Automating Security Detection Engineering:A hands-on guide to implementing Detection as Code

by: Dennis Chow (Author),David Bruskin(Foreword)

Publisher: Packt Publishing

Publication Date: 2024/6/28

Language: English

Print Length: 252 pages

ISBN-10: 1837636419

ISBN-13: 9781837636419

Book Description

Accelerate security detection development with AI-enabled technical solutions using threat-informed defenseKey Features- Create automated CI/CD pipelines for testing and implementing threat detection use cases- Apply implementation strategies to optimize the adoption of automated work streams- Use a variety of enterprise-grade tools and APIs to bolster your detection program- Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionToday's global enterprise security programs grapple with constantly evolving threats. Even though the industry has released abundant security tools, most of which are equipped with APIs for integrations, they lack a rapid detection development work stream. This book arms you with the skills you need to automate the development, testing, and monitoring of detection-based use cases.You'll start with the technical architecture, exploring where automation is conducive throughout the detection use case lifecycle. With the help of hands-on labs, you'll lea how to utilize threat-informed defense artifacts and then progress to creating advanced AI-powered CI/CD pipelines to bolster your Detection as Code practices. Along the way, you'll develop custom code for EDRs, WAFs, SIEMs, CSPMs, RASPs, and NIDS. The book will also guide you in developing KPIs for program monitoring and cover collaboration mechanisms to operate the team with DevSecOps principles. Finally, you'll be able to customize a Detection as Code program that fits your organization's needs.By the end of the book, you'll have gained the expertise to automate nearly the entire use case development lifecycle for any enterprise.What you will lea- Understand the architecture of Detection as Code implementations- Develop custom test functions using Python and Terraform- Leverage common tools like GitHub and Python 3.x to create detection-focused CI/CD pipelines- Integrate cutting-edge technology and operational pattes to further refine program efficacy- Apply monitoring techniques to continuously assess use case health- Create, structure, and commit detections to a code repositoryWho this book is forThis book is for security engineers and analysts responsible for the day-to-day tasks of developing and implementing new detections at scale. If you're working with existing programs focused on threat detection, you'll also find this book helpful. Prior knowledge of DevSecOps, hands-on experience with any programming or scripting languages, and familiarity with common security practices and tools are recommended for an optimal leaing experience.Table of Contents- Detection as Code Architecture and Lifecycle- Scoping and Automating Threat-Informed Defense Inputs- Developing Core CI/CD Pipeline Functions- Leveraging AI for Use Case Development- Implementing Logical Unit Tests- Creating Integration Tests- Leveraging AI for Testing- Monitoring Detection Health- Measuring Program Efficiency- Operating Pattes by Maturity

About the Author

Accelerate security detection development with AI-enabled technical solutions using threat-informed defenseKey Features- Create automated CI/CD pipelines for testing and implementing threat detection use cases- Apply implementation strategies to optimize the adoption of automated work streams- Use a variety of enterprise-grade tools and APIs to bolster your detection program- Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionToday's global enterprise security programs grapple with constantly evolving threats. Even though the industry has released abundant security tools, most of which are equipped with APIs for integrations, they lack a rapid detection development work stream. This book arms you with the skills you need to automate the development, testing, and monitoring of detection-based use cases.You'll start with the technical architecture, exploring where automation is conducive throughout the detection use case lifecycle. With the help of hands-on labs, you'll lea how to utilize threat-informed defense artifacts and then progress to creating advanced AI-powered CI/CD pipelines to bolster your Detection as Code practices. Along the way, you'll develop custom code for EDRs, WAFs, SIEMs, CSPMs, RASPs, and NIDS. The book will also guide you in developing KPIs for program monitoring and cover collaboration mechanisms to operate the team with DevSecOps principles. Finally, you'll be able to customize a Detection as Code program that fits your organization's needs.By the end of the book, you'll have gained the expertise to automate nearly the entire use case development lifecycle for any enterprise.What you will lea- Understand the architecture of Detection as Code implementations- Develop custom test functions using Python and Terraform- Leverage common tools like GitHub and Python 3.x to create detection-focused CI/CD pipelines- Integrate cutting-edge technology and operational pattes to further refine program efficacy- Apply monitoring techniques to continuously assess use case health- Create, structure, and commit detections to a code repositoryWho this book is forThis book is for security engineers and analysts responsible for the day-to-day tasks of developing and implementing new detections at scale. If you're working with existing programs focused on threat detection, you'll also find this book helpful. Prior knowledge of DevSecOps, hands-on experience with any programming or scripting languages, and familiarity with common security practices and tools are recommended for an optimal leaing experience.Table of Contents- Detection as Code Architecture and Lifecycle- Scoping and Automating Threat-Informed Defense Inputs- Developing Core CI/CD Pipeline Functions- Leveraging AI for Use Case Development- Implementing Logical Unit Tests- Creating Integration Tests- Leveraging AI for Testing- Monitoring Detection Health- Measuring Program Efficiency- Operating Pattes by Maturity