Aegis Dlp: A Unified Endpoint Security Platform With Deep Learning-Based Threat Detection

Modern cyberattacks rarely occur in isolation. A single phishing email can trigger a chain of events-network intrusion, malware deployment, sensitive data access, and ultimately data exfiltration or ransomware. Aegis DLP presents a unified endpoint security platform that integrates multiple defensive capabilities into a single locally deployed system. The platform combines deep learning models, real-time monitoring, and an event-driven architecture to detect and correlate threats across multiple attack stages. This book documents the design and engineering of a complete Data Loss Prevention platform featuring nine integrated security modules including phishing detection, network intrusion detection, sensitive data classification, ransomware-aware file monitoring, device control, malware analysis, and a central correlation engine. Built using transformer models such as RoBERTa with LoRA fine-tuning, multi-dataset IDS training, and a real-time event bus architecture, the system demonstrates how modern AI techniques can enhance endpoint security while maintaining privacy through fully local execution. Aegis DLP provides a practical blueprint for building intelligent security platforms and offers insights into the challenges of integrating machine learning, system architecture, and cybersecurity engineering into a single unified solution.