Building Threat Intelligence Feed Integration

When to Use

Use this skill when:

• SOC teams need automated ingestion of threat intelligence feeds into SIEM platforms
• Multiple TI sources require normalization into a common format (STIX 2.1)
• Detection systems need real-time IOC matching against network and endpoint telemetry
• TI feed quality assessment and deduplication processes need to be established

Do not use for manual IOC lookup — use dedicated enrichment tools (VirusTotal, AbuseIPDB) for ad-hoc queries.

Prerequisites

• MISP instance or Threat Intelligence Platform (TIP) for feed aggregation
• STIX/TAXII client library (taxii2-client, stix2 Python packages)
• SIEM platform (Splunk ES, Elastic Security, or Sentinel) with TI framework configured
• API keys for commercial and open-source feeds (AlienVault OTX, Abuse.ch, CISA AIS)
• Python 3.8+ for feed processing automation

Workflow

Step 1: Identify and Catalog Intelligence Sources

Map available feeds by type, format, and update frequency:

Feed Source	Format	IOC Types	Update Freq	Cost
AlienVault OTX	STIX/JSON	IP, Domain, Hash, URL	Real-time	Free
Abuse.ch URLhaus	CSV/JSON	URL, Domain	Every 5 min	Free
Abuse.ch MalwareBazaar	JSON API	File Hash	Real-time	Free
CISA AIS	STIX/TAXII 2.1	All types	Daily	Free (US Gov)
CrowdStrike Intel	STIX/JSON	All types + Actor TTP	Real-time	Commercial
Mandiant Advantage	STIX 2.1	All types + Reports	Real-time	Commercial

Step 2: Ingest STIX/TAXII Feeds

Connect to a TAXII 2.1 server and download indicators:

from taxii2client.v21 import Server, Collection
from stix2 import parse

# Connect to TAXII server (example: CISA AIS)
server = Server(
    "https://taxii.cisa.gov/taxii2/",
    user="your_username",
    password="your_password"
)

# List available collections
for api_root in server.api_roots:
    print(f"API Root: {api_root.title}")
    for collection in api_root.collections:
        print(f"  Collection: {collection.title} (ID: {collection.id})")

# Fetch indicators from a collection
collection = Collection(
    "https://taxii.cisa.gov/taxii2/collections/COLLECTION_ID/",
    user="your_username",
    password="your_password"
)

# Get indicators added in last 24 hours
from datetime import datetime, timedelta
added_after = (datetime.utcnow() - timedelta(days=1)).strftime("%Y-%m-%dT%H:%M:%S.000Z")

response = collection.get_objects(added_after=added_after, type=["indicator"])
for obj in response.get("objects", []):
    indicator = parse(obj)
    print(f"Type: {indicator.type}")
    print(f"Pattern: {indicator.pattern}")
    print(f"Valid Until: {indicator.valid_until}")
    print(f"Confidence: {indicator.confidence}")
    print("---")

Step 3: Ingest Open-Source Feeds

Abuse.ch URLhaus Feed:

import requests
import csv
from io import StringIO

# Download URLhaus recent URLs
response = requests.get("https://urlhaus.abuse.ch/downloads/csv_recent/")
reader = csv.reader(StringIO(response.text), delimiter=',')

indicators = []
for row in reader:
    if row[0].startswith("#"):
        continue
    indicators.append({
        "id": row[0],
        "dateadded": row[1],
        "url": row[2],
        "url_status": row[3],
        "threat": row[5],
        "tags": row[6]
    })

print(f"Ingested {len(indicators)} URLs from URLhaus")

# Filter for active threats only
active = [i for i in indicators if i["url_status"] == "online"]
print(f"Active threats: {len(active)}")

AlienVault OTX Pulse Feed:

from OTXv2 import OTXv2, IndicatorTypes

otx = OTXv2("YOUR_OTX_API_KEY")

# Get subscribed pulses (last 24 hours)
pulses = otx.getall(modified_since="2024-03-14T00:00:00")

for pulse in pulses:
    print(f"Pulse: {pulse['name']}")
    print(f"Tags: {pulse['tags']}")
    for indicator in pulse["indicators"]:
        print(f"  IOC: {indicator['indicator']} ({indicator['type']})")

