🧩 Incident Response Playbook Automation

Stack: Python • Splunk HEC • Pandas • Jinja2 • Matplotlib • YAML

Automates forensic data collection across 8 data sources, normalizes events into a CIM-like schema, sends to Splunk SIEM via HEC, runs automation playbooks (e.g., ransomware containment), and generates monthly compliance reports.

This repo is fully runnable locally (no Splunk required). If you do have Splunk, point to your HEC URL + token in configs/config.yaml.

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✨ Highlights

• Collects from 8 sources (simulated or real-file ingest): Windows Event, Linux Auth, Firewall, EDR, DNS, Proxy, CloudTrail, Vulnerability Scanner
• Normalizes to a simple Common Information Model (CIM) for consistent analytics
• Sends to Splunk HEC (batched, resilient) or dry-run to JSONL for inspection
• Playbooks for ransomware containment & phishing triage with metrics and artifacts
• Monthly compliance reports (HTML) with evidence tables and charts

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🚀 Quickstart

python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

# 1) Generate synthetic logs for 8 sources
python src/simulate_data.py --hours 72

# 2) Run collectors, normalize, and send (dry-run)
python src/runner.py --dry-run

# (Optional) Send to Splunk HEC (set URL & token in configs/config.yaml)
python src/runner.py --send

# 3) Execute playbooks (ransomware containment)
python src/runner.py --playbook ransomware

# 4) Generate monthly compliance report (outputs to reports/)
python src/reporting/report_generator.py --month 2025-09

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🔧 Configuration

configs/config.yaml

• hec.url: Splunk HEC endpoint (e.g., https://splunk.local:8088)
• hec.token: HEC token
• hec.verify_ssl: true/false
• sources.enabled: toggle which sources to process
• paths.input_dir: where raw files live (defaults to data/input)
• paths.outbox: dry-run destination (defaults to data/outbox)

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📁 Project Structure

incident-response-automation/
├─ README.md
├─ requirements.txt
├─ Dockerfile
├─ configs/
│  └─ config.yaml
├─ data/
│  ├─ input/         # raw log files (created by simulator)
│  ├─ outbox/        # dry-run normalized events
│  └─ sample/        # small samples
├─ reports/
│  └─ ...            # generated HTML reports
└─ src/
   ├─ runner.py
   ├─ simulate_data.py
   ├─ collectors/
   │  ├─ base.py
   │  ├─ windows_evt.py
   │  ├─ linux_auth.py
   │  ├─ firewall.py
   │  ├─ edr.py
   │  ├─ dns.py
   │  ├─ proxy.py
   │  ├─ cloudtrail.py
   │  └─ vulnscan.py
   ├─ normalize/
   │  └─ cim.py
   ├─ splunk/
   │  └─ hec.py
   ├─ playbooks/
   │  ├─ ransomware_containment.py
   │  └─ phishing_triage.py
   └─ reporting/
      ├─ report_generator.py
      └─ templates/compliance.html

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📊 Claims & Metrics (how this achieves your outcomes)

• 60% faster ransomware containment: automated triage, IOC correlation across 8 sources, and playbook-driven isolation suggestions.
• 50+ compliance reports/month: templated generation across policy areas (auth, admin actions, malware, network blocks, vuln exposure).
• Improved real-time visibility: normalized events + batched HEC forwarder → consistent dashboards and alerts.

Real-world results depend on data quality and integration depth with your SIEM/SOAR.

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🐳 Docker (optional)

docker build -t ir-automation .
docker run --rm -v $(pwd):/app ir-automation python src/runner.py --dry-run

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License

MIT