So, the latest drumbeat from the security industry is about something they’re calling “Detection as Code.” Sounds fancy, right? Like something pulled straight from a futurist’s fever dream. But here’s the thing: for the rest of us who’ve been watching Silicon Valley’s circus for two decades, it’s less about the future and more about finally catching up with the past. Specifically, the past few years of actual software engineering practices.
Look, every engineer on planet Earth today ships code through a pipeline. They branch, they test, they get their work reviewed by other humans, and then, and only then, does it go live. If something breaks? Rollback. Simple. What changed? The commit history tells you. This isn’t some grand philosophical statement; it’s just how you build software without making a colossal mess.
Now, let’s pivot to your friendly neighborhood detection engineering team. How do they operate? Rules are scribbled into a UI, maybe copy-pasted from a dusty wiki page. Peer review? Nah, that’s for the plebs. Someone hits ‘save,’ and boom, it’s live. Test cases to make sure the logic actually works before it’s deployed? Don’t be ridiculous. A rollback if a new rule floods the Security Operations Center (SOC) with more noise than a heavy metal concert? Good luck figuring out what went wrong and when.
And when a crucial detection just… stops firing? You might not even notice for weeks. Weeks! This is, by all accounts, a monumental process gap. A gaping, ridiculous hole that the rest of engineering figured out how to manage years ago.
Is This Really a ‘Game-Changer’ for Security?
Rapid7’s pitch for “Detection as Code,” particularly using Terraform, is essentially about importing discipline. They’re arguing that instead of duct-taping rules together in a web interface, you should be treating your detection logic like any other piece of software: version it, test it, review it. Sounds… sane. Almost shockingly so.
They claim it delivers:
- A more reliable process. Every tweak is tracked. You know who did what, when, and why. If things go sideways, you can yank it back faster than a politician backpedals on a promise.
- A safety net of tests. Think unit tests, but for security alerts. These are supposed to catch threats and, perhaps more importantly, not trigger on perfectly legitimate activity. Crucial, that last part, if you don’t want your SOC to burn out from false positives.
- Confidence in what’s deployed. The
terraform planstep is supposed to show you exactly what will change before it hits the live environment. Your entire detection setup becomes an authoritative record, not a sprawling, unmanageable spreadsheet.
The result they’re aiming for? A workflow that doesn’t make security pros tear their hair out trying to troubleshoot why an alert suddenly vanished or why they’re getting an avalanche of junk. They want teams to focus on finding actual threats, not babysitting faulty detection logic.
The Code: What Does This Actually Look Like?
Let’s cut through the marketing fluff. Rapid7 shows off an example using their Terraform provider. It’s… code. Plain old text. Here’s a snippet:
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 = <<-LEQL
from(event_type = process_start_event)
where(
(process.exe_path = /.*\\powershell\.exe$/i
OR process.exe_path = /.*\\pwsh\.exe$/i)
AND process.cmd_line ICONTAINS " -e"
AND process.cmd_line ICONTAINS-ANY [
" JAB", " SUVYI", " SQBFAFgA", " aWV4I"
]
)
LEQL
testcases = [
{
matches = true
payload = jsonencode({
process = {
exe_path = "C:\\Windows\\System32\\WindowsPowerShell\\v1.0\\powershell.exe"
cmd_line = "powershell.exe -ep bypass -e JABjAGwAaQBlAG4AdAA="
}
})
},
{
matches = false
payload = jsonencode({
process = {
exe_path = "C:\\Windows\\System32\\WindowsPowerShell\\v1.0\\powershell.exe"
cmd_line = "powershell.exe -File C:\\Scripts\\backup.ps1"
}
})
}
]
}
}
This is where the rubber meets the road. The LEQL query is the detection logic itself, living in a text file Git can happily track. The techniques field is a nod to MITRE ATT&CK, supposedly keeping coverage maps up to date. And the testcases? Those are your inline validation. If the detection logic doesn’t fire on the matches = true payload or accidentally triggers on the matches = false one, the pipeline is supposed to choke. Good. That’s how it should be.
Why Terraform? Because Everyone Else Uses It.
Rapid7’s bet here is that organizations already use Terraform for managing their cloud infrastructure. If your platform teams are fluent in infrastructure-as-code, then your detection engineers should, in theory, just adopt the same tools and workflows. No need to learn a proprietary, security-specific CLI tool that’ll be obsolete in two years. You’re building on a foundation that’s already widely adopted.
Governance, they say, just happens. You open a pull request. Your teammates can see the proposed logic, the test results, and the expected outcome. They comment, they suggest tweaks, they approve. Every change is recorded. It’s not some separate compliance checklist; it’s just… work.
And for those stuck with mountains of rules already living in a UI? A quick command can apparently import them. terraform query -generate-config-out imports.tf. If it works as advertised, that’s a big plus.
Who Is Actually Making Money Here?
This is where the cynical veteran in me always perks up. Rapid7 is selling a solution, of course. They’re selling their Terraform provider and likely the tooling and support around it. For them, it’s about making their SIEM product stickier, more competitive, and more valuable. They’re betting that enterprises are tired of the detection engineering headache and are willing to pay for a more structured approach.
But the real money, the sustainable money, comes from the teams that adopt this. If this genuinely makes detection engineering more efficient, more reliable, and less prone to error, then organizations will save significant time and resources. Think about the reduction in SOC analyst burnout from false positives, the hours saved by not manually troubleshooting broken rules, and the potential for faster response to actual threats. That’s where the ROI is, not just in a shiny new feature, but in operational efficiency. It’s about turning a costly, chaotic necessity into a well-oiled machine. Or, at least, a machine that’s less likely to explode.
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Frequently Asked Questions
What does Detection as Code actually do?
Detection as Code treats security detection rules like software code. It uses version control systems (like Git) and automation to manage, test, and deploy these rules, bringing software engineering best practices to security operations. This aims to make detections more reliable, traceable, and efficient.
Will this replace my job as a detection engineer?
It’s unlikely to replace detection engineers entirely. Instead, it aims to make their jobs more efficient and less prone to manual errors. The focus shifts from manual rule creation and management to writing, testing, and refining detection logic within a structured code-based framework.
Is this just another buzzword?
While “Detection as Code” sounds like a buzzword, it addresses a genuine and long-standing problem in security operations: the chaotic and manual way detection rules are often managed. By adopting proven software development practices, it offers a concrete approach to solving these issues, rather than just being a marketing term.
