This module provides a comprehensive overview of the detection engineering life cycle, covering phases such as investigation, development, monitoring, and the role of threat intelligence. Learners will gain insights into how to align detection strategies with organizational needs and improve threat response effectiveness.

This module guides learners through setting up a detection engineering test lab using the Elastic Stack. It covers deploying Docker, configuring the Elastic Stack, and implementing detection rules for threat monitoring. Learners will gain hands-on experience in building and managing a secure, scalable lab environment.

This module covers the importance of detection data sources and how to enhance lab environments with telemetry for better threat detection. Learners will explore various data sources, including Windows Event Logs, network telemetry, application logs, and SIEM systems. The module also addresses challenges in data collection and provides practical steps for adding and configuring data sources.

This module guides learners through the process of identifying, prioritizing, and implementing detection requirements within an organization. It covers methods for analyzing data sources, leveraging productivity tools, and assessing detection coverage to optimize security efforts. Learners will gain practical skills in structuring detection strategies based on threat severity and organizational needs.

This module provides learners with hands-on experience in developing static indicator-based detections using the detection engineering life cycle. It covers identifying indicators of compromise, analyzing network and system data, and implementing detection strategies using tools like Sysmon.

This module covers the process of building effective security detections using behavioral indicators rather than traditional threat intelligence. Learners will gain skills in configuring data sources, understanding adversary tools, and implementing detection rules in the Elastic Stack for real-world threat identification.

This module covers the creation and management of detection documentation, the implementation of detection-as-code, and the deployment of detection pipelines to ensure efficient and consistent security operations. Learners will gain hands-on skills in documenting detections, using detection tools, and setting up automated pipelines for rule management and validation.

This module covers techniques for validating security detections through simulated adversary activities. Learners will gain hands-on experience with tools like Caldera and Atomic Red Team to test and improve security operations. The focus is on understanding how to measure and enhance detection coverage in real-world scenarios.

This module explores how threat intelligence is integrated into detection engineering, focusing on its role in requirements discovery, triage, and investigations. Learners will understand how threat assessments shape detection strategies and how to use tools like VirusTotal for threat analysis. The module provides practical knowledge on leveraging internal and external threat intelligence sources.

This module explores the principles and metrics used to assess the efficiency and effectiveness of detection engineering programs. It covers key concepts such as velocity, validation coverage, precision, recall, and ROC curves, providing learners with tools to measure and improve cybersecurity performance. By the end, students will understand how to evaluate and optimize detection strategies.

This module provides insights into the career path of a detection engineer, covering job opportunities, skill development, and industry trends. It also discusses training options, community involvement, and the evolving role of detection engineering in the future of cybersecurity.