Detection as Code: Key Components, Tools, and More

As software development accelerates, the need to identify threats and respond in real time is greater than ever. Detection as Code (DaC) allows you to write, maintain, and automate your threat detection logic as if it were software code, making security a built-in part of the development pipeline.

This article will explore DaC and how modern code detector systems and automated security tools can benefit your organization. Learn about the tools that facilitate a DaC approach and how Legit Security can support you in adopting this modernized detection strategy.

What Is Detection as Code?

DaC is a modern approach to integrating security detection practices into the software development lifecycle (SDLC). Instead of handling detection separately, DaC treats security controls like software—expressed and maintained as code. This makes it easier to automate the configuration and maintenance of security measures throughout the SDLC.

Aligned with DevSecOps principles, DaC incorporates tools like automated code scanning to proactively integrate security into every development step. This way, security isn't an afterthought but a core component that evolves along with the software, reducing vulnerabilities and improving threat detection.

This method offers several advantages, including writing higher-quality, reusable, and maintainable detections. Organizations can streamline security operations and reduce manual intervention by building a detection pipeline that integrates directly into CI/CD workflows. Additionally, logs of these activities provide an audit trail that enhances traceability.

Key Components and Benefits of Detection as Code

Using advanced methods to detect code vulnerabilities, here are the major components and benefits that make DaC an essential part of modern cybersecurity strategies:

Code Reusability

One of the standout benefits of DaC is the ability to write detection logic once and reuse it across multiple environments. By creating modular code snippets for application security testing, security teams can significantly reduce the time they spend writing redundant rules and ensure consistency across all applications and services.

Automating Workflows

DaC seamlessly integrates with CI/CD pipelines, enabling automatic updates and testing in response to codebase changes. This automation means security measures evolve alongside the software without requiring separate manual updates, reducing human error and keeping detection capabilities in sync with the latest development iterations.

Version Control

Applying a version control system to detection logic means it’s easy to trace, audit, or roll back every change if necessary. With a clear history of security modifications, you can track how detection rules have evolved and why those changes were made.

Scalability

DaC provides a scalable approach to managing cyber vulnerabilities. Easily add new rules, update existing ones, or remove obsolete logic. As the software and threat landscape evolves, DaC scales to meet those needs without introducing complexities or requiring a complete overhaul of existing detection systems.

Team Collaboration

Like traditional software development, DaC encourages collaboration between security, development, and operations teams. Incorporating peer reviews for detection logic helps teams align on quality control and shared objectives. This cross-functional collaboration integrates security considerations from the beginning, leading to fewer coverage gaps.

Increased Accuracy

With modern development practices, DaC reduces false positives and increases threat detection accuracy. Testing detection logic within automated CI/CD workflows means new rules function as intended, minimizing the risk of disruptions and ensuring each security alert is meaningful and actionable.

Detection as Code Tools

These tools streamline security integration into development workflows, making security measures scalable and maintainable. Below are some of the key tools that support a DaC strategy:

GitHub

GitHub can manage detection logic through repositories, enabling version control, peer review, and collaboration. It’s easy to automate security workflows using GitHub Actions and integrated code scanning capabilities, testing and deploying detection logic efficiently.

Sigma-cli

Sigma is a generic signature format for detection rules, and Sigma-cli can create and manage rules that integrate seamlessly into multiple security information and event management (SIEM) systems. Written in the YAML programming language, these rules provide flexibility when applying detection strategies across different platforms, triggering precise security alerts.

ELK Stack

The Elasticsearch, Logstash, and Kibana (ELK) stack can collect, store, and visualize security data. You can write and integrate detection rules with ELK to monitor for threats in real time, offering a flexible solution for managing large datasets.

Splunk

Splunk is a popular data analytics and SIEM tool that can automate the deployment and management of detection logic. By integrating with CI/CD workflows, Splunk helps your security teams monitor changes continuously and respond proactively to new threats.

Support Your Detection as Code Efforts With Legit Security

Security automation systems integrate threat detection directly into your CI/CD pipelines. With Legit Security, you can consolidate, correlate, and prioritize the findings from all your application security tools, making your application security program more efficient and effective.

Modernize your application security strategy with Legit Security. Book a demo today.