Conduct a sector-specific threat landscape assessment by analyzing threat actor targeting patterns, common attack vectors, and industry-specific vulnerabilities to inform organizational risk management.
Conduct a sector-specific threat landscape assessment by analyzing threat actor targeting patterns, common attack vectors, and industry-specific vulnerabilities to inform organizational risk management.
A sector-specific threat landscape assessment analyzes the cyber threat environment facing a particular industry vertical (healthcare, financial services, energy, government, manufacturing) by examining which threat actors target the sector, their preferred attack vectors and TTPs, common vulnerabilities exploited, historical incident data, and emerging threats. This produces actionable intelligence for risk management, security investment prioritization, and board-level reporting.
When to Use
When conducting security assessments that involve performing threat landscape assessment for sector
When following incident response procedures for related security events
When performing scheduled security testing or auditing activities
When validating security controls through hands-on testing
Detection Gaps & Validation
Hardcoded actor lists go stale: the SECTOR_GROUPS mapping is a snapshot - groups emerge, rebrand, and shift targeting quarterly, and ATT&CK documents only publicly reported groups, undercounting fresh ransomware affiliates and access brokers. Refresh against current ATT&CK, vendor annual reports (M-Trends, CrowdStrike GTR), and your sector ISAC rather than trusting a static list.
ATT&CK TTP coverage is incomplete:get_techniques_used_by_group reflects only techniques analysts documented from observed intrusions; absence of a technique is not evidence the group cannot use it. Treat the technique list as a floor, not a ceiling.
Vendor-naming overlap: the same activity is named differently per vendor, and clusters are sometimes merged or split, so aggregating across sources double-counts or conflates groups. Normalize aliases before ranking "most common techniques."
Reporting bias: well-funded nation-state APTs are over-documented while higher-probability criminal/ransomware threats are under-represented in ATT&CK - weight the assessment by likelihood and recent sector incidents, not just available documentation.
How to confirm: corroborate sector targeting with at least two independent sources (ISAC advisories plus a vendor report) and recent incident data before driving security investment from the profile.
Prerequisites
Python 3.9+ with attackcti, requests, pandas, matplotlib libraries
Access to threat intelligence feeds (AlienVault OTX, MISP, vendor reports)
Understanding of sector-specific regulatory requirements
Key Concepts
Sector Targeting Analysis
Different sectors face different threat profiles. Financial services face sophisticated nation-state actors (Lazarus Group) and cybercriminal groups focused on financial fraud. Healthcare faces ransomware groups exploiting urgency and legacy systems. Energy and critical infrastructure face nation-state groups (TEMP.Veles, Sandworm) with destructive capabilities. Government faces espionage-focused APTs (APT29, APT28, Turla).
Threat Landscape Components
A comprehensive assessment includes: threat actor profiling (groups targeting the sector), attack vector analysis (initial access methods observed), TTP mapping (techniques commonly used against sector), vulnerability landscape (CVEs commonly exploited), incident trend analysis (breach frequency, impact, recovery time), and emerging threats (new groups, evolving techniques, supply chain risks).
Intelligence Sources
Sector-specific intelligence comes from ISACs (Information Sharing and Analysis Centers), government advisories (CISA, FBI, NSA), vendor threat reports (CrowdStrike Annual Threat Report, Mandiant M-Trends, Verizon DBIR), and academic research on sector-specific attacks.
Workflow
Step 1: Identify Threat Actors Targeting the Sector
from attackcti import attack_client
import json
classSectorThreatAssessment:
SECTOR_GROUPS = {
"financial": ["FIN7", "FIN8", "FIN11", "Carbanak", "Lazarus Group",
"Cobalt Group", "TA505", "GOLD SOUTHFIELD"],
"healthcare": ["FIN12", "Ryuk", "Conti", "Wizard Spider",
"GOLD ULRICK", "Vice Society"],
"energy": ["TEMP.Veles", "Sandworm Team", "Dragonfly",
"XENOTIME", "ERYTHRITE", "Berserk Bear"],
"government": ["APT29", "APT28", "Turla", "Gamaredon Group",
"Mustang Panda", "APT41", "Lazarus Group"],
"manufacturing": ["APT41", "TEMP.Veles", "Dragonfly",
"HEXANE", "MAGNALLIUM"],
"technology": ["APT41", "Lazarus Group", "APT10",
"HAFNIUM", "Winnti Group"],
}
def__init__(self, sector):
self.sector = sector.lower()
self.lift = attack_client()
self.groups = self.lift.get_groups()
self.assessment = {
"sector": sector,
"threat_actors": [],
"common_techniques": {},
"attack_vectors": {},
"risk_summary": {},
}
defanalyze_sector_actors(self):
"""Analyze threat actors known to target this sector."""
