building-ioc-enrichment-pipeline-with-opencti
OpenCTI is an open-source platform for managing cyber threat intelligence knowledge, built on STIX 2.1 as its native data model. This skill covers building an automated IOC enrichment pipeline using O
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How to use building-ioc-enrichment-pipeline-with-opencti on Cursor
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building-ioc-enrichment-pipeline-with-opencti
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Documentation
| name | building-ioc-enrichment-pipeline-with-opencti |
| description | OpenCTI is an open-source platform for managing cyber threat intelligence knowledge, built on STIX 2.1 as its native data model. This skill covers building an automated IOC enrichment pipeline using O |
| domain | cybersecurity |
| subdomain | threat-intelligence |
| tags | - threat-intelligence - cti - ioc - mitre-attack - stix - opencti - enrichment - virustotal |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - ID.RA-01 - ID.RA-05 - DE.CM-01 - DE.AE-02 |
Building IOC Enrichment Pipeline with OpenCTI
Overview
OpenCTI is an open-source platform for managing cyber threat intelligence knowledge, built on STIX 2.1 as its native data model. This skill covers building an automated IOC enrichment pipeline using OpenCTI's connector ecosystem to enrich indicators with context from VirusTotal, Shodan, AbuseIPDB, GreyNoise, and other sources. The pipeline automatically enriches newly ingested indicators, correlates them with known threat actors and campaigns, and scores them for analyst prioritization.
When to Use
- When deploying or configuring building ioc enrichment pipeline with opencti 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 OpenCTI deployment
- Python 3.9+ with
pyctilibrary - API keys for enrichment services: VirusTotal, Shodan, AbuseIPDB, GreyNoise
- Understanding of STIX 2.1 data model and relationships
- ElasticSearch or OpenSearch for OpenCTI backend
- RabbitMQ or Redis for connector messaging
Key Concepts
OpenCTI Architecture
OpenCTI uses a GraphQL API frontend backed by ElasticSearch for storage and Redis/RabbitMQ for connector communication. Data is natively stored as STIX 2.1 objects with relationships. Connectors are categorized as: External Import (feed ingestion), Internal Import (file parsing), Internal Enrichment (context addition), and Stream (real-time export).
Enrichment Connector Model
Internal enrichment connectors are triggered automatically when new observables are created or manually by analysts. Each connector receives STIX objects, queries external services, and returns STIX 2.1 bundles that augment the original observable with additional context, labels, and relationships.
Confidence Scoring
OpenCTI uses a 0-100 confidence scale for indicators. Enrichment connectors can update confidence scores based on external validation: VirusTotal detection ratios, Shodan exposure data, AbuseIPDB report counts, and GreyNoise classification results.
Workflow
Step 1: Deploy OpenCTI with Docker Compose
# docker-compose.yml (key services)
version: '3'
services:
opencti:
image: opencti/platform:6.4.4
environment:
- APP__PORT=8080
- [email protected]
- APP__ADMIN__PASSWORD=ChangeMeNow
- APP__ADMIN__TOKEN=your-admin-token-uuid
- ELASTICSEARCH__URL=http://elasticsearch:9200
- MINIO__ENDPOINT=minio
- RABBITMQ__HOSTNAME=rabbitmq
ports:
- "8080:8080"
depends_on:
- elasticsearch
- minio
- rabbitmq
- redis
connector-virustotal:
image: opencti/connector-virustotal:6.4.4
environment:
- OPENCTI_URL=http://opencti:8080
- OPENCTI_TOKEN=your-admin-token-uuid
- CONNECTOR_ID=connector-virustotal-id
- CONNECTOR_NAME=VirusTotal
- CONNECTOR_SCOPE=StixFile,Artifact,IPv4-Addr,Domain-Name,Url
- CONNECTOR_AUTO=true
- VIRUSTOTAL_TOKEN=your-vt-api-key
- VIRUSTOTAL_MAX_TLP=TLP:AMBER
connector-shodan:
image: opencti/connector-shodan:6.4.4
environment:
- OPENCTI_URL=http://opencti:8080
- OPENCTI_TOKEN=your-admin-token-uuid
- CONNECTOR_ID=connector-shodan-id
- CONNECTOR_NAME=Shodan
- CONNECTOR_SCOPE=IPv4-Addr
- CONNECTOR_AUTO=true
- SHODAN_TOKEN=your-shodan-api-key
- SHODAN_MAX_TLP=TLP:AMBER
connector-abuseipdb:
image: opencti/connector-abuseipdb:6.4.4
environment:
- OPENCTI_URL=http://opencti:8080
- OPENCTI_TOKEN=your-admin-token-uuid
- CONNECTOR_ID=connector-abuseipdb-id
- CONNECTOR_NAME=AbuseIPDB
- CONNECTOR_SCOPE=IPv4-Addr
- CONNECTOR_AUTO=true
- ABUSEIPDB_API_KEY=your-abuseipdb-key
Step 2: Build Custom Enrichment Connector
import os
from pycti import OpenCTIConnectorHelper, get_config_variable
from stix2 import (
Bundle, Indicator, Note, Relationship,
IPv4Address, DomainName
)
import requests
class CustomEnrichmentConnector:
def __init__(self):
config = {
"opencti": {
"url": os.environ.get("OPENCTI_URL"),
"token": os.environ.get("OPENCTI_TOKEN"),
},
"connector": {
"id": os.environ.get("CONNECTOR_ID"),
"name": "CustomEnrichment",
"scope": "IPv4-Addr,Domain-Name,Url",
"auto": True,
"type": "INTERNAL_ENRICHMENT",
},
}
self.helper = OpenCTIConnectorHelper(config)
self.helper.listen(self._process_message)
def _process_message(self, data):
entity_id = data["entity_id"]
stix_object = self.helper.api.stix_cyber_observable.read(id=entity_id)
if not stix_object:
return "Observable not found"
observable_type = stix_object["entity_type"]
observable_value = stix_object.get("value", "")
enrichment_results = []
if observable_type == "IPv4-Addr":
enrichment_results = self._enrich_ip(observable_value, entity_id)
elif observable_type == "Domain-Name":
enrichment_results = self._enrich_domain(observable_value, entity_id)
if enrichment_results:
bundle = Bundle(objects=enrichment_results, allow_custom=True)
self.helper.send_stix2_bundle(bundle.serialize())
return "Enrichment completed"
def _enrich_ip(self, ip_address, entity_id):
"""Enrich IP address with GreyNoise, AbuseIPDB context."""
