Implementing SOAR Playbook for Phishing

Overview

This skill implements a phishing incident response workflow using the Splunk SOAR (formerly Phantom) REST API. When a suspected phishing email is reported, the agent parses email headers and body, creates a SOAR container representing the incident, attaches artifacts containing indicators of compromise (sender address, URLs, IP addresses, file hashes), triggers an automated investigation playbook, and polls for action results.

Splunk SOAR orchestrates and automates security operations through playbooks that chain together investigative and response actions. The REST API at

/rest/container

,

/rest/artifact

, and

/rest/playbook_run

enables programmatic incident creation and automation triggering from external tools, email gateways, and SIEM alerts.

When to Use

• When deploying or configuring implementing soar playbook for phishing capabilities in your environment
• When establishing security controls aligned to compliance requirements
• When building or improving security architecture for this domain
• When conducting security assessments that require this implementation

Prerequisites

• Python 3.9 or later with

  requests

  and

  email

  modules
• Splunk SOAR instance (Cloud or On-Premises) with REST API access
• SOAR API token with permissions to create containers and trigger playbooks
• Network connectivity to SOAR instance on port 443
• A configured phishing investigation playbook in SOAR

Steps

1. Parse the phishing email: Read the email file (.eml format) and extract headers including From, To, Subject, Reply-To, Return-Path, Received, Message-ID, X-Mailer, and authentication results (SPF, DKIM, DMARC). Extract URLs and IP addresses from the email body.

2. Authenticate to SOAR REST API: Use the API token in the

   ph-auth-token

   header to authenticate all REST API requests to the SOAR instance.

3. Create a container: POST to

   /rest/container

   with the incident label, name, description, severity, and status. The container represents the phishing incident and receives a container ID in the response.

4. Add email header artifacts: POST to

   /rest/artifact

   with

   container_id

   and CEF (Common Event Format) fields containing sender address (

   fromAddress

   ), recipient (

   toAddress

   ), subject, originating IP (

   sourceAddress

   ), and Message-ID. Set

   run_automation

   to False for all but the last artifact.

5. Add URL artifacts: For each URL extracted from the email body, create an artifact with CEF field

   requestURL

   and type

   url

   . These artifacts feed into URL reputation checks in the playbook.

6. Trigger the playbook: POST to

   /rest/playbook_run

   with the playbook ID or name and the container ID. This initiates the automated investigation workflow.

7. Poll action results: GET

   /rest/action_run

   filtered by container ID to monitor playbook progress. Poll until all actions reach a terminal state (success, failed, or cancelled).

8. Compile response report: Aggregate playbook action results into a summary report with verdicts from URL reputation, domain reputation, IP geolocation, and email header analysis.

Expected Output

{
  "incident": {
    "container_id": 1542,
    "status": "new",
    "severity": "high",
    "artifacts_created": 5
  },
  "playbook": {
    "name": "phishing_investigate",
    "run_id": 892,
    "status": "success",
    "actions_completed": 8
  },
  "verdict": "malicious",
  "indicators": {
    "sender_domain_reputation": "malicious",
    "urls_flagged": 2,
    "spf_result": "fail",
    "dkim_result": "fail"
  }
}