
Explore detection as code in IBM QRadar and learn to implement it methodically, then build a simple Python-based program applying detection-as-code components for QRadar.
Integrate detection as code to embed security detections into the software development life cycle with CI/CD, automatic testing and deployment, in a centralized repository for QRadar aligned with DevSecOps.
Examine detection as code considerations in shader, noting rules stored as complex objects rather than text and how APIs enable importing and exporting rules for building DAC building blocks.
Explore the PyQt6 skeleton generated from the Pic editor, including the main window, retranslate UI, and main loop, as a solid starting point for programmatic GUI development.
Build a detection-as-code in IBM QRadar API request using a base URL, JSON headers, and a seq token, fetch analytics API rules, and populate UI with name, enabled, and ID.
Export selected rules from QRadar via api, mapping names to ids, monitor the export task until completion, and save the result as output.zip in xml format.
Learn to import rules into QRadar via API by selecting a zip, posting to extension management, tracking status until completion, and applying overwrite and install options for detection as code.
Explore integrating GitHub as a central detection repository in QRadar by authenticating with a token, listing repository directories, pulling descriptions and readmes, and importing rules from zip files.
Conclude the detection-as-code series by summarizing how to pull rules via API, export and import them, and integrate GitHub into curator, while suggesting improvements and future resources.
Hi everyone, and welcome to my 2nd course - "Detection-as-Coode in IBM QRadar".
This course provides a comprehensive, hands-on introduction to leveraging Detection-as-Code (DaC) principles within IBM QRadar, enabling security professionals to automate and streamline threat detection. Participants will learn how to design, develop, and implement detection rules in a reusable and scalable manner, enhancing the efficiency and consistency of their security operations.
Key topics include building reusable detection rules, leveraging GitHub as a central repository for managing detection content, and integrating DaC methodologies into QRadar workflows. Participants will also explore how to automate the deployment of detection rules.
The course emphasizes practical application through interactive demonstrations and real-world scenarios, ensuring learners gain the skills necessary to build and manage detection mechanisms that can evolve with changing threat landscapes. By the end of the course, participants will be able to develop, deploy, and maintain scalable, automated detection solutions using QRadar’s full capabilities.
This course is ideal for security analysts, administrators, and engineers looking to enhance their QRadar workflows, reduce manual effort, and improve their organization’s threat detection and response capabilities through automation.
I truly hope you will enjoy the material, and that you take some things into your day-to-day career. Thank you!