skills/mukul975/anthropic-cybersecurity-skills/implementing-threat-intelligence-lifecycle-management
implementing-threat-intelligence-lifecycle-management
SKILL.md
Implementing Threat Intelligence Lifecycle Management
Overview
The threat intelligence lifecycle is a structured, iterative process for transforming raw data into actionable intelligence. Based on the intelligence cycle used by military and government agencies, it comprises six phases: Direction (requirements gathering), Collection (data acquisition), Processing (normalization and deduplication), Analysis (contextualization and assessment), Dissemination (distribution to stakeholders), and Feedback (evaluation and refinement). This skill covers building each phase with tooling, metrics, and integration points for a mature CTI program.
Prerequisites
- Python 3.9+ with
pymisp,stix2,requests,pandaslibraries - MISP or OpenCTI as threat intelligence platform
- Ticketing system (Jira, ServiceNow) for requirements management
- SIEM integration (Splunk, Elastic) for indicator operationalization
- Understanding of intelligence analysis techniques (ACH, Diamond Model)
Key Concepts
Intelligence Requirements (IR)
Priority Intelligence Requirements (PIRs) define what the organization needs to know. Examples: Which threat actors target our sector? What vulnerabilities are being actively exploited? Are our brand or credentials being traded on dark web? PIRs drive collection planning and ensure intelligence production is relevant.
Collection Management Framework
A collection management framework maps intelligence requirements to collection sources, tracks collection gaps, and ensures coverage across the threat landscape. Sources include OSINT, commercial feeds, ISAC sharing, internal telemetry, and human intelligence from industry contacts.
Intelligence Levels
Strategic intelligence informs executive decision-making (threat landscape, risk trends, geopolitical context). Operational intelligence supports security operations (campaign tracking, actor TTPs, attack timing). Tactical intelligence enables immediate defense (IOCs, detection rules, blocklists).
Practical Steps
Step 1: Define Intelligence Requirements
import json
from datetime import datetime
from enum import Enum
class Priority(Enum):
CRITICAL = 1
HIGH = 2
MEDIUM = 3
LOW = 4
class IntelligenceRequirement:
def __init__(self, requirement_id, question, priority, stakeholder,
intelligence_level, collection_sources=None):
self.id = requirement_id
self.question = question
self.priority = priority
self.stakeholder = stakeholder
self.level = intelligence_level
self.sources = collection_sources or []
self.created = datetime.now().isoformat()
self.status = "active"
self.last_answered = None
def to_dict(self):
return {
"id": self.id,
"question": self.question,
"priority": self.priority.name,
"stakeholder": self.stakeholder,
"intelligence_level": self.level,
"collection_sources": self.sources,
"created": self.created,
"status": self.status,
"last_answered": self.last_answered,
}
class RequirementsManager:
def __init__(self):
self.requirements = []
def add_requirement(self, requirement):
self.requirements.append(requirement)
print(f"[+] Added IR-{requirement.id}: {requirement.question[:60]}...")
def get_active_requirements(self, priority=None, level=None):
filtered = [r for r in self.requirements if r.status == "active"]
if priority:
filtered = [r for r in filtered if r.priority == priority]
if level:
filtered = [r for r in filtered if r.level == level]
return filtered
def export_requirements(self, output_file="intelligence_requirements.json"):
data = [r.to_dict() for r in self.requirements]
with open(output_file, "w") as f:
json.dump(data, f, indent=2)
print(f"[+] Exported {len(data)} requirements to {output_file}")
# Define organizational PIRs
mgr = RequirementsManager()
mgr.add_requirement(IntelligenceRequirement(
"PIR-001", "Which threat actors are actively targeting our sector?",
Priority.CRITICAL, "CISO", "strategic",
["MITRE ATT&CK", "ISAC feeds", "Vendor reports"],
))
mgr.add_requirement(IntelligenceRequirement(
"PIR-002", "What vulnerabilities are being actively exploited in the wild?",
Priority.CRITICAL, "Vulnerability Management", "operational",
["CISA KEV", "Exploit-DB", "VulnCheck", "Shodan"],
))
mgr.add_requirement(IntelligenceRequirement(
"PIR-003", "Are any organization credentials or data exposed on dark web?",
Priority.HIGH, "SOC Manager", "tactical",
["Dark web monitoring", "Paste site monitoring", "Breach databases"],
))
mgr.add_requirement(IntelligenceRequirement(
"PIR-004", "What are the emerging attack techniques against cloud infrastructure?",
Priority.HIGH, "Cloud Security", "operational",
["ATT&CK Cloud matrix", "Vendor advisories", "ISAC bulletins"],
))
mgr.export_requirements()
Step 2: Build Collection Pipeline
import requests
from datetime import datetime, timedelta
class CollectionPipeline:
def __init__(self, config):
self.config = config
self.collected_data = []
def collect_cisa_kev(self):
"""Collect CISA Known Exploited Vulnerabilities catalog."""
url = "https://www.cisa.gov/sites/default/files/feeds/known_exploited_vulnerabilities.json"
resp = requests.get(url, timeout=30)
if resp.status_code == 200:
data = resp.json()
vulns = data.get("vulnerabilities", [])
self.collected_data.append({
"source": "CISA KEV",
"type": "vulnerability",
"count": len(vulns),
"collected_at": datetime.now().isoformat(),
"data": vulns,
})
print(f"[+] CISA KEV: {len(vulns)} known exploited vulnerabilities")
return vulns
return []
def collect_otx_pulses(self, api_key, days=7):
"""Collect recent OTX pulses."""