Abuse.ch Feodo Tracker (C2 IPs):

response = requests.get("https://feodotracker.abuse.ch/downloads/ipblocklist_recommended.json")
c2_data = response.json()

for entry in c2_data:
    print(f"IP: {entry['ip_address']}:{entry['port']}")
    print(f"Malware: {entry['malware']}")
    print(f"First Seen: {entry['first_seen']}")
    print(f"Last Online: {entry['last_online']}")

Step 4: Normalize and Deduplicate

Convert all feeds to STIX 2.1 format for standardization:

from stix2 import Indicator, Bundle
import hashlib

def create_stix_indicator(ioc_value, ioc_type, source, confidence=50):
    """Convert raw IOC to STIX 2.1 indicator"""
    pattern_map = {
        "ipv4": f"[ipv4-addr:value = '{ioc_value}']",
        "domain": f"[domain-name:value = '{ioc_value}']",
        "url": f"[url:value = '{ioc_value}']",
        "sha256": f"[file:hashes.'SHA-256' = '{ioc_value}']",
        "md5": f"[file:hashes.MD5 = '{ioc_value}']",
    }

    return Indicator(
        name=f"{ioc_type}: {ioc_value}",
        pattern=pattern_map[ioc_type],
        pattern_type="stix",
        valid_from="2024-03-15T00:00:00Z",
        confidence=confidence,
        labels=[source],
        custom_properties={"x_source_feed": source}
    )

# Deduplicate across sources
seen_iocs = set()
unique_indicators = []

for ioc in all_collected_iocs:
    ioc_hash = hashlib.sha256(f"{ioc['type']}:{ioc['value']}".encode()).hexdigest()
    if ioc_hash not in seen_iocs:
        seen_iocs.add(ioc_hash)
        unique_indicators.append(
            create_stix_indicator(ioc["value"], ioc["type"], ioc["source"])
        )

bundle = Bundle(objects=unique_indicators)
print(f"Unique indicators: {len(unique_indicators)}")

Step 5: Push to SIEM Threat Intelligence Framework

Push to Splunk ES Threat Intelligence:

import requests

splunk_url = "https://splunk.company.com:8089"
headers = {"Authorization": f"Bearer {splunk_token}"}

for indicator in unique_indicators:
    # Extract IOC value from STIX pattern
    ioc_value = indicator.pattern.split("'")[1]

    # Upload to Splunk ES threat intel collection
    data = {
        "ip": ioc_value,
        "description": indicator.name,
        "weight": indicator.confidence // 10,
        "threat_key": indicator.id,
        "source_feed": indicator.get("x_source_feed", "unknown")
    }

    requests.post(
        f"{splunk_url}/services/data/threat_intel/item/ip_intel",
        headers=headers, data=data, verify=False
    )

Push to MISP for centralized management:

from pymisp import PyMISP, MISPEvent, MISPAttribute

misp = PyMISP("https://misp.company.com", "YOUR_MISP_API_KEY")

# Create event for feed batch
event = MISPEvent()
event.info = f"TI Feed Import - {datetime.now().strftime('%Y-%m-%d')}"
event.threat_level_id = 2  # Medium
event.analysis = 2  # Completed

# Add indicators as attributes
for ioc in unique_indicators:
    attr = MISPAttribute()
    attr.type = "ip-dst" if "ipv4" in ioc.pattern else "domain"
    attr.value = ioc.pattern.split("'")[1]
    attr.to_ids = True
    attr.comment = f"Source: {ioc.get('x_source_feed', 'mixed')}"
    event.add_attribute(**attr)

result = misp.add_event(event)
print(f"MISP Event created: {result['Event']['id']}")

Step 6: Monitor Feed Health and Quality

Track feed effectiveness metrics:

index=threat_intel sourcetype="threat_intel_manager"
| stats count AS total_iocs,
        dc(threat_key) AS unique_iocs,
        dc(source_feed) AS feed_count
  by source_feed
| join source_feed [
    search index=notable source="Threat Intelligence"
    | stats count AS matches by source_feed
  ]
| eval match_rate = round(matches / unique_iocs * 100, 2)
| sort - match_rate
| table source_feed, unique_iocs, matches, match_rate

