Multi-tenant LLM analytics with row-level security: How we built a secure agent on AWS
PAR developed a production-ready multi-tenant analytics system using large language models on AWS that implements strict row-level security. The framework relies on a three-layer architecture designed to keep separate tenant data isolated even if an attacker compromises the underlying model. This system combines cryptographic signing using AWS SigV4, semantic validation on Amazon Bedrock, and programmatic isolation with Split-Plane SQL.
Key Takeaways
- PAR created a production-ready analytics framework powered by large language models on AWS to deliver multi-tenant data analysis with robust row-level security.
To prevent unauthorized cross-tenant access, the design utilizes a three-layer architecture where each layer functions independently.
- This independent operation ensures that sensitive data remains isolated even in scenarios where the language model itself becomes compromised or suffers manipulation.
The security strategy integrates cryptographic request signing through AWS SigV4, semantic validation handled by Amazon Bedrock, and programmatic data isolation implemented via Split-Plane SQL.
- These layered mechanisms ensure that data queries remain strictly partitioned at the database layer, protecting multi-tenant environments against unauthorized prompt injections and cross-tenant data leaks.
PAR designed a production-ready multi-tenant analytics platform using large language models while maintaining row-level security.
- The security model incorporates a three-layer architecture to mitigate the risk of cross-tenant data leaks.
The platform leverages cryptographic request signing via AWS SigV4, semantic validation on Amazon Bedrock, and programmatic isolation using Split-Plane SQL.
- Each protective layer operates independently so that data remains secure even if the language model is manipulated or compromised.
PAR created a production-ready analytics framework powered by large language models on AWS to deliver multi-tenant data analysis with robust row-level security. To prevent unauthorized cross-tenant access, the design utilizes a three-layer architecture where each layer functions independently. This independent operation ensures that sensitive data remains isolated even in scenarios where the language model itself becomes compromised or suffers manipulation.
The security strategy integrates cryptographic request signing through AWS SigV4, semantic validation handled by Amazon Bedrock, and programmatic data isolation implemented via Split-Plane SQL. These layered mechanisms ensure that data queries remain strictly partitioned at the database layer, protecting multi-tenant environments against unauthorized prompt injections and cross-tenant data leaks. PAR designed a production-ready multi-tenant analytics platform using large language models while maintaining row-level security.
The security model incorporates a three-layer architecture to mitigate the risk of cross-tenant data leaks. The platform leverages cryptographic request signing via AWS SigV4, semantic validation on Amazon Bedrock, and programmatic isolation using Split-Plane SQL. Each protective layer operates independently so that data remains secure even if the language model is manipulated or compromised.
For more details please read the original article at AWS Machine Learning.
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