external/anthropic-cybersecurity-skills/skills/implementing-ot-network-traffic-analysis-with-nozomi/SKILL.md
Deploy Nozomi Networks Guardian sensors for passive OT network traffic analysis, providing asset visibility, behavioral anomaly detection, protocol-aware monitoring, and vulnerability assessment across industrial control systems without disrupting operations. Use when deploying OT/ICS network monitoring, configuring Guardian sensors, or building real-time threat detection for SCADA and industrial environments.
npx skillsauth add seikaikyo/dash-skills implementing-ot-network-traffic-analysis-with-nozomiInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Do not use for active vulnerability scanning of OT devices (see performing-ot-vulnerability-scanning-safely), for environments standardized on Dragos (see implementing-dragos-platform-for-ot-monitoring), or for IT-only network monitoring.
#!/usr/bin/env python3
"""Nozomi Guardian Deployment Manager and Alert Analyzer.
Manages Nozomi Guardian sensor deployment validation, asset inventory
extraction, and threat alert analysis for OT environments.
"""
import json
import sys
from collections import defaultdict
from datetime import datetime
from typing import Dict, List, Optional
try:
import requests
except ImportError:
print("Install requests: pip install requests")
sys.exit(1)
class NozomiGuardianManager:
"""Manages Nozomi Networks Guardian for OT monitoring."""
def __init__(self, guardian_url: str, api_token: str, verify_ssl: bool = False):
self.guardian_url = guardian_url.rstrip("/")
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {api_token}",
"Content-Type": "application/json",
})
self.session.verify = verify_ssl
def get_nodes(self, node_type: Optional[str] = None) -> List[Dict]:
"""Retrieve discovered network nodes (assets)."""
params = {}
if node_type:
params["type"] = node_type
resp = self.session.get(f"{self.guardian_url}/api/v1/nodes", params=params)
resp.raise_for_status()
return resp.json().get("result", [])
def get_alerts(self, severity: str = "high", limit: int = 100) -> List[Dict]:
"""Retrieve security alerts."""
params = {"severity": severity, "limit": limit, "status": "open"}
resp = self.session.get(f"{self.guardian_url}/api/v1/alerts", params=params)
resp.raise_for_status()
return resp.json().get("result", [])
def get_links(self) -> List[Dict]:
"""Retrieve communication links between nodes."""
resp = self.session.get(f"{self.guardian_url}/api/v1/links")
resp.raise_for_status()
return resp.json().get("result", [])
def get_vulnerabilities(self) -> List[Dict]:
"""Retrieve detected vulnerabilities."""
resp = self.session.get(f"{self.guardian_url}/api/v1/vulnerabilities")
resp.raise_for_status()
return resp.json().get("result", [])
def validate_deployment(self):
"""Validate Guardian sensor deployment and coverage."""
print(f"\n{'='*65}")
print("NOZOMI GUARDIAN DEPLOYMENT VALIDATION")
print(f"{'='*65}")
print(f"Guardian URL: {self.guardian_url}")
print(f"Validation Time: {datetime.now().isoformat()}")
# Check system status
try:
resp = self.session.get(f"{self.guardian_url}/api/v1/system/status")
if resp.status_code == 200:
status = resp.json()
print(f"\n--- SYSTEM STATUS ---")
print(f" Version: {status.get('version', 'N/A')}")
print(f" Uptime: {status.get('uptime', 'N/A')}")
print(f" Packets Processed: {status.get('packets_processed', 'N/A')}")
print(f" Threat Intelligence: {status.get('threat_intelligence_version', 'N/A')}")
except requests.RequestException as e:
print(f" [!] System status unavailable: {e}")
# Asset discovery summary
nodes = self.get_nodes()
print(f"\n--- ASSET DISCOVERY ---")
print(f" Total Nodes Discovered: {len(nodes)}")
type_counts = defaultdict(int)
vendor_counts = defaultdict(int)
protocol_set = set()
