How I Built an Autonomous AI SIEM With 10 Neural Networks in 3 Months The Beginning Three months ago, I started with a simple Python script that could detect port scans. Today, SHARD has 10 neural networks, 13 honeypots, and can autonomously block attacks in real-time. This is the story of how it happened. Month 1: The Foundation The first month was all about getting the basics right. I built: A packet capture engine using Scapy Basic ML classification with XGBoost EventBus architecture for modular communication SQLite storage with date-based partitions 13 honeypots (SSH, MySQL, Redis, MongoDB, FTP, etc.) The biggest challenge was making all the modules communicate reliably. The EventBus went through 5 rewrites before it could handle 1000+ events per second without dropping. Month 2: The Neural Networks This was the hardest month.…