HIPAA-Compliant Real-Time AI Inference Engine
Architecture LeadThe Problem
The clinical platform required real-time AI processing capabilities to support digital therapeutics, but integrating Large Language Models natively posed extreme risks of leaking Protected Health Information (PHI) to third-party endpoints.
The Constraints
Strict HIPAA compliance. Zero tolerance for PHI leaks. The system had to support burst traffic without degrading the core application performance.
Architecture & Solution
Designed a privacy-first Retrieve-and-Generate (RAG) pipeline. Architected a critical PII/PHI Redaction Layer using Microsoft Presidio, executing before any LLM inference calls to guarantee zero data leakage. Furthermore, I decoupled the heavy inference operations from the main application thread using an asynchronous, event-driven pattern via Apache Kafka, ensuring the UI remained completely responsive during processing.
Outcome & Impact
Deployed a fully compliant AI engine that met all regulatory clinical constraints. The event-driven architecture successfully absorbed 100% of burst traffic volatility, maintaining core system stability.
Impact: Zero-PHI leakage; 400ms inference time.