---
title: "Multi-tenant LLM analytics with row-level security: How we built a secure agent on AWS"
slug: "multi-tenant-llm-analytics-with-row-level-security-how-we-built-a-secure-agent-on-aws"
date: 2026-06-29
category: tech-pub
tags: [agents, amazon]
language: en
sources_count: 1
featured: false
publisher: AInauten News
url: https://news.ainauten.com/en/story/multi-tenant-llm-analytics-with-row-level-security-how-we-built-a-secure-agent-on-aws
---

# Multi-tenant LLM analytics with row-level security: How we built a secure agent on AWS

**Published**: 2026-06-29 | **Category**: tech-pub | **Sources**: 1

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## TL;DR

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.

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## Summary

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. We demonstrate how each layer operates independently to reduce the risk of cross-tenant data exposure, even when the LLM itself is compromised or manipulated.

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## Why it matters

We demonstrate how each layer operates independently to reduce the risk of cross-tenant data exposure, even when the LLM itself is compromised or manipulated.

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## Key Points

- We demonstrate how each layer operates independently to reduce the risk of cross-tenant data exposure, even when the LLM itself is compromised or manipulated.

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## FAQ

**Q:** What is Multi-tenant LLM analytics with row-level security about?

**A:** 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.

**Q:** Why does it matter?

**A:** We demonstrate how each layer operates independently to reduce the risk of cross-tenant data exposure, even when the LLM itself is compromised or manipulated.

**Q:** What are the key takeaways?

**A:** We demonstrate how each layer operates independently to reduce the risk of cross-tenant data exposure, even when the LLM itself is compromised or manipulated.

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## Related Topics

- [agents](https://news.ainauten.com/en/tag/agents)
- [amazon](https://news.ainauten.com/en/tag/amazon)

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## Sources

- [Multi-tenant LLM analytics with row-level security: How we built a secure agent on AWS](https://aws.amazon.com/blogs/machine-learning/multi-tenant-llm-analytics-with-row-level-security-how-we-built-a-secure-agent-on-aws/) - AWS Machine Learning Blog

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## About This Article

This article is a synthesis of 1 sources, curated and summarized by AInauten News. We aggregate AI news from trusted sources and provide bilingual (German/English) coverage.

**Publisher**: [AInauten](https://www.ainauten.com) | **Site**: [news.ainauten.com](https://news.ainauten.com)

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*Last Updated: 2026-06-30*