Enterprise organizations are adopting Retrieval Augmented Generation (RAG) to unlock insights from company knowledge sources like Microsoft SharePoint, Google Drive, and Atlassian Confluence. However, these knowledge sources contain sensitive information governed by complex permission structures. Making sure that AI-generated answers respect those permissions is one of the hardest challenges in enterprise AI.

In this post, we explore how Amazon Quick and Amazon Bedrock Knowledge Bases solve this challenge through real-time access control list (ACL) enforcement, verifying permissions directly with authoritative sources at query time.

Consider this scenario: A SharePoint site owner creates a knowledge base for their organization. Team members across multiple departments use an AI assistant to get answers from this knowledge base. The critical requirement is that each team member must only receive AI-generated insights from documents they’re authorized to access.

This is a universal enterprise challenge. Organizations want to democratize access to AI-powered insights without compromising their existing security posture. A single unauthorized document surfaced in an AI response could expose confidential strategy documents, unreleased financial data, or sensitive HR information.

A common approach to RAG access control uses a replicate-and-filter approach to enforce document-level permissions. Here’s how it typically works:

While this approach seems reasonable on the surface, it has three fundamental weaknesses.

In this model, the AI system takes sole responsibility for enforcement without being the authoritative source of permissions. This requires data connectors to accurately replicate complex, source-specific ACL logic across various data sources. Each data source has its own unique permission models. Mapping inheritance hierarchies, group memberships, conditional access policies, and deny rules across dozens of connectors is an error-prone undertaking.

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