在AWS上构建带行级安全的多租户LLM分析系统
Multi-tenant LLM analytics with row-level security: How we built a secure agent on AWS
AWS 这篇教程手把手教你用 Bedrock 和 Split-Plane SQL 搭建多租户 LLM 分析系统，三层隔离防止数据泄露，值得做企业级 AI 安全的开发者看看。
PAR 在 AWS 上构建了一个生产级多租户 LLM 分析系统，通过三层架构强制执行行级安全：使用 AWS SigV4 进行加密请求签名、在 Amazon Bedrock 上进行语义验证、以及通过 Split-Plane SQL 实现程序化数据隔离。每层独立运行以降低跨租户数据暴露风险，即使 LLM 本身被攻破或操纵也能保护数据。
Multi-tenant LLM analytics with row-level security: How we built a secure agent on AWS
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.
This report is categorized under AITOP's "How-to" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include AWS, Amazon Bedrock, 多租户, 行级安全.
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