Large language model inference over confidential data using AWS Nitro Enclaves

The article discusses using AWS Nitro Enclaves to enable secure large language model (LLM) inference over confidential data like personally identifiable information (PII) and protected health information (PHI). It outlines the potential privacy risks of deploying LLMs and proposes an architecture leveraging Nitro Enclaves to mitigate these risks.

Specifically, the article covers:

• Overview of LLMs and their use cases, as well as an introduction to AWS Nitro Enclaves and its security benefits
• A solution overview detailing the steps involved in deploying an LLM inside a Nitro Enclave for secure inference over encrypted user data
• Prerequisites and step-by-step instructions for configuring an EC2 instance, Nitro Enclaves, and updating the AWS KMS key policy
• Building and running the enclave image with the LLM model, and an example of securely asking questions containing PII to the enclave-hosted LLM
• Conclusion highlighting the benefits of this approach and potential future enhancements