Detection-as-Code (DaC): How to Automate By CyberDudeBivash — Cybersecurity & AI

Executive Summary

Traditional detection engineering often suffers from manual processes, ad-hoc rule changes, and lack of version control, leading to inconsistencies, regressions, and missed threats. Detection-as-Code (DaC) applies the principles of modern software engineering—version control, CI/CD, testing, and automation—to the creation, deployment, and management of detection logic in a SOC/SIEM/SOAR environment.

This article provides a full technical breakdown of DaC concepts, architecture, automation workflow, and implementation best practices.

1. What is Detection-as-Code?

Detection-as-Code treats security detection rules, playbooks, parsers, and enrichment logic as code artifacts—stored in a repository, versioned, tested, and deployed automatically.

Core Goals:

Repeatability — Every detection rule is reproducible in any environment.
Auditability — All changes are tracked in version control.
Testing — Detections are validated against positive and negative datasets.
Automation — CI/CD ensures faster and safer deployment.

2. DaC Architecture Overview

sqlCopyEdit┌────────────────────┐
│  Git Repository    │  <- Rules, Playbooks, Parsers, Tests
└───────┬────────────┘
        │ Git push / merge
        ▼
┌────────────────────┐
│  CI/CD Pipeline    │  <- Lint → Unit Test → E2E Test → Deploy
└───────┬────────────┘
        │ Successful tests
        ▼
┌────────────────────┐
│  SIEM/SOAR System  │  <- Auto-sync or API-based update
└────────────────────┘

3. Detection-as-Code Workflow

Step 1 — Rule Development

Write rules in a standardized detection language like Sigma, Splunk SPL, KQL, or YARA-L.
Include metadata:
- Title
- Description
- Severity
- MITRE ATT&CK mapping
- Author
- Change log
- False positive guidance

Example Sigma snippet:

yamlCopyEdittitle: Suspicious PowerShell EncodedCommand
logsource:
  product: windows
  service: powershell
detection:
  selection:
    CommandLine|contains: "-enc"
  condition: selection
level: high
tags: [attack.t1059.001]

Step 2 — Version Control

Store all detection rules, playbooks, and test datasets in Git.
Use branching strategy (e.g., main for production, dev for staging).
Implement pull request reviews for peer validation.

Step 3 — Testing

Types of Tests:

Positive tests — Known malicious inputs trigger alerts.
Negative tests — Benign events must not trigger alerts.
Performance tests — Ensure rules run within acceptable latency.
Schema validation — Fields match the detection platform’s expected schema.

Example test dataset (positive.json):

jsonCopyEdit{
  "EventID": 4104,
  "CommandLine": "powershell -nop -enc SQBFAFgA"
}

Example negative dataset (negative.json):

jsonCopyEdit{
  "EventID": 4104,
  "CommandLine": "powershell Get-ChildItem C:\\"
}

Step 4 — Continuous Integration (CI)

Use CI tools (GitHub Actions, GitLab CI, Jenkins) to automate:

Linting — Syntax and metadata checks.
Unit testing — Positive/negative dataset validation.
E2E testing — Replay synthetic events in a sandbox SIEM.
Security checks — Scan for exposed secrets in rules/playbooks.

Example GitHub Actions job:

yamlCopyEditname: Detection-as-Code CI
on: [push, pull_request]
jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Install Dependencies
        run: pip install sigmac
      - name: Lint Rules
        run: sigmac --lint rules/
      - name: Run Unit Tests
        run: pytest tests/

Step 5 — Continuous Deployment (CD)

After passing all tests, the CI/CD pipeline deploys detections automatically to SIEM/SOAR via API.
Staging environment → QA review → Production deployment.

Example deployment:

bashCopyEditcurl -X POST "https://siem.api/detections/import" \
     -H "Authorization: Bearer $TOKEN" \
     -F "file=@rules/encodedcommand.yml"

4. Benefits of DaC Automation

✅ Speed — Faster detection deployment and updates.
✅ Quality — Automated tests reduce false positives.
✅ Traceability — Every change has an audit trail.
✅ Consistency — Standardized schema, naming, and tagging.
✅ Resilience — Rollback capability if a detection causes issues.

5. Challenges & Mitigation

Challenge	Mitigation
Inconsistent rule formats	Enforce schema validation in CI
High false positive rate	Add extensive negative test datasets
CI/CD security risks	Use signed commits, secure API tokens
Complex deployment pipelines	Start with staging-only automation before full prod CD

6. Example DaC Repo Structure

bashCopyEditdetections/
  sigma/              # Sigma rules
  splunk/             # SPL queries
  azure_sentinel/     # KQL detections
tests/
  positive/           # Positive test datasets
  negative/           # Negative test datasets
playbooks/
  response/           # SOAR automation scripts
ci/
  pipelines/          # CI/CD YAML files
docs/
  coverage/           # ATT&CK mapping reports

7. Best Practices

Map every detection to MITRE ATT&CK TTPs.
Use automation to verify coverage gaps.
Keep test datasets realistic but anonymized.
Integrate with attack simulation tools (Caldera, Atomic Red Team) for E2E validation.
Automate alert enrichment (asset owner, geo-IP, threat intel context).

Conclusion

Detection-as-Code transforms SOC detection engineering from manual, error-prone workflows into a scalable, automated, and reliable process.
By combining version control, CI/CD, and automated testing, security teams can detect threats faster, with fewer false positives, and maintain a provable detection posture.

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