Key Concepts

Term	Definition
STIX 2.1	Structured Threat Information Expression — standardized JSON format for sharing threat intelligence objects
TAXII	Trusted Automated eXchange of Indicator Information — transport protocol for sharing STIX data via REST API
TIP	Threat Intelligence Platform — centralized system for aggregating, scoring, and distributing threat intelligence
IOC Scoring	Process of assigning confidence values to indicators based on source reliability and corroboration
Feed Deduplication	Removing duplicate IOCs across multiple sources while preserving multi-source attribution
IOC Expiration	Time-to-live policy removing aged indicators (IP: 30 days, Domain: 90 days, Hash: 1 year)

Tools & Systems

• MISP: Open-source threat intelligence platform for feed aggregation, correlation, and sharing
• AlienVault OTX: Free threat intelligence sharing platform with community pulse feeds
• Abuse.ch: Suite of free threat feeds (URLhaus, MalwareBazaar, Feodo Tracker, ThreatFox)
• OpenCTI: Open-source cyber threat intelligence platform supporting STIX 2.1 native storage
• TAXII2 Client: Python library for connecting to STIX/TAXII 2.1 servers for automated indicator retrieval

Common Scenarios

• New Feed Onboarding: Evaluate feed quality, map fields to STIX, configure automated ingestion pipeline
• Multi-SIEM Distribution: Push normalized IOCs from MISP to Splunk, Elastic, and Sentinel simultaneously
• False Positive Reduction: Score IOCs by source count and age, expire stale indicators automatically
• Feed Quality Audit: Compare detection match rates across feeds to identify highest-value sources
• Incident IOC Sharing: Package investigation IOCs as STIX bundle and share with ISACs via TAXII

Output Format

THREAT INTEL FEED STATUS — Daily Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Date:         2024-03-15
Total IOCs:   45,892 active indicators

Feed Health:
  Feed                  IOCs    Matches  Match Rate  Status
  Abuse.ch URLhaus      12,340  47       0.38%       HEALTHY
  AlienVault OTX        18,567  23       0.12%       HEALTHY
  Abuse.ch Feodo        1,203   12       1.00%       HEALTHY
  CISA AIS              8,945   8        0.09%       HEALTHY
  CrowdStrike Intel     4,837   31       0.64%       HEALTHY

Actions Today:
  New IOCs ingested:    1,247
  IOCs expired:         892
  Duplicates removed:   156
  SIEM matches:         121 notable events generated
  False positives:      3 (CDN IPs removed from feed)

Verification Criteria

Confirm successful execution by validating:

• [ ] All prerequisite tools and access requirements are satisfied
• [ ] Each workflow step completed without errors
• [ ] Output matches expected format and contains expected data
• [ ] No security warnings or misconfigurations detected
• [ ] Results are documented and evidence is preserved for audit

Compliance Framework Mapping

This skill supports compliance evidence collection across multiple frameworks:

• SOC 2: CC7.1 (Monitoring), CC7.2 (Anomaly Detection), CC7.3 (Incident Identification)
• ISO 27001: A.12.4 (Logging & Monitoring), A.16.1 (Security Incident Management)
• NIST 800-53: AU-6 (Audit Review), SI-4 (System Monitoring), IR-5 (Incident Monitoring)
• NIST CSF: DE.AE (Anomalies & Events), DE.CM (Continuous Monitoring)

Claw GRC Tip: When this skill is executed by a registered agent, compliance evidence is automatically captured and mapped to the relevant controls in your active frameworks.

Deploying This Skill with Claw GRC

Agent Execution

Register this skill with your Claw GRC agent for automated execution:

# Install via CLI
npx claw-grc skills add building-threat-intelligence-feed-integration

# Or load dynamically via MCP
grc.load_skill("building-threat-intelligence-feed-integration")

Audit Trail Integration

When executed through Claw GRC, every step of this skill generates tamper-evident audit records:

• SHA-256 chain hashing ensures no step can be modified after execution
• Evidence artifacts (configs, scan results, logs) are automatically attached to relevant controls
• Trust score impact — successful execution increases your agent's trust score

Continuous Compliance

Schedule this skill for recurring execution to maintain continuous compliance posture. Claw GRC monitors for drift and alerts when re-execution is needed.