implementing-dragos-platform-for-ot-monitoring▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Deploy and configure the Dragos Platform for OT network monitoring, leveraging its 600+ industrial protocol parsers, intelligence-driven threat detection analytics, and asset visibility capabilities to protect ICS environments against threat groups like VOLTZITE, GRAPHITE, and BAUXITE.
| name | implementing-dragos-platform-for-ot-monitoring |
| description | 'Deploy and configure the Dragos Platform for OT network monitoring, leveraging its 600+ industrial protocol parsers, intelligence-driven threat detection analytics, and asset visibility capabilities to protect ICS environments against threat groups like VOLTZITE, GRAPHITE, and BAUXITE. ' |
| domain | cybersecurity |
| subdomain | ot-ics-security |
| tags | - ot-security - ics - dragos - threat-detection - ot-monitoring - scada - threat-intelligence - ndr |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_ai_rmf | - MEASURE-2.7 - MAP-5.1 - MANAGE-2.4 |
| atlas_techniques | - AML.T0070 - AML.T0066 - AML.T0082 |
| nist_csf | - PR.IR-01 - DE.CM-01 - ID.AM-05 - GV.OC-02 |
Implementing Dragos Platform for OT Monitoring
When to Use
- When deploying an OT-specific network detection and response (NDR) solution for industrial environments
- When needing threat intelligence-driven detection against known ICS threat groups (VOLTZITE, CHERNOVITE, KAMACITE)
- When building an OT SOC capability with purpose-built industrial security tooling
- When requiring asset discovery and vulnerability management alongside threat detection in a single platform
- When integrating OT security monitoring with an enterprise SIEM (Splunk, Sentinel, QRadar)
Do not use for IT-only network monitoring without ICS components, for endpoint detection and response (EDR) on OT workstations, or for environments standardized on Claroty or Nozomi (see respective skills).
Prerequisites
- Dragos Platform license and deployment package
- Network TAP or SPAN port at OT network boundaries (one sensor per monitored segment)
- Dragos sensor hardware (physical appliance) or virtual appliance meeting minimum specifications
- Firewall rules allowing sensor-to-Dragos-SiteStore communication (encrypted, outbound only from OT)
- Dragos Knowledge Pack subscription for threat intelligence updates
Workflow
Step 1: Deploy Dragos Sensors and Configure Monitoring
#!/usr/bin/env python3
"""Dragos Platform Deployment Validator and Integration Tool.
Validates Dragos sensor deployment, checks connectivity, and
configures integration with enterprise SIEM for OT alert forwarding.
"""
import json
import sys
import csv
from datetime import datetime
from typing import Optional, List, Dict
try:
import requests
except ImportError:
print("Install requests: pip install requests")
sys.exit(1)
class DragosPlatformManager:
"""Interface with Dragos Platform API for OT monitoring management."""
def __init__(self, base_url: str, api_key: str, api_secret: str, verify_ssl: bool = True):
self.base_url = base_url.rstrip("/")
self.session = requests.Session()
self.session.headers.update({
"API-Key": api_key,
"API-Secret": api_secret,
"Content-Type": "application/json",
})
self.session.verify = verify_ssl
def get_sensors(self) -> List[Dict]:
"""Retrieve all deployed Dragos sensors and their status."""
resp = self.session.get(f"{self.base_url}/api/v1/sensors")
resp.raise_for_status()
return resp.json().get("sensors", [])
def get_assets(self, asset_type: Optional[str] = None) -> List[Dict]:
"""Retrieve OT assets discovered by Dragos."""
params = {}
if asset_type:
params["type"] = asset_type
resp = self.session.get(f"{self.base_url}/api/v1/assets", params=params)
resp.raise_for_status()
return resp.json().get("assets", [])
def get_notifications(self, severity: str = "high", limit: int = 50) -> List[Dict]:
"""Retrieve threat detection notifications."""
params = {"min_severity": severity, "limit": limit}
resp = self.session.get(f"{self.base_url}/api/v1/notifications", params=params)
resp.raise_for_status()
return resp.json().get("notifications", [])
def get_vulnerabilities(self, severity: str = "critical") -> List[Dict]:
"""Retrieve OT vulnerabilities with Dragos-specific context."""
params = {"min_severity": severity}
resp = self.session.get(f"{self.base_url}/api/v1/vulnerabilities", params=params)
resp.raise_for_status()
return resp.json().get("vulnerabilities", [])
def get_threat_groups(self) -> List[Dict]:
"""Retrieve tracked ICS threat group activity relevant to the environment."""
resp = self.session.get(f"{self.base_url}/api/v1/threat-groups")
resp.raise_for_status()
return resp.json().get("threat_groups", [])
def validate_deployment(self):
"""Validate sensor deployment health and coverage."""
