex and time-consuming.

Checking permissions for every document during retrieval creates a bottleneck. Even at thousands of documents, authorization performance can become the limiting factor in your pipeline.

Permission changes in source systems must be reflected immediately. Revocations that take minutes or hours to propagate mean unauthorized data has already been exposed.

Without a proper solution, you're choosing between slow AI responses, stale permissions that leak data, or building and maintaining custom authorization logic for every data connector.

Authorization infrastructure that scales with your AI

Filter at retrieval speed

Permission checks complete in milliseconds. Authorization never becomes the bottleneck in your AI pipeline. AuthZed can pre-compute results so queries across millions of documents return instantly, making permission-aware RAG performant at scale.

Model any permission structure

Express Google Drive's folder inheritance, OneDrive's sharing semantics, Box's collaboration rules, or your custom permission model in a single declarative schema. Add new integrations by updating the schema without code changes, re-architecture, or per-customer logic scattered through your codebase.

Real-time permission sync

Keep authorization current as permissions change in source systems. Revocations are reflected immediately across your pipeline, not minutes or hours later. Changes propagate automatically without manual re-indexing.

Operate at proven scale

37+ billion documents with fine-grained permissions. AuthZed handles this in production for one of the world's largest enterprise AI deployments, with reliability that eliminated most of the SRE burden for that team.

Authorization at every pipeline stage

Pre-filter document ingestion

Filter data sources to include only authorized documents before generating embeddings and storing them in your vector database. Prevent unauthorized data from entering your pipeline in the first place.

Permission-aware retrieval

Pre-filter inputs for relevance ranking to maximize the number of authorized documents considered, or post-filter ranked results to remove unauthorized documents. AI responses never include information from documents the user isn't authorized to see.

Filtered context generation

Post-filter vector database query results to ensure only authorized data is used as augmented context for response generation. Maintain data boundaries at the moment before sensitive data reaches your LLM.

Enterprise connector permissions

Replicate permission models from Google Drive, OneDrive, SharePoint, and Box without custom code for each integration. AuthZed's flexible schema expresses any permission structure in a single unified system.

Assessment

Where does your authorization stand?

Evaluate your infrastructure across performance, agility, and risk — get a personalized maturity score with actionable next steps.

Take the Assessment Takes ~2 minutes

Common questions

Ready to build permission-aware AI?

Get in touch to learn how your RAG system can integrate fine-grained permissions, utilize performant authorization checks, and scale securely.