Automating Security Detection Engineering A Hands-On Guide to Implementing Detection As Code

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Average Rating
Published
Birmingham : Packt Publishing, Limited, 2024.
Status
Available Online

Description

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.

More Details

Format
Edition
1st edition.
Language
English
ISBN
9781837631421, 1837631425

Notes

General Note
Description based upon print version of record.
General Note
Evaluating data security and ROI
Description
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.
Local note
O'Reilly O'Reilly Online Learning: Academic/Public Library Edition

Table of Contents

Cover
Title Page
Copyright
Dedication
Foreword
Contributors
Table of Contents
Preface
Part 1: Automating Detection Inputs and Deployments
Chapter 1: Detection as Code Architecture and Lifecycle
Understanding detection life cycle concepts
Establish requirements
Development
Testing
Implementation
Deprecation
Conceptualizing detection as code requirements
Version control systems
API support
Use case syntax
Testing instrumentation
Secrets management
Planning automation milestones
Summary
Further reading
Chapter 2: Scoping and Automating Threat-Informed Defense Inputs
Technical requirements
Scoping threat-based inputs
Parsing indicators and payloads
Lab 2.1
Custom STIX2 JSON parser
Lab 2.2
Automatically block domains with intel feed
Lab 2.3
Integrate malicious hashes into Wazuh EDR
Lab 2.4
Deploy custom IOCs to CrowdStrike
Leveraging context enrichment
Lab 2.5
Analyze and develop custom detections in Google Chronicle
Summary
Further reading
Chapter 3: Developing Core CI/CD Pipeline Functions
Technical requirements
Deploying code repositories
GitHub usage concepts
Branching strategy
Lab 3.1
Create a new repository
Setting up CI/CD runners
Lab 3.2
Deploy a custom IOA to CrowdStrike Falcon
Lab 3.3
CI/CD with Terraform Cloud and Cloudflare WAF
Lab 3.4
Policy as Code with Cloud Custodian in AWS
Lab 3.5
Custom RASP rule in Trend Micro Cloud One
Lab 3.6
Custom detection for Datadog Cloud SIEM with GitHub Actions
Monitoring pipeline jobs
Summary
Chapter 4: Leveraging AI for Use Case Development
Technical requirements
Optimizing generative AI usage
Lab 4.1
Tuning an LLM-based chatbot
Experimenting with multiple AI tools
Lab 4.2
Exploring SOC Prime Uncoder AI
Automating LLM interactions
Lab 4.3
Generating Splunk SPL content from news
Summary
Part 2: Automating Validations within CI/CD Pipelines
Chapter 5: Implementing Logical Unit Tests
Technical requirements
Validating syntax and linting
Lab 5.1
CrowdStrike syntax validation
Performing metadata and taxonomy checks
Lab 5.2
Google Chronicle payload validation
Performing data input checks
Lab 5.3
Palo Alto signature limitation tests
Lab 5.4
Suricata simulation testing
Lab 5.5
Git pre-commit hook protections
Summary
Further reading
Chapter 6: Creating Integration Tests
Technical requirements
Mapping and Using Synthetic Payloads
Lab 6.1
Splunk SPL Detection Testing
Testing In-Line Payloads
Lab 6.2
AWS CloudTrail Detection Tests
Executing Live-Fire Asynchronous Tests
Lab 6.3
CrowdStrike Falcon Payload Testing
Lab 6.4
Deploying Caldera BAS
Summary
Further reading
Chapter 7: Leveraging AI for Testing
Technical requirements
Synthetic testing with LLMs
Lab 7.1
Poe Bot synthetic CI/CD unit testing

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Citations

APA Citation, 7th Edition (style guide)

Chow, D., & Bruskin, D. (2024). Automating Security Detection Engineering: A Hands-On Guide to Implementing Detection As Code (1st edition.). Packt Publishing, Limited.

Chicago / Turabian - Author Date Citation, 17th Edition (style guide)

Chow, Dennis and David, Bruskin. 2024. Automating Security Detection Engineering: A Hands-On Guide to Implementing Detection As Code. Birmingham: Packt Publishing, Limited.

Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)

Chow, Dennis and David, Bruskin. Automating Security Detection Engineering: A Hands-On Guide to Implementing Detection As Code Birmingham: Packt Publishing, Limited, 2024.