sensors = self.get_sensors()
assets = self.get_assets()
print(f"\n{'='*65}")
print("DRAGOS PLATFORM DEPLOYMENT VALIDATION")
print(f"{'='*65}")
print(f"Validation Time: {datetime.now().isoformat()}")
print(f"\n--- SENSOR STATUS ---")
healthy_sensors = 0
for sensor in sensors:
status = sensor.get("status", "unknown")
icon = "[OK]" if status == "connected" else "[!!]"
print(f" {icon} {sensor.get('name', 'Unknown')} | Status: {status}")
print(f" IP: {sensor.get('ip_address')} | Segment: {sensor.get('monitored_segment')}")
print(f" Last Seen: {sensor.get('last_seen')} | Packets/sec: {sensor.get('pps', 0)}")
print(f" Knowledge Pack: {sensor.get('knowledge_pack_version', 'N/A')}")
if status == "connected":
healthy_sensors += 1
print(f"\n Sensor Health: {healthy_sensors}/{len(sensors)} operational")
print(f"\n--- ASSET VISIBILITY ---")
print(f" Total Assets Discovered: {len(assets)}")
asset_types = {}
for asset in assets:
atype = asset.get("type", "Unknown")
asset_types[atype] = asset_types.get(atype, 0) + 1
for atype, count in sorted(asset_types.items(), key=lambda x: -x[1]):
print(f" {atype}: {count}")
protocols = set()
for asset in assets:
protocols.update(asset.get("protocols", []))
print(f" Protocols Observed: {', '.join(sorted(protocols))}")
print(f"\n--- THREAT INTELLIGENCE ---")
groups = self.get_threat_groups()
print(f" Relevant Threat Groups: {len(groups)}")
for group in groups:
print(f" - {group.get('name')}: {group.get('description', '')[:80]}")
print(f" Targets: {', '.join(group.get('target_sectors', []))}")
print(f" Activity Level: {group.get('activity_level', 'Unknown')}")
def generate_siem_integration_config(self, siem_type: str = "splunk"):
"""Generate SIEM integration configuration for Dragos alerts."""
configs = {
"splunk": {
"syslog_format": "CEF",
"syslog_port": 514,
"severity_mapping": {
"critical": 10,
"high": 7,
"medium": 5,
"low": 3,
"info": 1,
},
"index": "ot_security",
"sourcetype": "dragos:notification",
"fields": [
"notification_id", "severity", "category", "source_ip",
"destination_ip", "asset_name", "protocol", "description",
"mitre_ics_technique", "threat_group",
],
},
"sentinel": {
"connector_type": "Syslog-CEF",
"workspace_id": "<workspace-id>",
"log_analytics_table": "DragosOTAlerts_CL",
"severity_mapping": {
"critical": "High",
"high": "High",
"medium": "Medium",
"low": "Low",
"info": "Informational",
},
},
}
config = configs.get(siem_type, configs["splunk"])
print(f"\n--- {siem_type.upper()} INTEGRATION CONFIG ---")
print(json.dumps(config, indent=2))
return config
if __name__ == "__main__":
manager = DragosPlatformManager(
base_url="https://dragos-sitestore.plant.local",
api_key="your-api-key",
api_secret="your-api-secret",
verify_ssl=True,
)
manager.validate_deployment()
manager.generate_siem_integration_config("splunk")
print(f"\n--- RECENT HIGH-SEVERITY NOTIFICATIONS ---")
notifications = manager.get_notifications(severity="high", limit=10)
for n in notifications:
print(f" [{n.get('severity', '').upper()}] {n.get('title', 'No title')}")
print(f" Category: {n.get('category')} | Time: {n.get('timestamp')}")
print(f" Assets: {', '.join(n.get('affected_assets', []))}")
print(f" MITRE ICS: {n.get('mitre_technique', 'N/A')}")
Step 2: Configure Detection Analytics and Knowledge Packs
# Dragos Platform Detection Configuration
# Tuned for manufacturing/energy environment
detection_configuration:
knowledge_pack:
auto_update: true
update_schedule: "weekly"
include_threat_groups:
- "VOLTZITE" # Targets energy sector, exfiltrates OT diagrams
- "GRAPHITE" # New 2025 threat group targeting ICS
- "BAUXITE" # New 2025 threat group targeting ICS
- "CHERNOVITE" # Developed PIPEDREAM/INCONTROLLER framework
- "ELECTRUM" # Linked to Industroyer/CrashOverride
- "KAMACITE" # Targets energy sector initial access
detection_categories:
network_baseline:
enabled: true
learning_period_days: 30
alert_on:
- "new_communication_pair"
- "new_protocol_detected"
- "new_device_on_network"
- "protocol_anomaly"
threat_detection:
enabled: true
alert_on:
- "known_malware_ioc"
- "threat_group_ttp"
- "lateral_movement"
- "command_and_control"
- "data_exfiltration"
vulnerability_correlation:
enabled: true
alert_on:
- "active_exploitation_attempt"
- "vulnerability_with_public_exploit"
protocol_monitoring:
modbus:
monitor_writes: true
baseline_function_codes: true
baseline_register_ranges: true
dnp3:
monitor_control_commands: true
detect_firmware_updates: true
s7comm:
detect_cpu_stop: true
detect_program_download: true
opc_ua:
monitor_method_calls: true
detect_browsing: true
ethernet_ip:
monitor_cip_services: true
detect_firmware_flash: true
alert_routing:
critical:
notify: ["ot_soc_team", "plant_manager"]
siem_forward: true
auto_ticket: true
high:
notify: ["ot_soc_team"]
siem_forward: true
auto_ticket: true
medium:
siem_forward: true
low:
siem_forward: true
Key Concepts
| Term | Definition |
|---|---|