objects = []
# GreyNoise Community API
try:
gn_response = requests.get(
f"https://api.greynoise.io/v3/community/{ip_address}",
headers={"key": os.environ.get("GREYNOISE_API_KEY")},
timeout=30,
)
if gn_response.status_code == 200:
gn_data = gn_response.json()
classification = gn_data.get("classification", "unknown")
noise = gn_data.get("noise", False)
riot = gn_data.get("riot", False)
note_content = (
f"## GreyNoise Enrichment\n"
f"- Classification: {classification}\n"
f"- Internet Noise: {noise}\n"
f"- RIOT (Benign Service): {riot}\n"
f"- Name: {gn_data.get('name', 'N/A')}\n"
f"- Last Seen: {gn_data.get('last_seen', 'N/A')}"
)
note = Note(
content=note_content,
object_refs=[entity_id],
abstract=f"GreyNoise: {classification}",
allow_custom=True,
)
objects.append(note)
# Add labels based on classification
if classification == "malicious":
self.helper.api.stix_cyber_observable.add_label(
id=entity_id, label_name="greynoise:malicious"
)
elif riot:
self.helper.api.stix_cyber_observable.add_label(
id=entity_id, label_name="greynoise:benign-service"
)
except Exception as e:
self.helper.log_error(f"GreyNoise enrichment failed: {e}")
return objects
def _enrich_domain(self, domain, entity_id):
"""Enrich domain with WHOIS and DNS context."""
objects = []
try:
# Use SecurityTrails API for domain enrichment
st_response = requests.get(
f"https://api.securitytrails.com/v1/domain/{domain}",
headers={"APIKEY": os.environ.get("SECURITYTRAILS_API_KEY")},
timeout=30,
)
if st_response.status_code == 200:
st_data = st_response.json()
current_dns = st_data.get("current_dns", {})
a_records = [
r.get("ip") for r in current_dns.get("a", {}).get("values", [])
]
note_content = (
f"## SecurityTrails Enrichment\n"
f"- A Records: {', '.join(a_records)}\n"
f"- Alexa Rank: {st_data.get('alexa_rank', 'N/A')}\n"
f"- Hostname: {st_data.get('hostname', 'N/A')}"
)
note = Note(
content=note_content,
object_refs=[entity_id],
abstract=f"SecurityTrails: {domain}",
allow_custom=True,
)
objects.append(note)
except Exception as e:
self.helper.log_error(f"SecurityTrails enrichment failed: {e}")
return objects
if __name__ == "__main__":
connector = CustomEnrichmentConnector()
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Use Cases
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Steps
- 1Install skill using provided installation command
- 2Test with simple use case relevant to your work
- 3Evaluate output quality and relevance
- 4Iterate on prompts to improve results
- 5Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
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Reviews
- AAnika Kapoor★★★★★Dec 28, 2024
building-ioc-enrichment-pipeline-with-opencti has been reliable in day-to-day use. Documentation quality is above average for community skills.
- CChaitanya Patil★★★★★Dec 20, 2024
Keeps context tight: building-ioc-enrichment-pipeline-with-opencti is the kind of skill you can hand to a new teammate without a long onboarding doc.
- DDiego Thompson★★★★★Dec 16, 2024
I recommend building-ioc-enrichment-pipeline-with-opencti for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- YYuki Khan★★★★★Dec 12, 2024
Solid pick for teams standardizing on skills: building-ioc-enrichment-pipeline-with-opencti is focused, and the summary matches what you get after install.
- SSofia Anderson★★★★★Nov 19, 2024
Useful defaults in building-ioc-enrichment-pipeline-with-opencti — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- CCarlos Flores★★★★★Nov 15, 2024
We added building-ioc-enrichment-pipeline-with-opencti from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- PPiyush G★★★★★Nov 11, 2024
Registry listing for building-ioc-enrichment-pipeline-with-opencti matched our evaluation — installs cleanly and behaves as described in the markdown.
- YYuki Nasser★★★★★Nov 7, 2024
Solid pick for teams standardizing on skills: building-ioc-enrichment-pipeline-with-opencti is focused, and the summary matches what you get after install.
- DDiego Jain★★★★★Nov 3, 2024
I recommend building-ioc-enrichment-pipeline-with-opencti for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- CCamila Tandon★★★★★Oct 26, 2024
building-ioc-enrichment-pipeline-with-opencti has been reliable in day-to-day use. Documentation quality is above average for community skills.
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