Detection as Code: Revolutionizing Cybersecurity with Automation and Precision

In today’s advanced threat landscape, Detection as Code stands out as a game-changing approach. By treating detection logic as code, organizations can achieve scalability, automation, and precision in threat detection. This blog will delve into the technical details of TDaC, integrating tools like VECTR, Atomic Red Team, and CALDERA, and provide detailed code examples and workflows for implementation.

What is Detection as Code?

Detection as Code leverages software development practices to create and manage detection logic. Core features include:

• 1. Version Control: Manage detection rules with Git repositories.
  2. Automated Testing: Simulate threats and validate detection effectiveness.
  3. Continuous Deployment: Push validated detections to SIEMs/EDRs.
  4. Scalability: Easily replicate detections across environments.

Core Tools for Detection as Code Implementation

1. VECTR

• • – Purpose: Threat detection validation and MITRE ATT&CK coverage tracking.
  • – Key Feature: Automates purple teaming processes and provides ATT&CK matrices.
  • – Integration: RESTful API for automation.

2. Atomic Red Team

• • – Purpose: Provides pre-built adversary behavior simulations.
  • – Key Feature: YAML-based modular test definitions.
  • – Integration: Easily callable from CI/CD pipelines.

3. CALDERA

• • – Purpose: Automated adversary emulation.
  • – Key Feature: Plugin architecture to extend its capabilities.
  • – Integration: Integrates seamlessly with CI/CD for testing.

Technical Implementation of Detection as Code

1. Setting Up a Repository for Detection as Code

Structure the repository to store detection rules, attack simulations, and CI/CD configurations:

threat-detection-as-code/
├── detections/
│   ├── siem/
│   │   ├── t1218.yml
│   │   ├── t1059.json
│   ├── edr/
│       ├── t1059.edr.json
├── tests/
│   ├── atomic_tests/
│   │   ├── T1218.010.yml
│   │   ├── T1059.001.yml
├── pipelines/
│   ├── ci_cd.yaml
├── scripts/
│   ├── run_tests.sh
│   ├── validate_rules.sh
│   ├── deploy_rules.sh

2. Writing Detection Rules

Detection rules should be modular and mapped to MITRE ATT&CK techniques.

Example SIEM Rule (YAML – Splunk Detection)

name: Detect PowerShell Obfuscation
id: T1059.001
type: query
description: Detects obfuscated PowerShell commands.
tactics:
  - Execution
techniques:
  - id: T1059.001
    name: PowerShell
query: >
  `index=main sourcetype=win_event_log 
   (EventCode=4688 OR EventCode=4104) 
   CommandLine="*FromBase64String*" 
   OR CommandLine="*iex*"`
trigger: 5 events within 5 minutes

Example EDR Rule (JSON – Microsoft Defender)

{
  "name": "Detect Suspicious DLL Injection",
  "id": "T1055",
  "description": "Detects potential DLL injection attempts.",
  "query": "DeviceProcessEvents | where ProcessCommandLine contains '.dll' and InitiatingProcessFileName contains 'explorer.exe'",
  "severity": "high",
  "mitre_tactic": "Defense Evasion",
  "mitre_technique": "T1055"
}

3. Simulating Attacks

Use Atomic Red Team to simulate specific MITRE ATT&CK techniques.

Example: Simulating T1059.001 (PowerShell)

Install Atomic Red Team and run a test:

# Clone Atomic Red Team repository
git clone https://github.com/redcanaryco/atomic-red-team.git

# Execute a PowerShell technique
Invoke-AtomicTest T1059.001 -TestNumbers 1

YAML Definition for T1059.001

atomic_tests:
  - name: Obfuscated PowerShell Command
    executor:
      name: powershell
      command: |
        $enc = [Convert]::ToBase64String([System.Text.Encoding]::UTF8.GetBytes('Write-Output "Hello World"'));
        powershell.exe -EncodedCommand $enc
    supported_platforms:
      - windows

4. Automating Detection Validation

Integrate detection testing into a CI/CD pipeline using tools like GitHub Actions.

CI/CD Pipeline Example

name: CI/CD for Threat Detection
on:
  push:
    branches:
      - main
jobs:
  test-detections:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout repository
        uses: actions/checkout@v3

      - name: Install dependencies
        run: |
          sudo apt update
          sudo apt install -y python3-pip
          pip3 install vectr-api-client

      - name: Simulate attacks with Atomic Red Team
        run: |
          ./scripts/run_tests.sh

      - name: Validate detection rules
        run: |
          ./scripts/validate_rules.sh

      - name: Deploy validated rules
        run: |
          ./scripts/deploy_rules.sh

5. Real-Time Monitoring and Continuous Improvement

Deploying Detection Rules to SIEM

Use APIs for automatic rule deployment. Example for Splunk:

curl -k -u admin:password https://splunk-server:8089/servicesNS/admin/search/saved/searches \
  -d name="Detect PowerShell Obfuscation" \
  -d search="index=main sourcetype=win_event_log (EventCode=4688 OR EventCode=4104) CommandLine=\"*FromBase64String*\""

Integrating with VECTR

Automatically upload test results to VECTR for analysis:

from vectr import VectrClient

client = VectrClient(api_url="https://vectr-server/api", api_key="your_api_key")
test_results = client.upload_test_results("test_results.json")
print(test_results.status)

Challenges and Solutions

• 1. False Positives: Mitigate by testing against benign scenarios.
  2. Tool Integration Complexity: Use modular scripts and pipelines for simplicity.
  3. Rule Maintenance: Automate updates with scheduled pipelines.

Conclusion

Detection-as-Code transforms reactive security into proactive, automated, and precise detection engineering. By leveraging tools like VECTR, Atomic Red Team, and CALDERA, along with automated pipelines, organizations can achieve a robust, scalable threat detection program.

Adopt TDaC to empower your security teams with precision and efficiency. The future of cybersecurity lies in automation, and with Detection as Code, you are building a foundation to stay ahead of evolving threats.

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