Amazon Quick adds real-time ACL checks to RAG with Bedrock Knowledge Bases
An AWS technical post describes how Amazon Quick and Amazon Bedrock Knowledge Bases enforce access control in retrieval-augmented generation. The two-stage design first filters candidates using ACLs stored in the vector index, then verifies each candidate at query time by calling the authoritative source—such as Google Drive APIs via service-account impersonation—so only authorised passages reach the LLM. AWS says this avoids stale or mis-mapped permissions from replicated ACL syncs, and cites Mondelēz International, which it says has deployed Amazon Quick for more than 35,000 employees across four regions.
Permission lag is a common risk when enterprises connect internal knowledge to AI assistants; checking the authoritative permission source at query time is a practical way to balance performance and security.
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.
The business problem
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.
#Amazon Quick#Amazon Bedrock Knowledge Bases#AWS#Google Drive#Microsoft SharePoint#Atlassian Confluence#Mondelēz International