Automated & Real-Time Privacy Quantification for Microservices Architectures
TL;DR
This work introduces the Privacy Exposure Index (PEI), a dynamic risk metric that links the detection of sensitive entities directly with their operational context and establishes a foundation for a proactive DevPrivOps lifecycle, demonstrating that semantic-aware observability can replace manual privacy checks and act as the core decision engine for active privacy enforcement.
Abstract
: The decentralized nature of microservice architectures introduces privacy challenges that exceed the capabilities of traditional static auditing. To address this, we present a real-time privacy quantification framework based on the Privacy-sensitive Data Categorization (PsDC) model. By combining Layer 7 Deep Packet In-spection (DPI) with Natural Language Processing (NLP), our methodology continuously inspects the semantic intent of network payloads. We introduce the Privacy Exposure Index (PEI), a dynamic risk metric that links the detection of sensitive entities directly with their operational context. We validated this approach in a Ku-bernetes environment using a custom ingestion engine and analyzer. Experimental results show high fidelity in identifying complex threats, including DNS-based data exfiltration, and successfully isolated high-risk service interactions with localized PEI scores of 5.32. Ultimately, this work establishes a foundation for a proactive DevPrivOps lifecycle, demonstrating that semantic-aware observability can replace manual privacy checks and act as the core decision engine for active privacy enforcement.