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This course will first teach the theory behind security operations and detection engineering
Updated on Sep, 2026
Teaching and Academics , Engineering,
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Welcome to the Detection Engineering Masterclass: Part 2!
Don't purchase if you haven't gone through Part 1!
Two-Part Course Overview:
This course will first teach the theory behind security operations and detection engineering. We’ll then start building out our home lab using VirtualBox and Elastic’s security offering. Then we’ll run through three different attack scenarios, each more complex than the one prior. We’ll make detections of our attacks and learn how to document our detections. Next, we’ll dive more into coding and Python by writing validation scripts and learning how to interact with Elastic through its API. Wrapping everything up, we’ll host all our detections on GitHub and sync with Elastic through our own GitHub Action automations. As a cherry on top, we’ll have a final section on how to write scripts to gather important metrics and visualizations.
This course takes students from A-Z on the detection engineering lifecycle and the technical implementation of a detection engineering architecture.
While this course is marketed as entry-level, any prerequisite knowledge will help in the course's learning curve. Familiarity with security operations, searching logs, security analysis, or any related skillset will be helpful (but ultimately not required).
This is part two of a two-part series on Detection Engineering! This course is meant to kickstart anyone interested in security analysis, detection engineering, and security architecture.
The first part is the meat of the course, where we will go over:
Detection Engineering Theory.
Working with Logging and our SIEM.
Running Attack Scenarios to generate logs and create alerts.
Learn how to use Atomic Red Team for testing.
The second part deals with detection as code philosophies, which will be very Python and GitHub heavy (but don't worry! I'll walk you through everything step by step.)
The entire course runs ~11 or so hours in length, but should take ~20-40 hours to complete fully. All code written will be available on the course GitHub in case you'd like to skip the Python-heavy sections.
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Run offensive tests.
Save alerts using a standardised template.
Enforce template data through code.
Programmatically push the alerts to the SIEM.
Run periodic metrics on the detection data.
The ability to run 2-3 VMs on a local machine:
Minimum Requirements:
Hard Drive Space: 50GB
Recommended Requirements:
Hard Drive Space: 50GB+
Check out the detailed breakdown of what’s inside the course
My name is Anthony Isherwood. I am a seasoned security professional with past roles in incident response, vulnerability management, SIEM engineering, security architecture, SOC coaching, and consulting. I currently enjoy working as a Lead Detection Engineer for a large media company, focusing on detection creation, automation, and adversary emulation.
I have taken red team courses and certs such as TCM's own Practical Ethical Hacking course, VirtualHackingLabs, and obtained the OSCP. In addition, I also obtained the GIAC Reverse Engineering Malware GREM certification and have a couple of lapsed Comptia certs such as the Security+ and CySA+.
I truly love this field! My goal is to enable others to accelerate their growth and enjoy the field as much as I do.
Outside of my professional work, I enjoy lifting in my home gym or playing some games to unwind at night. I have a beautiful family, a wife and a son, who always drive me to be the best version of myself I can be. A special shoutout to my wife, who shouldered extra responsibility as I was developing and creating this course!
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