Deception Engineering and High-Confidence Signals
Learn how decoys create controlled security claims, why a rare trigger still needs context, and how deception can produc...
3 lessons · 2 hr 30 minCourse
Detection Engineering: From Telemetry to Tuned Detections
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Difficulty
All levels
Modules
5
Lessons
9
Time
4 hr 5 min
Language
en
Created by
Threat Intelligence Lab
Updated
16 September 2026
About this course
Detection engineering is the discipline of designing, building, testing, and maintaining the alerts and analytics that help security teams find malicious activity. This course takes students from foundational concepts to job-ready skills. It is written for beginners who are new to security operations and for mediors who want to formalize their knowledge and improve their craft. The course begins with the detection engineering mindset and the detection lifecycle. It then teaches students how to understand telemetry sources, from endpoint logs to network data and cloud activity. Next, students learn to write detection logic, map adversary behavior to the MITRE ATT&CK framework, reduce false positives through tuning and suppression, and validate detections with adversary emulation. Throughout the course, realistic examples, practical trade-offs, and common mistakes are explained in plain language. The final module prepares students to operate detections in production, including monitoring, metrics, documentation, and continuous improvement. By the end, a student will be ready to contribute meaningfully to a detection engineering team.
What you'll learn
- ✓ By the end of this course, learners will be able to design, build, test, tune, and operate threat detections using modern telemetry sources, detection logic, MITRE ATT&CK mapping, and adversary emulation.
Course content
Module 1: Foundations of Detection Engineering
What detection engineering is, why it matters, how it fits into security operations, and the mindset and lifecycle that guide every detection.
What Is Detection Engineering?
Understand the role, value, and responsibilities of a detection engineer, and how detection engineering differs from threat hunting and incident response.
The Detection Engineering Lifecycle
Learn the end-to-end lifecycle of a detection, from requirements and design through development, testing, deployment, and continuous tuning.
Module 2: Telemetry and Data Sources
Understand the data that powers detections: endpoint telemetry, network data, logs, cloud activity, and the data quality issues that make or break a detection.
Understanding Telemetry Sources
Explore the four main pillars of detection data: endpoint, network, cloud, and application logs. Learn what each source reveals and where it falls short.
Data Quality and Detection Blind Spots
Learn how missing fields, broken collection, inconsistent formats, and blind spots undermine detections, and how to assess data quality before writing rules.
Module 3: Writing Detection Logic and Mapping Adversary Behavior
Learn to translate adversary techniques into detection logic, use detection approaches like signatures and behavior analytics, and map detections to MITRE ATT&CK.
From Technique to Detection Logic
Learn a repeatable thought process for turning a threat technique into concrete detection logic, with a realistic example from technique to rule.
MITRE ATT&CK Mapping and Honest Coverage Claims
Learn how ATT&CK v19 organizes adversary behavior and defensive knowledge, map a detection honestly, and distinguish a technique mapping from proven coverage.
Module 4: Testing, Tuning, and Reducing False Positives
Learn how to validate detections with safe testing and adversary emulation, tune thresholds and exceptions, and systematically reduce false positives without creating false negatives.
Testing Detections with Adversary Emulation
Learn how to validate detections safely by emulating attacker techniques and confirming that your detection fires when it should and stays quiet when it should not.
Tuning Detections and Reducing False Positives
Learn practical techniques for tuning thresholds, adding context, and creating exceptions to reduce noise without introducing dangerous blind spots.
Module 5: Operating Detections in Production
Learn to deploy, document, monitor, and continuously improve detections. Understand detection-as-code, metrics, documentation, and how to build a sustainable detection program.