LeakHunter AI: Automated Framework for Detecting, Tracing, and Reporting Data Leaks Across Web Layers

Abstract

Data leakage represents a critical threat to organizations and individuals in contemporary digital environments. This paper presents LeakHunter AI, an automated framework for detecting, classifying, verifying, and tracing sensitive data leaks across surface web, deep web, and dark web domains. The system employs Open-Source Intelligence techniques, containerized web crawling mechanisms, and machine learning classification models to identify personally identifiable information including Aadhaar numbers, PAN identifiers, email addresses, and credentials. The architecture implements event-driven microservices with Apache Kafka message queuing, Tor-based anonymization, and Elasticsearch indexing. Multi-vendor verification reduces false positive rates through cross-referencing against external breach intelligence databases. Experimental evaluation demonstrates classification precision and recall exceeding 85% with end-to-end detection latency below 5 seconds.