automating-ioc-enrichment
Helps your agent automate threat intelligence gathering for indicators of compromise. This reduces analysis time and standardizes outputs.
What it does
This tool automatically gathers information about potential security threats (called 'indicators of compromise' or IOCs). It uses various data sources to add context to these indicators, helping to quickly identify if they are dangerous.
When to use it
- ✓When receiving a security alert in your system (SIEM).
- ✓When processing many suspicious emails at once.
- ✓To speed up the analysis of security alerts.
Example of what to ask your AI
“Please enrich this IP address with threat intelligence: 192.168.1.1”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The tool takes a suspicious item (like an IP address or a file) and checks it against different security databases. It then combines all the information found to provide a risk score.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill automating-ioc-enrichmentOr download just the file:
curl -L https://raw.githubusercontent.com/mukul975/Anthropic-Cybersecurity-Skills/main/skills/automating-ioc-enrichment/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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:
- Trigger: Alert created in SIEM (via webhook or polling)
- Extract IOCs: Use "Extract Indicators" task with regex patterns for IP, domain, URL, hash
- Parallel enrichment: Fan-out to multiple enrichment tasks simultaneously
- VT Enrichment: Call
!vt-file-scanor!vt-ip-scancommands - AbuseIPDB check: Call
!abuseipdb-check-ipcommand - MISP Lookup: Call
!misp-searchfor cross-referencing - Score aggregation: Python transform task computing composite score
- Conditional routing: If score ≥70 → High Priority queue; if 40–69 → Medium; <40 → Auto-close with note
- Alert enrichment: Write enrichment results to alert context for analyst view
Step 4: Handle Rate Limiting and Failures
import time
from functools import wraps
…
FAQ
What are 'Indicators of Compromise' (IOCs)?
These are digital clues, such as IP addresses, domain names, or file hashes, that indicate a system may have been attacked or compromised by malware.
What is 'SOAR'?
SOAR stands for 'Security Orchestration, Automation, and Response'. It's a type of platform that helps automate repetitive security tasks.
What kind of information is collected?
The tool collects data from sources like VirusTotal, AbuseIPDB, Shodan, and MISP to check the indicator's reputation and behavior.
Details
Original description by the author
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 inv
Popularity
- GitHub stars
- 35k
- Ranking position
- #5524
- License
- Apache-2.0
- Updated
- 8/31/2026
Comments
- …
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