AI & Automation in Detection Engineering

If you have spent time creating detections in real-world environments, you already know that writing the rule is the easy part. Researching the detection logic, getting it reviewed, tested, deployed, and trusted is usually the slow work, and it gets cut first when the backlog piles up, affecting detection quality.

This room is about how detection engineers solve that with automation and AI. Automation provides a structured environment for detection engineering, and AI accelerates the work within that structure.

Learning Objectives

By the end of this room, you will be able to:

• Explain why the detection engineering lifecycle is hard to run manually, and identify which stages detection engineers usually automate.
• Describe what Detection-as-Code (DaC) is, and walk through a real review-and-deploy pipeline.
• Identify the most useful applications of AI in detection engineering, along with the risks that come with relying on it.
• Critically review an AI-generated detection rule, spot the gaps it introduces, and explain why human review is still essential.

Learning Prerequisites

Before you start this room, we recommend that you have at least completed:

• Intro to Detection Engineering room and understand the steps of the detection lifecycle.
• Sigma Language room and understand the basics of Sigma syntax.