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.…