Detection-as-Code in IBM QRadar

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Daniel Koifman

1:25:28

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1 - Introduction

1 -Introduction.mp4

01:54

2 - Course resources.html

Files.zip

2 - Understanding detection-as-code

1 -What is DAC.mp4

02:22

2 -DAC in the context of QRadar.mp4

03:28

3 - Coding Part 1 - QRadar side

1 -PyQt6 Skeleton.mp4

02:52

2 -Pulling rules from QRadar via API.mp4

08:50

3 -Exporting rules from QRadar via API.mp4

24:49

4 -Importing rules to QRadar via API.mp4

12:30

4 - Coding Part 2 - Github side

1 -Github Integration.mp4

24:59

5 - Course end

1 -Final words.mp4

03:44

Description

Learn how to implement a Detection-as-Code practice in the context of IBM QRadar, Github and Python

What You'll Learn?

Implement detection-as-code methodologies within IBM QRadar to enhance security operations efficiency.
Automate deployment of detection rules in IBM QRadar
Understand how Detection-as-code works in the context of QRadar
Implement detection-as-code functionality using Python

Who is this for?

Security analysts and engineers familiar with IBM QRadar looking to automate and code detection rules.

SOC professionals aiming to integrate detection-as-code methodologies into their security operations.

Developers and IT specialists interested in enhancing threat detection by coding custom rules in IBM QRadar.

What You Need to Know?

Recommended basic experience in Python

Recommended basic experience in QRadar

More details

Description
Hi everyone, and welcome to my 2nd course - "Detection-as-Coode in IBMÂ 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 QRadarâ€™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 organizationâ€™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!
Who this course is for:
Security analysts and engineers familiar with IBM QRadar looking to automate and code detection rules.
SOC professionals aiming to integrate detection-as-code methodologies into their security operations.
Developers and IT specialists interested in enhancing threat detection by coding custom rules in IBM QRadar.

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Python

Daniel Koifman

Instructor's Courses

Verified IBM QRadar Subject Matter Expert with experience working at a fortune-500 bank as a Senior Threat Detection Engineer.I am skilled in various areas of cybersecurity, defensive and offensive security, threat hunting/detection engineering, SIEM/SOC (QRadar, Splunk, Sentinel), SIGMA/YARA Rules and Python.Won 3rd place @ Splunk Boss of the SOC V8 EMEA Israel event.Comptia CASP+ certified.

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