for node in nodes:
type_counts[node.get("type", "unknown")] += 1
vendor_counts[node.get("vendor", "Unknown")] += 1
for proto in node.get("protocols", []):
protocol_set.add(proto)
print(f"\n By Type:")
for ntype, count in sorted(type_counts.items(), key=lambda x: -x[1]):
print(f" {ntype}: {count}")
print(f"\n By Vendor:")
for vendor, count in sorted(vendor_counts.items(), key=lambda x: -x[1])[:10]:
print(f" {vendor}: {count}")
print(f"\n Protocols Observed: {', '.join(sorted(protocol_set))}")
# Alert summary
alerts = self.get_alerts(severity="high")
print(f"\n--- ALERT SUMMARY ---")
print(f" High/Critical Alerts: {len(alerts)}")
alert_types = defaultdict(int)
for alert in alerts:
alert_types[alert.get("type_id", "unknown")] += 1
for atype, count in sorted(alert_types.items(), key=lambda x: -x[1])[:10]:
print(f" {atype}: {count}")
# Vulnerability summary
vulns = self.get_vulnerabilities()
print(f"\n--- VULNERABILITY SUMMARY ---")
print(f" Total Vulnerabilities: {len(vulns)}")
sev_counts = defaultdict(int)
for vuln in vulns:
sev_counts[vuln.get("severity", "unknown")] += 1
for sev in ["critical", "high", "medium", "low"]:
if sev in sev_counts:
print(f" {sev.capitalize()}: {sev_counts[sev]}")
def analyze_communication_patterns(self):
"""Analyze OT communication patterns for anomalies."""
links = self.get_links()
nodes = {n.get("id"): n for n in self.get_nodes()}
print(f"\n--- COMMUNICATION ANALYSIS ---")
print(f" Total Communication Links: {len(links)}")
# Identify cross-zone communications
cross_zone = []
for link in links:
src_node = nodes.get(link.get("source_id"), {})
dst_node = nodes.get(link.get("destination_id"), {})
src_zone = src_node.get("zone", "unknown")
dst_zone = dst_node.get("zone", "unknown")
if src_zone != dst_zone and src_zone != "unknown" and dst_zone != "unknown":
cross_zone.append({
"source": src_node.get("label", "Unknown"),
"source_zone": src_zone,
"destination": dst_node.get("label", "Unknown"),
"dest_zone": dst_zone,
"protocols": link.get("protocols", []),
})
if cross_zone:
print(f"\n Cross-Zone Communications: {len(cross_zone)}")
for comm in cross_zone[:10]:
print(f" {comm['source']} ({comm['source_zone']}) -> "
f"{comm['destination']} ({comm['dest_zone']}) "
f"via {', '.join(comm['protocols'])}")
if __name__ == "__main__":
manager = NozomiGuardianManager(
guardian_url="https://nozomi-guardian.plant.local",
api_token="your-api-token",
)
manager.validate_deployment()
manager.analyze_communication_patterns()
| Term | Definition | |------|------------| | Guardian | Nozomi Networks passive sensor that monitors OT network traffic via SPAN/TAP without generating additional traffic | | Vantage | Nozomi cloud-based central management platform for aggregating data across multiple Guardian sensors | | Behavioral Anomaly Detection (BAD) | Nozomi's AI-driven approach to detecting deviations from learned normal OT network behavior | | Smart Polling | Nozomi's active query feature using native protocols to safely extract additional device details | | Asset Intelligence | Nozomi's automatic identification and classification of OT/IoT assets from network traffic | | Threat Intelligence Feed | Nozomi Labs-maintained feed of OT-specific threat indicators, updated based on global honeypot data |
NOZOMI GUARDIAN OT MONITORING REPORT
=======================================
Site: [site name]
Date: YYYY-MM-DD
ASSET VISIBILITY:
Total Assets: [count]
PLCs: [count] | HMIs: [count] | Switches: [count]
Protocols: [list]
Vendors: [top 5]
THREAT DETECTION:
Critical Alerts: [count]
High Alerts: [count]
Top Alert Categories: [list]
VULNERABILITIES:
Critical: [count]
High: [count]
NETWORK ANALYSIS:
Communication Links: [count]
Cross-Zone Flows: [count]
tools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.