building-threat-intelligence-platform skill

Building Threat Intelligence Platform

Overview

Building a Threat Intelligence Platform (TIP) involves deploying and integrating multiple CTI tools into a unified system for collecting, analyzing, enriching, and disseminating threat intelligence. This skill covers designing TIP architecture using open-source tools (MISP, OpenCTI, TheHive, Cortex), configuring feed ingestion pipelines, establishing enrichment workflows, implementing STIX/TAXII interoperability, and building analyst dashboards for CTI operations.

When to Use

• When deploying or configuring building threat intelligence platform 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

• Docker and Docker Compose for deploying platform components
• Python 3.9+ with pymisp, pycti, thehive4py libraries
• Elasticsearch/OpenSearch cluster for data storage
• Redis and RabbitMQ for message queuing
• Understanding of STIX 2.1 data model and TAXII 2.1 transport
• API keys for enrichment services (VirusTotal, Shodan, AbuseIPDB)

Key Concepts

TIP Architecture Components

1. Collection Layer: Feed ingestion from OSINT, commercial, and internal sources
2. Storage Layer: Elasticsearch/OpenSearch for indexed CTI data with STIX 2.1 schema
3. Analysis Layer: OpenCTI for knowledge graph analysis and MISP for IOC correlation
4. Enrichment Layer: Cortex analyzers for automated IOC enrichment
5. Response Layer: TheHive for case management and incident response integration
6. Sharing Layer: TAXII server for outbound intelligence sharing

Platform Integration Points

• MISP <-> OpenCTI: Bidirectional sync via OpenCTI MISP connector
• OpenCTI <-> TheHive: Alert/case creation from high-confidence indicators
• TheHive <-> Cortex: Automated analysis and enrichment of case observables
• All <-> SIEM: Real-time IOC push to Splunk/Elastic via API or Kafka

Workflow

Step 1: Deploy Platform with Docker Compose

version: '3.8'
services:
  # --- Storage Layer ---
  elasticsearch:
    image: docker.elastic.co/elasticsearch/elasticsearch:8.12.0
    environment:
      - discovery.type=single-node
      - xpack.security.enabled=false
      - "ES_JAVA_OPTS=-Xms2g -Xmx2g"
    ports:
      - "9200:9200"
    volumes:
      - es-data:/usr/share/elasticsearch/data

  redis:
    image: redis:7
    ports:
      - "6379:6379"

  rabbitmq:
    image: rabbitmq:3-management
    ports:
      - "5672:5672"
      - "15672:15672"

  minio:
    image: minio/minio
    command: server /data --console-address ":9001"
    ports:
      - "9000:9000"
      - "9001:9001"

  # --- MISP ---
  misp:
    image: ghcr.io/misp/misp-docker/misp-core:latest
    ports:
      - "8443:443"
    environment:
      - MISP_ADMIN_EMAIL=admin@tip.local
      - MISP_BASEURL=https://localhost:8443
    volumes:
      - misp-data:/var/www/MISP/app/files

  # --- OpenCTI ---
  opencti:
    image: opencti/platform:6.4.4
    environment:
      - APP__PORT=8080
      - APP__ADMIN__EMAIL=admin@tip.local
      - APP__ADMIN__PASSWORD=TIPAdminPassword
      - APP__ADMIN__TOKEN=tip-opencti-token-uuid
      - ELASTICSEARCH__URL=http://elasticsearch:

Step 2: Configure Feed Ingestion Pipeline

from pymisp import PyMISP
from pycti import OpenCTIApiClient
import json

class TIPFeedManager:
    """Manage threat intelligence feed ingestion across platform components."""

    def __init__(self, misp_url, misp_key, opencti_url, opencti_token):
        self.misp = PyMISP(misp_url, misp_key, ssl=False)
        self.opencti = OpenCTIApiClient(opencti_url, opencti_token)

    def configure_osint_feeds(self):
        """Enable default OSINT feeds in MISP."""
        osint_feeds = [
            {"name": "CIRCL OSINT", "id": 1},
            {"name": "Botvrij.eu", "id": 2},
            {"name": "abuse.ch URLhaus", "id": 5},
            {"name": "abuse.ch Feodo Tracker", "id": 6},
        ]
        for feed in osint_feeds:
            try:
                self.misp.enable_feed(feed["id"])
                self.misp.fetch_feed(feed["id"])
                print(f"[+] Enabled feed: {feed['name']}")
            except Exception as e:
                print(f"[-] Failed: {feed['name']}: {e}")

    def configure_opencti_connectors(self):
        """List and verify OpenCTI connector status."""
        connectors = self.opencti.connector.list()
        for conn in connectors:
            print(

Step 3: Build Enrichment Pipeline with Cortex

import requests

class CortexEnrichment:
    """Integrate Cortex analyzers for automated enrichment."""

    def __init__(self, cortex_url, cortex_key):
        self.url = cortex_url
        self.headers = {"Authorization": f"Bearer {cortex_key}"}

    def list_analyzers(self):
        """List available Cortex analyzers."""
        resp = requests.get(
            f"{self.url}/api/analyzer",
            headers=self.headers,
            timeout=30,
        )
        if resp.status_code == 200:
            analyzers = resp.json()
            for a in analyzers:
                print(f"  {a['name']}: {a.get('description', '')[:60]}")
            return analyzers
        return []

    def analyze_observable(self, observable_type, observable_value, analyzer_id):
        """Submit an observable for analysis."""
        job = {
            "data": observable_value,
            "dataType": observable_type,
            "tlp": 2,
            "message": "TIP automated enrichment",
        }
        resp = requests.post(
            f"{self.url}/api/analyzer/{analyzer_id}/run",
            json=job,
            headers=self.headers,
            timeout=30,
        )
        if resp.status_co

Step 4: Implement Analyst Dashboard Metrics

class TIPMetrics:
    """Collect platform metrics for analyst dashboards."""

    def __init__(self, misp, opencti):
        self.misp = misp
        self.opencti = opencti

    def get_platform_stats(self):
        """Collect statistics across all platform components."""
        stats = {}

        # MISP stats
        misp_stats = self.misp.get_server_statistics()
        stats["misp"] = {
            "total_events": misp_stats.get("event_count", 0),
            "total_attributes": misp_stats.get("attribute_count", 0),
            "active_feeds": len([
                f for f in self.misp.feeds()
                if f.get("Feed", {}).get("enabled")
            ]),
        }

        # OpenCTI stats via GraphQL
        stats["opencti"] = {
            "total_indicators": self.opencti.indicator.list(
                first=0, withPagination=True
            ).get("pagination", {}).get("globalCount", 0),
            "total_reports": self.opencti.report.list(
                first=0, withPagination=True
            ).get("pagination", {}).get("globalCount", 0),
        }

        return stats

Validation Criteria

• All platform components (MISP, OpenCTI, TheHive, Cortex) deployed and accessible
• MISP-OpenCTI bidirectional sync operational
• At least 3 OSINT feeds ingesting data
• Cortex analyzers configured and returning enrichment results
• Platform metrics dashboard showing real-time statistics
• STIX/TAXII export functional for intelligence sharing

References

• OpenCTI Documentation
• MISP Project
• TheHive Project
• Cortex Documentation
• MISP-OpenCTI Integration