test-environment-builder

name: Test Environment Builder description: Build and manage adversary emulation lab environments for any SIEM. Covers Splunk Attack Range, Elastic Security labs, Azure Sentinel labs, and Docker-based setups. Maps data source requirements to infrastructure components.

Test Environment Builder Skill

Overview

Detection engineering requires test infrastructure that produces the right telemetry. This skill covers building environments that generate the exact log sources your detections need — with options for every major SIEM platform.

Set $SIEM_PLATFORM to focus guidance: splunk, sentinel, elastic, sigma

Lab Options by SIEM

Configuration

Environment variables (set what applies to your stack):

Target placeholders:

• <TARGET_NAME> — Hostname of a lab machine (e.g., ar-win-1)
• <TARGET_IP> — IP address of a lab machine

Data Source to Infrastructure Mapping

This is the core planning table. Before building, identify which data sources your detection needs, then select the infrastructure that produces them.

Build Workflow

Step 1: Determine Required Data Sources

From your detection rule, identify every field and data source referenced:

Splunk (SPL):

`sysmon` EventCode=1 ParentImage=*\\cmd.exe Image=*\\powershell.exe

This needs: Sysmon process creation events → Windows host + Sysmon + Splunk UF.

Sentinel (KQL):

DeviceProcessEvents
| where FileName == "powershell.exe"
| where InitiatingProcessFileName == "cmd.exe"

This needs: Microsoft Defender for Endpoint agent → Log Analytics Workspace.

Elastic (EQL):

process where process.name == "powershell.exe"
  and process.parent.name == "cmd.exe"

This needs: Elastic Agent with Endpoint integration → Elasticsearch.

Step 2: Select Configuration

cd "$ATTACK_RANGE_PATH"
source "$ATTACK_RANGE_VENV/bin/activate"

# Show current config
python attack_range.py show

Step 3: Build

# Build the environment
python attack_range.py build

# Check status
python attack_range.py show

# Destroy when done (saves cost)
python attack_range.py destroy

Build Time Estimates

Cost note: AWS t3.xlarge instances (~$0.17/hr each). A standard 3-VM lab costs ~$0.50/hr. Always destroy when not actively testing.

Example Configurations

Minimal: Windows Endpoint Detection

For testing process creation, PowerShell, registry, and file system detections:

[global]
attack_range_password = <STRONG_PASSWORD>

[splunk_server]
s3_bucket_url = https://attack-range-appbinaries.s3-us-west-2.amazonaws.com

[windows_servers]
windows_server_1_os = "Windows Server 2022"
windows_server_1_sysmon = "1"
windows_server_1_atomic_red_team = "1"

Standard: Windows + Linux

For cross-platform detection testing:

[windows_servers]
windows_server_1_os = "Windows Server 2022"
windows_server_1_sysmon = "1"
windows_server_1_atomic_red_team = "1"

[linux_servers]
linux_server_1_os = "Ubuntu 22.04"
linux_server_1_atomic_red_team = "1"

Network: With Zeek

For network-based detections (DNS, HTTP, TLS):

[windows_servers]
windows_server_1_os = "Windows Server 2022"
windows_server_1_sysmon = "1"

[zeek_server]
install_zeek = "1"

Configuration Schema Reference

Key sections in attack_range.conf:

SIEM-Specific Lab Guides

Splunk: Attack Range (Detailed Above)

Attack Range is the most mature option for Splunk-based detection testing. See the configuration examples and build workflow above.

Elastic Security: Docker Compose Lab

The fastest way to stand up an Elastic detection testing lab:

# Clone Elastic's detection-rules repo (includes Docker setup)
git clone https://github.com/elastic/detection-rules.git
cd detection-rules