Harvard Citation (style guide)

Chow, D. and Bruskin, D. (2024). Automating security detection engineering: a hands-on guide to implementing detection as code. 1st edn. Birmingham: Packt Publishing, Limited.

MLA Citation, 9th Edition (style guide)

Chow, Dennis,, and David Bruskin. Automating Security Detection Engineering: A Hands-On Guide to Implementing Detection As Code 1st edition., Packt Publishing, Limited, 2024.

Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.

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dd0ec488-0b9e-c3a0-09e0-bf6582f72878-eng
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Grouped Work IDdd0ec488-0b9e-c3a0-09e0-bf6582f72878-eng
Full titleautomating security detection engineering a hands on guide to implementing detection as code
Authorchow dennis
Grouping Categorybook
Last Update2025-01-24 12:33:29PM
Last Indexed2025-05-22 03:41:31AM

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MARC Record

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500 |a Evaluating data security and ROI
5050 |a Cover -- Title Page -- Copyright -- Dedication -- Foreword -- Contributors -- Table of Contents -- Preface -- Part 1: Automating Detection Inputs and Deployments -- Chapter 1: Detection as Code Architecture and Lifecycle -- Understanding detection life cycle concepts -- Establish requirements -- Development -- Testing -- Implementation -- Deprecation -- Conceptualizing detection as code requirements -- Version control systems -- API support -- Use case syntax -- Testing instrumentation -- Secrets management -- Planning automation milestones -- Summary -- Further reading
5058 |a Chapter 2: Scoping and Automating Threat-Informed Defense Inputs -- Technical requirements -- Scoping threat-based inputs -- Parsing indicators and payloads -- Lab 2.1 -- Custom STIX2 JSON parser -- Lab 2.2 -- Automatically block domains with intel feed -- Lab 2.3 -- Integrate malicious hashes into Wazuh EDR -- Lab 2.4 -- Deploy custom IOCs to CrowdStrike -- Leveraging context enrichment -- Lab 2.5 -- Analyze and develop custom detections in Google Chronicle -- Summary -- Further reading -- Chapter 3: Developing Core CI/CD Pipeline Functions -- Technical requirements -- Deploying code repositories
5058 |a GitHub usage concepts -- Branching strategy -- Lab 3.1 -- Create a new repository -- Setting up CI/CD runners -- Lab 3.2 -- Deploy a custom IOA to CrowdStrike Falcon -- Lab 3.3 -- CI/CD with Terraform Cloud and Cloudflare WAF -- Lab 3.4 -- Policy as Code with Cloud Custodian in AWS -- Lab 3.5 -- Custom RASP rule in Trend Micro Cloud One -- Lab 3.6 -- Custom detection for Datadog Cloud SIEM with GitHub Actions -- Monitoring pipeline jobs -- Summary -- Chapter 4: Leveraging AI for Use Case Development -- Technical requirements -- Optimizing generative AI usage -- Lab 4.1 -- Tuning an LLM-based chatbot
5058 |a Experimenting with multiple AI tools -- Lab 4.2 -- Exploring SOC Prime Uncoder AI -- Automating LLM interactions -- Lab 4.3 -- Generating Splunk SPL content from news -- Summary -- Part 2: Automating Validations within CI/CD Pipelines -- Chapter 5: Implementing Logical Unit Tests -- Technical requirements -- Validating syntax and linting -- Lab 5.1 -- CrowdStrike syntax validation -- Performing metadata and taxonomy checks -- Lab 5.2 -- Google Chronicle payload validation -- Performing data input checks -- Lab 5.3 -- Palo Alto signature limitation tests -- Lab 5.4 -- Suricata simulation testing
5058 |a Lab 5.5 -- Git pre-commit hook protections -- Summary -- Further reading -- Chapter 6: Creating Integration Tests -- Technical requirements -- Mapping and Using Synthetic Payloads -- Lab 6.1 -- Splunk SPL Detection Testing -- Testing In-Line Payloads -- Lab 6.2 -- AWS CloudTrail Detection Tests -- Executing Live-Fire Asynchronous Tests -- Lab 6.3 -- CrowdStrike Falcon Payload Testing -- Lab 6.4 -- Deploying Caldera BAS -- Summary -- Further reading -- Chapter 7: Leveraging AI for Testing -- Technical requirements -- Synthetic testing with LLMs -- Lab 7.1 -- Poe Bot synthetic CI/CD unit testing
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