Amazon Quick and Amazon Bedrock Knowledge Bases have implemented a real-time access control list (ACL) enforcement mechanism for enterprise Retrieval-Augmented Generation (RAG) workflows. The architecture combines vector index pre-filtering with query-time API validation directly against source repositories to prevent unauthorized document data from appearing in AI-generated answers.
Limits of Replicated Permission Models
Standard RAG access control systems rely on periodic synchronization jobs that ingest ACL data along with document content, storing access rules as metadata in a vector index. When a user submits a query, the system filters results against this stored snapshot.
This approach creates security vulnerabilities when permissions change between sync cycles. If an employee's access is revoked in connected data platforms like Microsoft SharePoint, Google Drive, or Atlassian Confluence, the AI index retains stale permissions until the next update. Repositories such as Confluence do not emit events when group memberships change, expanding the window for potential exposure. Mapping custom inheritance structures and policy changes across different platforms also leads to connector logic errors.
Two-Stage Query Verification
To resolve permission lag while maintaining search responsiveness, AWS uses a two-stage authorization workflow. The first stage executes semantic search against the vector index while applying cached ACL attributes to generate a reduced set of candidate document passages, limiting downstream API overhead.
In the second stage, Amazon Quick verifies candidate passages in real time by calling target platform APIs. For example, when interacting with Google Drive, the system uses administrator credentials and service account impersonation to confirm current access tokens for the specific user. Passages that fail real-time authorization are removed before context is submitted to the large language model.
Operational Tradeoffs and Enterprise Rollouts
By performing real-time verification only on pre-filtered candidate passages, the architecture avoids the latency and expense of querying external APIs for every item in a large index. Grounding checks and Amazon Bedrock Guardrails provide secondary controls to reduce hallucinations and filter response content.
Enterprise implementations include Mondelēz International, which deployed Amazon Quick across more than 35,000 employees in four regions to enforce document-level authorization rules during AI interactions.
AI-generated editorial illustration; not a documentary photograph.
Source: Rethinking access control for RAG with Amazon Quick and Amazon Bedrock
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