target_groups = self.SECTOR_GROUPS.get(self.sector, [])
actor_profiles = []
for group_name in target_groups:
group = next(
(g for g inself.groups
if g.get("name", "").lower() == group_name.lower()
or group_name.lower() in [a.lower() for a in g.get("aliases", [])]),
None
)
if group:
group_id = ""for ref in group.get("external_references", []):
if ref.get("source_name") == "mitre-attack":
group_id = ref.get("external_id", "")
break
techniques = []
if group_id:
techs = self.lift.get_techniques_used_by_group(group_id)
for t in techs:
for ref in t.get("external_references", []):
if ref.get("source_name") == "mitre-attack":
techniques.append({
"id": ref.get("external_id", ""),
"name": t.get("name", ""),
})
break
profile = {
"name": group.get("name", ""),
"aliases": group.get("aliases", []),
"description": group.get("description", "")[:300],
"attack_id": group_id,
"technique_count": len(techniques),
"techniques": techniques[:20],
}
actor_profiles.append(profile)
print(f" [+] {group.get('name')}: {len(techniques)} techniques")
self.assessment["threat_actors"] = actor_profiles
print(f"[+] Profiled {len(actor_profiles)} threat actors for {self.sector}")
return actor_profiles
defidentify_common_techniques(self):
"""Find the most commonly used techniques across sector actors."""from collections import Counter
technique_counter = Counter()
for actor inself.assessment["threat_actors"]:
for tech in actor.get("techniques", []):
technique_counter[f"{tech['id']}:{tech['name']}"] += 1
common = technique_counter.most_common(20)
self.assessment["common_techniques"] = [
{
"technique": tech.split(":")[0],
"name": tech.split(":")[1] if":"in tech else"",
"actor_count": count,
"actors_using": [
a["name"] for a inself.assessment["threat_actors"]
ifany(t["id"] == tech.split(":")[0] for t in a.get("techniques", []))
],
}
for tech, count in common
]
print(f"\n=== Top Techniques for {self.sector.upper()} ===")
for entry inself.assessment["common_techniques"][:10]:
print(f" {entry['technique']}{entry['name']}: "f"used by {entry['actor_count']} groups")
returnself.assessment["common_techniques"]
assessment = SectorThreatAssessment("financial")
assessment.analyze_sector_actors()
assessment.identify_common_techniques()
Step 2: Analyze Attack Vectors and Initial Access
defanalyze_attack_vectors(assessment):
"""Analyze initial access vectors common for the sector."""
initial_access_techniques = [
t for t in assessment.assessment["common_techniques"]
if t["technique"].startswith("T1566") or t["technique"].startswith("T1190")
or t["technique"].startswith("T1133") or t["technique"].startswith("T1078")
or t["technique"].startswith("T1195")
]
# Supplement with known sector-specific vectors
sector_vectors = {
"financial": {
"primary": ["Spearphishing (T1566)", "Exploit Public-Facing App (T1190)",
"Valid Accounts (T1078)", "Supply Chain Compromise (T1195)"],
"emerging": ["MFA Fatigue/Push Bombing", "QR Code Phishing (Quishing)",
"Business Email Compromise", "API Key Theft"],
},
"healthcare": {
"primary": ["Spearphishing (T1566)", "Exploit Public-Facing App (T1190)",
"External Remote Services (T1133)", "Valid Accounts (T1078)"],
"emerging": ["IoMT Device Exploitation", "Telehealth Platform Attacks",
"Medical Device Firmware Attacks", "Supply Chain via EHR Vendors"],
},
"energy": {
"primary": ["Spearphishing (T1566)", "Exploit Public-Facing App (T1190)",
"External Remote Services (T1133)", "Supply Chain Compromise (T1195)"],
"emerging": ["OT/ICS Protocol Exploitation", "Remote Access to SCADA",
"Engineering Workstation Compromise", "Vendor VPN Exploitation"],
},
}
vectors = sector_vectors.get(assessment.sector, {})
assessment.assessment["attack_vectors"] = vectors
return vectors
Step 3: Generate Sector Threat Report
defgenerate_sector_report(assessment):
data = assessment.assessment
report = f"""# {data['sector'].title()} Sector Threat Landscape Assessment
Generated: {datetime.datetime.now().isoformat()}
## Executive Summary
This assessment analyzes the cyber threat landscape for the {data['sector']} sector,
identifying {len(data['threat_actors'])} active threat groups, their preferred techniques,
and recommended defensive priorities.
## Threat Actor Summary
| Actor | ATT&CK ID | Techniques | Key Focus |
|-------|-----------|------------|-----------|
"""for actor in data["threat_actors"]:
report += (f"| {actor['name']} | {actor['attack_id']} "f"| {actor['technique_count']} | {actor['description'][:60]}... |\n")
report += f"""
## Most Common Techniques
| Rank | Technique | Name | Groups Using |
|------|-----------|------|-------------|
"""for i, tech inenumerate(data.get("common_techniques", [])[:15], 1):
actors = ", ".join(tech["actors_using"][:3])
report += f"| {i} | {tech['technique']} | {tech['name']} | {actors} |\n"
vectors = data.get("attack_vectors", {})
report += f"""
## Attack Vectors
### Primary Vectors
"""for v in vectors.get("primary", []):
report += f"- {v}\n"
report += "\n### Emerging Vectors\n"for v in vectors.get("emerging", []):
report += f"- {v}\n"
report += """
## Recommendations
1. Prioritize detections for the top 10 techniques used by sector-targeting groups
2. Conduct threat-informed red team exercises mimicking identified actors
3. Join sector ISAC for real-time threat sharing
4. Implement controls for identified initial access vectors
5. Review supply chain security posture for sector-specific risks
"""withopen(f"threat_landscape_{data['sector']}.md", "w") as f:
f.write(report)
print(f"[+] Sector report saved: threat_landscape_{data['sector']}.md")
generate_sector_report(assessment)
Validation Criteria
Sector-specific threat actors identified and profiled
Common techniques across actors analyzed and ranked
Attack vectors mapped for the target sector
Emerging threats identified based on recent intelligence
Comprehensive sector threat report generated
Recommendations actionable for security investment decisions
