SOC Automation with Python and ML: Build AI-Powered Threat Detection, Alert Triage, and Incident Response Pipelines for Modern Security Operations

By Kaevren Solisar

Your SOC is drowning in alerts. Machine learning plus Python is the way out - and this book shows you exactly how to build it.

The average security operations center receives thousands of alerts per day and investigates fewer than 5% of them with any depth. The rest get closed as noise or silently missed. Vectra AI documented what changes when you apply ML-driven triage: 144,000 monthly alerts collapsed to 200 actionable cases. Phishing response time dropped from one week to under two minutes. This book builds that architecture from scratch - in Python, against your own SIEM data, deployable in weeks.

SOC Automation with Python and ML is a code-first engineering guide to building the ADAPT Pipeline: a five-stage system that ingests raw SIEM events, scores them with Random Forest and Isolation Forest models, enriches them with VirusTotal and MISP threat intelligence, routes findings to TheHive for case management, and triggers automated containment via firewall and EDR APIs - all within 30 seconds of initial detection.

You will learn to:

- Build a log ingestion and normalization pipeline that standardizes events from Wazuh, Splunk, and Elastic into a 12-field common schema
- Engineer 35-plus behavioral features from raw security events, including cyclic time encoding, LOLBin detection, and rolling-window burst signals
- Train and tune a Random Forest alert classifier that achieves 94-percent precision on production Wazuh data
- Deploy Isolation Forest anomaly detection for zero-day behavioral patterns your classifier has never seen
- Automate alert triage with Python scoring scripts integrated into Splunk or Wazuh using a three-tier routing system
- Write SOAR playbooks in Python that block IPs on Palo Alto firewalls, isolate endpoints via CrowdStrike, and create TheHive cases automatically
- Map every detection to MITRE ATT&CK using mitreattack-python and generate coverage heatmaps that expose detection gaps
- Integrate Python pipelines with TheHive, Cortex, and MISP for end-to-end automated incident response
- Measure pipeline performance using MTTD, MTTR, false positive rate, and ATT&CK coverage with automated Plotly dashboards
- Design a production-ready SOC automation architecture that scales horizontally using Docker, Redis, and circuit breakers

Every technique in this book is validated against real-world deployments. Case studies include the GitHubSOC project achieving 85-percent detection accuracy and 3.2-minute mean time to detect across 151 of 159 MITRE ATT&CK techniques. Each chapter includes working Python code, a decision filter that tells you which approach to use and when, and a lab exercise with a clear target outcome.

For security engineers, detection engineers, and SOC analysts ready to replace manual triage with production Python automation.

Stop triaging alerts. Start engineering their replacement.

• Format: Paperback
• Language: English
• ISBN: B0HJ17Z2W8
• ISBN13: 9798172423116
• Release Date: September 2026
• Publisher: Independently Published
• Length: 374 Pages
• Weight: 1.91 lbs.
• Dimensions: 0.8" x 8.5" x 11.0"