Multi-tenant LLM analytics with row-level security: How we built a secure agent on AWS
Not a pilot. PAR deployed multi-tenant LLM analytics with row-level security across production AWS infrastructure—here's the three-layer architecture that prevents cross-tenant data exposure even when the model is compromised.
Why it matters
As enterprises scale AI agents into production, data isolation and security become engineering problems, not afterthoughts. This case study shows how to architect for multi-tenancy without sacrificing model capability—a blueprint for regulated industries.
The key facts
4 to knowThree-layer architecture: cryptographic request signing (AWS SigV4) + semantic validation (Amazon Bedrock) + programmatic data isolation (Split-Plane SQL)
Designed to prevent cross-tenant data exposure even if LLM is compromised or manipulated
Production-ready multi-tenant system deployed on AWS
Row-level security enforcement at infrastructure level, not application layer
Go to the source
AWS Machine Learning Blogaws.amazon.com
Publisher excerpt: In this post, we show you how PAR built a production-ready multi-tenant LLM analytics system that enforces row-level security through a three-layer architecture: cryptographic request signing with AWS SigV4, semantic validation on Amazon Bedrock, and programmatic data isolation via Split-Plane SQL.…