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implementing soar playbook for phishing

Automate phishing incident response using Splunk SOAR REST API to create containers, add artifacts, and trigger

GitHub
githubcommunityai-agentsclaude-codecloud-securitycybersecuritydevsecopsethical-hacking
0.0
0 installs33.4K GitHub starsby mukul975

Skill Introduction

Overview
Automate phishing incident response using Splunk SOAR REST API to create containers, add artifacts, and trigger

Core value

Turns reusable Other know-how into an installable skill, helping users complete github, community, ai-agents, claude-code work faster.

Target users

  • Developers, testers, and maintainers who handle Other tasks in Focus Code.
  • Teams that already trust workflows or content from mukul975.
  • Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Best practices

  • Read the skill content first to confirm required inputs, expected outputs, and dependencies.
  • Try it on a small task before relying on it for critical work.
  • Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
  • For external sources, verify the source link, version, and recent maintenance activity.

Best use cases

  • Tasks related to github, community, ai-agents, claude-code that need a reusable execution flow.
  • Converting a community repo, team convention, or personal workflow into day-to-day assistance.
  • Starting from a proven skill instead of writing prompts or procedures from scratch.

Limits and boundaries

  • Results depend on the quality of the original skill content and may need human correction.
  • It does not replace code review, tests, security review, or professional judgment.
  • External tools, APIs, account permissions, and local dependencies still need separate setup.

Differentiation

  • Structured around Other, making it easier to discover and reuse than loose prompt snippets.
  • Marked as GitHub, which helps users judge trust and maintenance expectations.
  • Keeps the original source link available for repository, documentation, or discussion follow-up.
  • Tagged with github, community, ai-agents, claude-code, so it can be filtered by concrete task intent.

Install and use

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focus install implementing-soar-playbook-for-phishing-345261
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SKILL.md

Primary filemarkdown4 KB

name: implementing-soar-playbook-for-phishing description: Automate phishing incident response using Splunk SOAR REST API to create containers, add artifacts, and trigger playbooks domain: cybersecurity subdomain: security-operations tags:

  • soar
  • splunk-phantom
  • phishing
  • incident-response version: '1.0' author: mahipal license: Apache-2.0 nist_csf:
  • DE.CM-01
  • RS.MA-01
  • GV.OV-01
  • DE.AE-02

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"
  }
}

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