# Or use a standalone Elastic stack via Docker Compose
mkdir elastic-lab && cd elastic-lab
cat > docker-compose.yml << 'COMPOSE'
version: '3.8'
services:
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.15.0
    environment:
      - discovery.type=single-node
      - xpack.security.enabled=true
      - ELASTIC_PASSWORD=changeme
    ports:
      - "9200:9200"
    mem_limit: 4g

  kibana:
    image: docker.elastic.co/kibana/kibana:8.15.0
    environment:
      - ELASTICSEARCH_HOSTS=http://elasticsearch:9200
      - ELASTICSEARCH_USERNAME=elastic
      - ELASTICSEARCH_PASSWORD=changeme
    ports:
      - "5601:5601"
    depends_on:
      - elasticsearch
COMPOSE

docker compose up -d
# Kibana available at http://localhost:5601
# Install Elastic Agent on endpoints to forward telemetry

Adding endpoints:

1. In Kibana → Fleet → Add agent
2. Install Elastic Agent on Windows/Linux test VMs
3. Enable "Endpoint Security" integration for process, file, and network telemetry
4. Install Atomic Red Team on the endpoint for testing

Pros: Free, fast startup (~2 min), full Elastic Security features Cons: Single-node (not production-representative), manual endpoint setup

Microsoft Sentinel: Azure Lab

Option 1: Training Lab Solution (ARM template)

Deploy the Azure Sentinel Training Lab which provisions a Log Analytics workspace with sample data:

# Via Azure CLI
az deployment group create \
  --resource-group $AZURE_RESOURCE_GROUP \
  --template-uri "https://raw.githubusercontent.com/Azure/Azure-Sentinel/master/Solutions/Training/Package/mainTemplate.json"

Option 2: Custom Lab with Azure VMs

# Create resource group
az group create --name detection-lab --location eastus

# Create Log Analytics workspace
az monitor log-analytics workspace create \
  --resource-group detection-lab \
  --workspace-name sentinel-lab-ws

# Enable Microsoft Sentinel on the workspace
az sentinel onboarding-state create \
  --resource-group detection-lab \
  --workspace-name sentinel-lab-ws

# Create Windows VM with Defender for Endpoint
az vm create \
  --resource-group detection-lab \
  --name win-target-1 \
  --image Win2022Datacenter \
  --admin-username labadmin \
  --admin-password '<STRONG_PASSWORD>' \
  --size Standard_B2ms

# Install the Azure Monitor Agent (AMA) on the VM
az vm extension set \
  --resource-group detection-lab \
  --vm-name win-target-1 \
  --name AzureMonitorWindowsAgent \
  --publisher Microsoft.Azure.Monitor

# Create data collection rule for Sysmon/Security events
# (Use Azure Portal → Data Collection Rules for easier setup)

Pros: Native Sentinel environment, real KQL testing, integrates with Defender Cons: Requires Azure subscription, costs ~$2-5/hr for VMs + ingestion

DetectionLab (Multi-SIEM Friendly)

cd /opt/DetectionLab/Vagrant
vagrant up
# Builds: DC, WEF, Win10, Fleet server, Splunk
# Pre-configured logging pipeline

Pros: All-in-one, great defaults, includes Fleet for osquery Cons: Heavy (4+ VMs), Vagrant/VirtualBox only, Splunk-focused logging

Adapting for Elastic: After build, install Elastic Agent alongside or instead of Splunk UF. Adapting for Sentinel: Forward events via Azure Monitor Agent to a Log Analytics workspace.

Custom Terraform (Any SIEM)

For teams with existing IaC, build a module that:

1. Provisions compute (EC2, Azure VM, GCP CE)
2. Installs the appropriate logging agent:
   • Splunk: Universal Forwarder
   • Elastic: Elastic Agent
   • Sentinel: Azure Monitor Agent or Microsoft Defender for Endpoint
   • Any (via Sigma): Any agent that produces standard logs
3. Configures log forwarding to your SIEM
4. Installs Atomic Red Team for testing

Tips

• Start small. Build the minimal config that produces the telemetry you need. Add components as requirements grow.
• Snapshot before testing. If your cloud provider supports it, snapshot VMs before running destructive atomics.
• Automate destroy. Set a cron job or CI timeout to destroy labs after N hours to avoid cost surprises.
• Tag everything. Use consistent tags (project=detection-testing, owner=<you>) for cost tracking.
• Match your production SIEM. Test in the same platform you deploy to. A detection validated in Splunk may behave differently in Sentinel due to field mapping differences.