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We finally wrapped up all of our decisions by utilizing a simple checklist, decision, and justification matrix to help our organization understand the impact and advantages of investing time and resources...

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This chapter focuses on understanding the key elements of maturing a detection engineering program for developing use cases and leveraging automation. We&...

Welcome to the courseware, where we dive deep into various aspects of automation surrounding detection engineering. Throughout various sections of this book, we’ll be putting concepts to use with follow-along labs and exercises recommended for solidifying and applying knowledge as you progress. In addition, we’ll have spotlights from industry-leading experts who provide supplemental insights that align with the practices that you will encounter.

Now that we’ve established our minimum viable product (MVP) requirements for a successful detection as code technical set of solutions, it’s time to make decisions on the different key components of the engineering program including scope, technology stack selection, and a target roll-out schedule. Your team has different requirements and priorities depending on the organization than other departments.

Detection engineering operations are relied on to create robust detection use cases at scale. As the business grows, the requirements for new detections also grow and it becomes difficult to scale without continued automation. As practitioners, we need to build mechanisms that optimize consistency in detection development.

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Choosing the types of technologies based on organization-approved solutions and stack requirements included version control system, build job running compute, build time testing instrumentation, secrets management, and ensuring adequate support for each enterprise tool that was available in their APIs and an established use case syntax.

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This is the start of the detection life cycle phase where engineering teams should determine what is in scope for the program and what qualifies as a useful detection to develop. What qualifies as useful input depends on what tools and available data are within the enterprise environment to leverage. Using the following table, we can map example inputs with possible detection outputs based on input requirements:

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Now that we have established the life cycle of a use case from a detection engineering perspective, technical systems are needed to implement this to scale. As mentioned, CI/CD pipelines are part of it, but the core components will almost always require the following technical considerations:

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In this chapter, we’ve learned about the core concepts of what’s involved in successfully managing use cases for detection-engineering-focused teams. These include a life cycle that involves establishing requirements, development, testing, implementation, and deprecation. To facilitate that life cycle, we needed to determine what technology components and solutions were able to achieve this in a highly automated capacity.

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Modern version control systems will have similar features among major brands and products. Your organization’s developer team may elect to use different systems for their own workflow preferences or compatible integrations with other secrets management systems, third-party risk, and cost considerations. When practical, consider aligning the detection-as-code program to approved and supported security patterns of the enterprise...

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