Secure Multi-Tenant LLM Analytics with Row-Level Security
Summary
This post details how PAR built a production-ready multi-tenant LLM analytics system on AWS, enforcing row-level security through a three-layer architecture: cryptographic request signing, semantic validation, and programmatic data isolation.
Why it matters
For organizations building or deploying multi-tenant AI applications, ensuring stringent data isolation and security is paramount to protect sensitive customer information and maintain compliance.
How to implement this in your domain
- 1Design a multi-layered security architecture for LLM applications, including request signing.
- 2Implement semantic validation on your chosen LLM platform (e.g., Amazon Bedrock).
- 3Utilize programmatic data isolation techniques like Split-Plane SQL for row-level security.
- 4Conduct thorough security audits and penetration testing on each layer of the system.
- 5Establish monitoring and alerting for potential security vulnerabilities or data access anomalies.
Who benefits
Key takeaways
- Multi-tenant LLM analytics require robust row-level security.
- A three-layer architecture enhances data isolation and security.
- Cryptographic signing, semantic validation, and data isolation are key components.
- Independent security layers reduce risks even if the LLM is compromised.
Original post by Anuranjan Mondal
"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 v…"
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