Automating Security Detection Engineering A Hands-On Guide to Implementing Detection As Code
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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.
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Grouped Work ID | dd0ec488-0b9e-c3a0-09e0-bf6582f72878-eng |
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Full title | automating security detection engineering a hands on guide to implementing detection as code |
Author | chow dennis |
Grouping Category | book |
Last Update | 2025-01-24 12:33:29PM |
Last Indexed | 2025-05-22 03:41:31AM |
Book Cover Information
Image Source | default |
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First Loaded | Mar 6, 2025 |
Last Used | May 27, 2025 |
Marc Record
First Detected | Dec 16, 2024 11:30:13 PM |
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Last File Modification Time | Dec 17, 2024 08:29:22 AM |
Suppressed | Record had no items |
MARC Record
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001 | on1436832136 | ||
003 | OCoLC | ||
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020 | |a 1837631425 | ||
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037 | |a 10769219|b IEEE | ||
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049 | |a MAIN | ||
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100 | 1 | |a Chow, Dennis,|e author. | |
245 | 1 | 0 | |a Automating Security Detection Engineering|h [electronic resource] :|b A Hands-On Guide to Implementing Detection As Code /|c Dennis Chow ; foreword by David Bruskin. |
250 | |a 1st edition. | ||
260 | |a Birmingham :|b Packt Publishing, Limited,|c 2024. | ||
300 | |a 1 online resource (253 p.) | ||
500 | |a Description based upon print version of record. | ||
500 | |a Evaluating data security and ROI | ||
505 | 0 | |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 | |
505 | 8 | |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 | |
505 | 8 | |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 | |
505 | 8 | |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 | |
505 | 8 | |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 | |
520 | |a 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. | ||
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650 | 0 | |a Data protection.|9 35538 | |
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700 | 1 | |a Bruskin, David,|e writer of foreword. | |
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