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

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

Automating Security Detection Engineering: A hands-on guide for implementing Detection as Code Paperback / softback - 2024

by Dennis Chow

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Paperback / softback. New. Accelerate security detection development with AI-enabled technical solutions using threat-informed defense Key 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 Utilize a variety of enterprise grade tools and APIs to bolster your detection program Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionToday's global enterprise security programs grapple with constantly evolving threats. Even though the industry has released several 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. Starting with the technical architecture of where automation can occur throughout a detection use case lifecycle, you’ll then explore hands-on labs to learn how to utilize threat-informed defense artifacts. Next, you’ll create advanced AI-powered CI/CD pipelines to bolster your Detection as Code practices. Along the way, you'll learn how to develop custom code to test and deploy for EDRs, WAFs, SIEMs, CSPMs, RASPs, and NIDS. The book will also guide you on how to develop KPIs for program monitoring and discover collaboration mechanisms to operate the team with DevSecOps principals. Finally, you'll be able to customize a Detection as Code program that fits your organization's needs. As you reach the end of the book, you'll be able to automate nearly the entire use case development lifecycle for any enterprise. What you will learn 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 patterns to further refine program efficacy Apply monitoring techniques to continuously assess use case health Create, structure, and commit detections to a code repository Who this book is forThis book is designed for security engineers and analysts tasked with the day-to-day responsibilities of developing and implementing new detections at scale. Also, if you are working with existing programs that are focused on threat detection, you will find this book helpful. Prior knowledge of DevSecOps, hands on experience with any programming or scripting languages, and knowledge of common security practices and tools, is recommended for an optimal learning experience.
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Details

  • Title Automating Security Detection Engineering: A hands-on guide for implementing Detection as Code
  • Author Dennis Chow
  • Binding Paperback
  • Condition New
  • Pages 252
  • Volumes 1
  • Language ENG
  • Publisher Packt Publishing
  • Publication date 2024-06-28
  • Bookseller's Inventory # B9781837636419
  • ISBN 9781837636419 / 1837636419
  • Weight 0.97 lbs (0.44 kg)
  • Dimensions 9.25 x 7.5 x 0.53 in (23.50 x 19.05 x 1.35 cm)
  • Category Computer - Internet
  • Quantity available 10

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From the publisher

Accelerate security detection development with AI-enabled technical solutions using threat-informed defense

Key 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 eBook

Book Description

Today'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 learn 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 learn

- 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 patterns to further refine program efficacy

- Apply monitoring techniques to continuously assess use case health

- Create, structure, and commit detections to a code repository

Who this book is for

This 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 learning 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 Patterns by Maturity

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