/automating-ioc-enrichment

name: automating-ioc-enrichment
description: 'Automates the enrichment of raw indicators of compromise with multi-source threat intelligence context using
  SOAR platforms, Python pipelines, or TIP playbooks to reduce analyst triage time and standardize enrichment outputs. Use
  when building automated enrichment workflows integrated with SIEM alerts, email submission pipelines, or bulk IOC processing
  from threat feeds. Activates for requests involving SOAR enrichment, Cortex XSOAR, Splunk SOAR, TheHive, Python enrichment
  pipelines, or automated IOC processing.

  '
domain: cybersecurity
subdomain: threat-intelligence
tags:
- SOAR
- enrichment
- IOC
- Cortex-XSOAR
- Splunk-SOAR
- VirusTotal
- automation
- CTI
- NIST-CSF
version: 1.0.0
author: team-cybersecurity
license: Apache-2.0
nist_csf:
- ID.RA-01
- ID.RA-05
- DE.CM-01
- DE.AE-02

Automating IOC Enrichment

When to Use

Use this skill when:

• Building a SOAR playbook that automatically enriches SIEM alerts with threat intelligence context before routing to analysts
• Creating a Python pipeline for bulk IOC enrichment from phishing email submissions
• Reducing analyst mean time to triage (MTTT) by pre-populating alert context with VT, Shodan, and MISP data

**Do not use** this skill for fully automated blocking decisions without human review — enrichment automation should inform decisions, not execute blocks autonomously for high-impact actions.

Prerequisites

• SOAR platform (Cortex XSOAR, Splunk SOAR, Tines, or n8n) or Python 3.9+ environment
• API keys: VirusTotal, AbuseIPDB, Shodan, and at minimum one TIP (MISP or OpenCTI)
• SIEM integration endpoint for alert consumption
• Rate limit budgets documented per API (VT: 4/min free, 500/min enterprise)

Workflow

Step 1: Design Enrichment Pipeline Architecture

Define the enrichment flow for each IOC type:

SIEM Alert → Extract IOCs → Classify Type → Route to enrichment functions
  IP Address → AbuseIPDB + Shodan + VirusTotal IP + MISP
  Domain → VirusTotal Domain + PassiveTotal + Shodan + MISP
  URL → URLScan.io + VirusTotal URL + Google Safe Browse
  File Hash → VirusTotal Files + MalwareBazaar + MISP
→ Aggregate results → Calculate confidence score → Update alert → Notify analyst

Step 2: Implement Python Enrichment Functions

import requests
import time
from dataclasses import dataclass, field
from typing import Optional

RATE_LIMIT_DELAY = 0.25  # 4 requests/second for VT free tier

@dataclass
class EnrichmentResult:
    ioc_value: str
    ioc_type: str
    vt_malicious: int = 0
    vt_total: int = 0
    abuse_confidence: int = 0
    shodan_ports: list = field(default_factory=list)
    misp_events: list = field(default_factory=list)
    confidence_score: int = 0

def enrich_ip(ip: str, vt_key: str, abuse_key: str, shodan_key: str) -> EnrichmentResult:
    result = EnrichmentResult(ip, "ip")

    # VirusTotal IP lookup
    vt_resp = requests.get(
        f"https://www.virustotal.com/api/v3/ip_addresses/{ip}",
        headers={"x-apikey": vt_key}
    )
    if vt_resp.status_code == 200:
        stats = vt_resp.json()["data"]["attributes"]["last_analysis_stats"]
        result.vt_malicious = stats.get("malicious", 0)
        result.vt_total = sum(stats.values())

    time.sleep(RATE_LIMIT_DELAY)

    # AbuseIPDB
    abuse_resp = requests.get(
        "https://api.abuseipdb.com/api/v2/check",
        headers={"Key": abuse_key, "Accept": "application/json"},
        params={"ipAddress": ip, "maxAgeInDays": 90}
    )
    if abuse_resp.status_code == 200:
        result.abuse_confidence = abuse_resp.json()["data"]["abuseConfidenceScore"]

    # Calculate composite confidence score
    result.confidence_score = min(
        (result.vt_malicious / max(result.vt_total, 1)) * 60 +
        (result.abuse_confidence / 100) * 40, 100
    )

    return result

def enrich_hash(sha256: str, vt_key: str) -> EnrichmentResult:
    result = EnrichmentResult(sha256, "sha256")
    vt_resp = requests.get(
        f"https://www.virustotal.com/api/v3/files/{sha256}",
        headers={"x-apikey": vt_key}
    )
    if vt_resp.status_code == 200:
        stats = vt_resp.json()["data"]["attributes"]["last_analysis_stats"]
        result.vt_malicious = stats.get("malicious", 0)
        result.vt_total = sum(stats.values())
        result.confidence_score = int((result.vt_malicious / max(result.vt_total, 1)) * 100)
    return result

Step 3: Build SOAR Playbook (Cortex XSOAR)

In Cortex XSOAR, create an enrichment playbook: 1. **Trigger**: Alert created in SIEM (via webhook or polling) 2. **Extract IOCs**: Use "Extract Indicators" task with regex patterns for IP, domain, URL, hash 3. **Parallel enrichment**: Fan-out to multiple enrichment tasks simultaneously 4. **VT Enrichment**: Call `!vt-file-scan` or `!vt-ip-scan` commands 5. **AbuseIPDB check**: Call `!abuseipdb-check-ip` command 6. **MISP Lookup**: Call `!misp-search` for cross-referencing 7. **Score aggregation**: Python transform task computing composite score 8. **Conditional routing**: If score ≥70 → High Priority queue; if 40–69 → Medium; <40 → Auto-close with note 9. **Alert enrichment**: Write enrichment results to alert context for analyst view

Step 4: Handle Rate Limiting and Failures

import time
from functools import wraps

def rate_limited(max_per_second):
    min_interval = 1.0 / max_per_second
    def decorator(func):
        last_called = [0.0]
        @wraps(func)
        def wrapper(*args, **kwargs):
            elapsed = time.time() - last_called[0]
            wait = min_interval - elapsed
            if wait > 0:
                time.sleep(wait)
            result = func(*args, **kwargs)
            last_called[0] = time.time()
            return result
        return wrapper
    return decorator

def retry_on_429(max_retries=3):
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            for attempt in range(max_retries):