| Dragos Platform | Purpose-built OT cybersecurity platform with asset visibility, threat detection, and vulnerability management for ICS environments |
| Knowledge Pack | Dragos threat intelligence update containing detection analytics for new threats, malware, and vulnerability exploits specific to ICS |
| SiteStore | Dragos central management server aggregating data from all deployed sensors across a site |
| VOLTZITE | Dragos-tracked threat group targeting energy sector OT environments, exfiltrating GIS data and ICS network diagrams |
| PIPEDREAM/INCONTROLLER | Modular ICS attack framework developed by CHERNOVITE, targeting Schneider/OMRON PLCs and OPC UA servers |
| Neighborhood Keeper | Dragos community defense program sharing anonymized threat data across participating OT environments |
Common Scenarios
Scenario: Detecting VOLTZITE Reconnaissance in Energy Utility
Context: A Dragos sensor deployed at an electric utility detects unusual OPC UA browsing activity and exfiltration of device configuration data from an engineering workstation.
Approach:
- Review the Dragos notification for MITRE ATT&CK ICS technique mapping
- Identify the source host performing OPC UA browsing (check if it is an authorized engineering workstation)
- Check Dragos threat intelligence correlation for VOLTZITE TTPs
- Examine the scope of data accessed (GIS data, network diagrams, ICS configuration files)
- Isolate the compromised workstation from the OT network
- Check for lateral movement indicators to other OT systems
- Engage Dragos Professional Services if threat group attribution is confirmed
- Report to CISA as a critical infrastructure cyber incident
Pitfalls: Do not ignore OPC UA browsing alerts as false positives -- VOLTZITE specifically uses this technique for pre-positioning. Ensure Dragos Knowledge Packs are current to detect the latest VOLTZITE indicators. Do not reimage the compromised workstation before collecting forensic evidence.
Output Format
DRAGOS OT MONITORING DEPLOYMENT REPORT
==========================================
Site: [Site Name]
Date: YYYY-MM-DD
SENSOR DEPLOYMENT:
Total Sensors: [count]
Operational: [count]
Coverage: [percentage of OT segments monitored]
ASSET VISIBILITY:
Total OT Assets: [count]
PLCs: [count] | HMIs: [count] | Network Devices: [count]
Protocols: [list]
THREAT DETECTION:
Active Threat Groups Relevant: [count]
Detection Analytics Loaded: [count]
Alerts (Last 30 Days): [count by severity]
SIEM INTEGRATION:
Status: [Connected/Disconnected]
Events Forwarded (Last 24h): [count]
How to use implementing-dragos-platform-for-ot-monitoring on Cursor
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Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add implementing-dragos-platform-for-ot-monitoring
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches implementing-dragos-platform-for-ot-monitoring from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate implementing-dragos-platform-for-ot-monitoring. Access the skill through slash commands (e.g., /implementing-dragos-platform-for-ot-monitoring) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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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
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate 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
Discussion
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Ratings
4.8★★★★★51 reviews- ★★★★★Aisha Taylor· Dec 16, 2024
implementing-dragos-platform-for-ot-monitoring fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Aisha Brown· Dec 16, 2024
implementing-dragos-platform-for-ot-monitoring is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★James Bansal· Dec 4, 2024
implementing-dragos-platform-for-ot-monitoring reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Valentina Haddad· Nov 23, 2024
We added implementing-dragos-platform-for-ot-monitoring from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★William Martinez· Nov 7, 2024
Registry listing for implementing-dragos-platform-for-ot-monitoring matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Hassan Patel· Nov 7, 2024
Useful defaults in implementing-dragos-platform-for-ot-monitoring — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Soo Bansal· Oct 26, 2024
implementing-dragos-platform-for-ot-monitoring reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Ava Thomas· Oct 26, 2024
I recommend implementing-dragos-platform-for-ot-monitoring for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★William Choi· Oct 14, 2024
implementing-dragos-platform-for-ot-monitoring fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ava Anderson· Sep 17, 2024
Solid pick for teams standardizing on skills: implementing-dragos-platform-for-ot-monitoring is focused, and the summary matches what you get after install.
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