Zero Chaos: Scaling Detection Engineering at the Speed of Software, with Detection As Code

Every engineering team in your organization ships code through a pipeline. They branch, test, review, and deploy. If something breaks, they roll back. If someone asks "what changed?", the answer is in the commit history. This isn't heroic discipline to process; it's just how software gets built. Now think about how your detection engineering team works. Rules get written in a UI. Maybe copied and pasted from a wiki. There's no peer review; someone clicks "save," and it's live. No test cases validate the logic before deployment. No rollback if something breaks. When an alert suddenly floods your SOC, good luck figuring out what changed and when. When a detection stops firing, you might not notice for weeks. This is, by definition, a process gap . And it's one that the rest of engineering solved years ago. The gap becomes manageable through the five custom rules, listed below. As your detections grow, you need the same discipline that every other engineering team already has. Process Stage How it works in software engineering How it works in detection engineering Storage Git / Version Control UI / Wiki / "Tribal Knowledge" Validation Automated CI/CD Tests "Wait and see if it fires" Review Peer-reviewed Pull Requests Single-user "Save" button Rollback One-click git revert Manual query deletion How does this help my security team? Detection as Code gives your team a structured, repeatable way to build and manage detections with confidence. Instead of relying on manual updates and guesswork, every change is tested, reviewed, and tracked before it reaches production. Before we get into the how , here's why Detection as Code changes the way your team works: A more reliable process. Every change goes through version control and peer review before it goes live. When something goes wrong, you know exactly what changed, when it changed, and who approved it. Roll back in seconds if needed. A safety net of tests. Inline test cases validate detection logic before deployment. Positive tests prove it catches the threat; negative tests prove it doesn't fire on legitimate activity. Confidence in what's deployed. terraform plan previews every change before anything touches production. Terraform state is the authoritative record of your detection estate, not some spreadsheet. The result is a detection workflow your team can trust. Changes are predictable, validated, and fully traceable, so security teams don’t get caught up in troubleshooting and can focus on improving coverage and overall posture. The anatomy of a detection Here is what a detection rule looks like using Rapid7’s Terraform provider . It offers a practical view of how detection engineering teams can use Detection as Code in practice: resource "rapid7_siem_detection_rule" "encoded_powershell" { name = "Encoded PowerShell Command Execution" description = "Detects PowerShell launched with base64-encoded commands" techniques = ["T1059.001"] action = "CREATES_ALERTS" priority = "HIGH" logic = { leql = -LE