headers = {"X-OTX-API-KEY": api_key}
since = (datetime.now() - timedelta(days=days)).isoformat()
url = f"https://otx.alienvault.com/api/v1/pulses/subscribed?modified_since={since}"
resp = requests.get(url, headers=headers, timeout=30)
if resp.status_code == 200:
pulses = resp.json().get("results", [])
self.collected_data.append({
"source": "AlienVault OTX",
"type": "threat_intelligence",
"count": len(pulses),
"collected_at": datetime.now().isoformat(),
})
print(f"[+] OTX: {len(pulses)} pulses in last {days} days")
return pulses
return []
def collect_abuse_ch(self):
"""Collect recent malware samples from MalwareBazaar."""
url = "https://mb-api.abuse.ch/api/v1/"
resp = requests.post(url, data={"query": "get_recent", "selector": "time"}, timeout=30)
if resp.status_code == 200:
data = resp.json().get("data", [])
self.collected_data.append({
"source": "MalwareBazaar",
"type": "malware_samples",
"count": len(data),
"collected_at": datetime.now().isoformat(),
})
print(f"[+] MalwareBazaar: {len(data)} recent samples")
return data
return []
def get_collection_summary(self):
summary = {
"total_sources": len(self.collected_data),
"total_items": sum(d.get("count", 0) for d in self.collected_data),
"sources": [
{"name": d["source"], "type": d["type"], "count": d["count"]}
for d in self.collected_data
],
}
return summary
pipeline = CollectionPipeline({})
pipeline.collect_cisa_kev()
pipeline.collect_abuse_ch()
print(json.dumps(pipeline.get_collection_summary(), indent=2))
Step 3: Process and Normalize Data
class IntelligenceProcessor:
def __init__(self):
self.processed_items = []
self.dedup_hashes = set()
def process_collection(self, raw_data, source_name):
"""Normalize and deduplicate collected intelligence."""
processed = []
duplicates = 0
for item in raw_data:
normalized = self._normalize(item, source_name)
if normalized:
item_hash = self._compute_hash(normalized)
if item_hash not in self.dedup_hashes:
self.dedup_hashes.add(item_hash)
normalized["processed_at"] = datetime.now().isoformat()
processed.append(normalized)
else:
duplicates += 1
self.processed_items.extend(processed)
print(f"[+] Processed {len(processed)} items from {source_name} "
f"({duplicates} duplicates removed)")
return processed
def _normalize(self, item, source):
"""Normalize item to standard format."""
return {
"source": source,
"type": item.get("type", "unknown"),
"value": item.get("value", item.get("indicator", "")),
"confidence": item.get("confidence", 50),
"tlp": item.get("tlp", "green"),
"tags": item.get("tags", []),
"first_seen": item.get("first_seen", item.get("date_added", "")),
"raw": item,
}
def _compute_hash(self, item):
import hashlib
key = f"{item['type']}:{item['value']}:{item['source']}"
return hashlib.sha256(key.encode()).hexdigest()
processor = IntelligenceProcessor()
Step 4: Analyze and Produce Intelligence
class IntelligenceAnalyzer:
def __init__(self, requirements, processed_data):
self.requirements = requirements
self.data = processed_data
def answer_requirement(self, requirement_id):
"""Produce intelligence answering a specific requirement."""
req = next((r for r in self.requirements if r.id == requirement_id), None)
if not req:
return None
# Filter relevant data based on requirement type
relevant = self.data # In practice, filter by requirement topic
analysis = {
"requirement_id": requirement_id,
"question": req.question,
"intelligence_level": req.level,
"data_points_analyzed": len(relevant),
"produced_at": datetime.now().isoformat(),
"key_findings": [],
"confidence": "medium",
"recommendations": [],
}
return analysis
def produce_daily_brief(self):
"""Produce daily threat intelligence brief."""
brief = {
"date": datetime.now().strftime("%Y-%m-%d"),
"total_items_processed": len(self.data),
"highlights": [],
"active_requirements_status": [
{"id": r.id, "question": r.question[:80], "status": r.status}
for r in self.requirements if r.status == "active"
],
}
return brief
Step 5: Disseminate and Track Feedback
class IntelligenceDisseminator:
def __init__(self):
self.distribution_log = []
def distribute_report(self, report, channels, classification="TLP:GREEN"):
"""Distribute intelligence report to appropriate channels."""
for channel in channels:
entry = {
"report_id": report.get("requirement_id", "daily"),
"channel": channel,
"classification": classification,
"distributed_at": datetime.now().isoformat(),
"status": "sent",
}
self.distribution_log.append(entry)
print(f" [+] Distributed to {channel}")
def collect_feedback(self, report_id, stakeholder, rating, comments=""):
"""Collect stakeholder feedback on intelligence product."""
feedback = {
"report_id": report_id,
"stakeholder": stakeholder,
"rating": rating, # 1-5
"comments": comments,
"received_at": datetime.now().isoformat(),
}
print(f"[+] Feedback received from {stakeholder}: {rating}/5")
return feedback
def calculate_metrics(self):
"""Calculate CTI program performance metrics."""
metrics = {
"total_products_distributed": len(self.distribution_log),
"distribution_by_channel": {},
}
for entry in self.distribution_log:
channel = entry["channel"]
if channel not in metrics["distribution_by_channel"]:
metrics["distribution_by_channel"][channel] = 0
metrics["distribution_by_channel"][channel] += 1
return metrics
disseminator = IntelligenceDisseminator()
Validation Criteria
- Intelligence requirements defined with priorities and stakeholders
- Collection pipeline gathering from multiple sources
- Processing deduplicates and normalizes data correctly
- Analysis produces intelligence answering specific requirements
- Dissemination reaches appropriate stakeholders through right channels
- Feedback mechanism captures and incorporates stakeholder input
References
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