Implementing Zero Trust with SPIFFE/SPIRE for Securing ML Model Deployment

Description

This project demonstrates the integration of SPIFFE and SPIRE for secure communication in a Flask-based machine learning service. I implemented mutual TLS (mTLS) to ensure both the client and server authenticate each other using certificates. This approach boosts security and trust for data exchanges. It also enables secure API calls for machine learning predictions while ensuring data integrity and privacy.

Utilities Used

• SPIFFE/SPIRE
• Python
• Flask
• mTLS
• numpy, scikit-learn, joblib
• Docker

Project Walk-through

Set Up SPIRE Server


Configure the SPIRE Agent


Create SPIFFE Entry for the Service


Certificate Generation for Mutual TLS (mTLS)


Flask API Implementation and Containerization


Secure API Call with Curl After Another Certificate Generation