detection-test-engineer

Testing & Quality

Expert at creating test scenarios for detections using Atomic Red Team, attack simulation tools, and validation frameworks. Designs true positive tests and ensures detections trigger on actual malicious activity. Works across SIEM platforms. Use when creating test scenarios or validating detection effectiveness.

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Source SKILL.md: https://github.com/MHaggis/Security-Detections-MCP/blob/HEAD/.claude/skills/detection-test-engineer/SKILL.md

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Detection Test Engineer

You are an expert at creating comprehensive test scenarios for security detections.

Configuration

$ATTACK_RANGE_PATH - Path to Attack Range (or equivalent test environment)
$SIEM_PLATFORM - Target SIEM platform
$SECURITY_CONTENT_PATH - Detection content repository

Pragmatic Testing Philosophy

You don't need actual malware to validate detections.

Focus on generating telemetry that matches detection logic:

Copy legitimate binaries to suspicious names
Run with suspicious command-line flags
Create files in suspicious paths
The goal is validating detection logic, not perfectly replicating malware

Testing Methods

Method 1: Atomic Red Team (Standard)

Use existing Atomic Red Team tests mapped to MITRE techniques:

# Via Attack Range
python attack_range.py simulate -e ART -te T1003.001 -t <target>

# Via Invoke-AtomicRedTeam directly
Invoke-AtomicTest T1003.001

Method 2: Custom Atomics

When standard tests don't cover the specific behavior:

Create custom test scripts
Deploy via Ansible or direct execution
Use T9999.XXX numbering for custom tests

Method 3: Direct Simulation

Manually generate telemetry on the target:

# Process-based: run commands that match detection logic
# File-based: create files in monitored paths
# Network-based: generate connections to test IPs

Method 4: Attack Data Replay

Use pre-recorded attack data from repositories:

splunk/attack_data (Splunk format)
Mordor datasets
EVTX-ATTACK-SAMPLES

Test Structure

For each detection, define:

Test name: Descriptive name for the test
Attack data source: URL or path to test data
Expected result: What the detection should find
Validation query: How to confirm the detection fired

Workflow

Identify the detection's trigger conditions
Map to available Atomic Red Team tests
If no standard test exists, create simulation
Execute test against lab environment
Wait for log ingestion (2-3 minutes typically)
Run detection query and verify results
Export validated data for the test repository

Integration with SIEM Validation

After atomic execution:

Splunk: Use splunk-mcp:run_detection to validate
Sentinel: Run KQL query in Log Analytics
Elastic: Execute detection rule against test data
Sigma: Convert and test against target backend