Book Image

Practical Threat Detection Engineering

By : Megan Roddie, Jason Deyalsingh, Gary J. Katz
5 (2)
Book Image

Practical Threat Detection Engineering

5 (2)
By: Megan Roddie, Jason Deyalsingh, Gary J. Katz

Overview of this book

Threat validation is an indispensable component of every security detection program, ensuring a healthy detection pipeline. This comprehensive detection engineering guide will serve as an introduction for those who are new to detection validation, providing valuable guidelines to swiftly bring you up to speed. The book will show you how to apply the supplied frameworks to assess, test, and validate your detection program. It covers the entire life cycle of a detection, from creation to validation, with the help of real-world examples. Featuring hands-on tutorials and projects, this guide will enable you to confidently validate the detections in your security program. This book serves as your guide to building a career in detection engineering, highlighting the essential skills and knowledge vital for detection engineers in today's landscape. By the end of this book, you’ll have developed the skills necessary to test your security detection program and strengthen your organization’s security measures.
Table of Contents (20 chapters)
1
Part 1: Introduction to Detection Engineering
5
Part 2: Detection Creation
11
Part 3: Detection Validation
14
Part 4: Metrics and Management
16
Part 5: Detection Engineering as a Career

Measuring the efficiency of a detection engineering program

The first metric related to efficiency to be discussed, velocity, is productivity over time. Productivity can be defined as the number of detections but in an agile methodology should be measured in terms of story points. Story points are a unit of measure that is defined by each team as a consistent way of identifying how much work is required. Using points provides the added benefit of allowing team members to consider the complexity and uniqueness of the work required to create the detection. Point estimation requires teams to first normalize the value of a story point by reviewing some sample detections together and creating a consensus view of the points required to complete the work. Once there is a common agreement on point values, team members can estimate the amount of work required to complete a task, such as the creation of a new detection or the update of an existing one. The velocity of the team can therefore...