| name | performing-threat-landscape-assessment-for-sector |
| description | 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. |
| domain | cybersecurity |
| subdomain | threat-intelligence |
| tags | ["threat-landscape","sector-analysis","risk-assessment","threat-intelligence","industry-targeting","cti","strategic-intelligence"] |
| version | 1.0 |
| author | mahipal |
| license | Apache-2.0 |
Performing Threat Landscape Assessment for Sector
Overview
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.
Prerequisites
- Python 3.9+ with
attackcti, requests, pandas, matplotlib libraries
- Access to threat intelligence feeds (AlienVault OTX, MISP, vendor reports)
- MITRE ATT&CK knowledge base for TTP mapping
- Industry-specific ISAC membership (FS-ISAC, H-ISAC, E-ISAC, etc.)
- 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.
Practical Steps
Step 1: Identify Threat Actors Targeting the Sector
from attackcti import attack_client
import json
class SectorThreatAssessment:
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 ():
.sector = sector.lower()
.lift = attack_client()
.groups = .lift.get_groups()
.assessment = {
: sector,
: [],
: {},
: {},
: {},
}
():
target_groups = .SECTOR_GROUPS.get(.sector, [])
actor_profiles = []
group_name target_groups:
group = (
(g g .groups
g.get(, ).lower() == group_name.lower()
group_name.lower() [a.lower() a g.get(, [])]),
)
group:
group_id =
ref group.get(, []):
ref.get() == :
group_id = ref.get(, )
techniques = []
group_id:
techs = .lift.get_techniques_used_by_group(group_id)
t techs:
ref t.get(, []):
ref.get() == :
techniques.append({
: ref.get(, ),
: t.get(, ),
})
profile = {
: group.get(, ),
: group.get(, []),
: group.get(, )[:],
: group_id,
: (techniques),
: techniques[:],
}
actor_profiles.append(profile)
()
.assessment[] = actor_profiles
()
actor_profiles
():
collections Counter
technique_counter = Counter()
actor .assessment[]:
tech actor.get(, []):
technique_counter[] +=
common = technique_counter.most_common()
.assessment[] = [
{
: tech.split()[],
: tech.split()[] tech ,
: count,
: [
a[] a .assessment[]
(t[] == tech.split()[] t a.get(, []))
],
}
tech, count common
]
()
entry .assessment[][:]:
(
)
.assessment[]
assessment = SectorThreatAssessment()
assessment.analyze_sector_actors()
assessment.identify_common_techniques()
Step 2: Analyze Attack Vectors and Initial Access
def analyze_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")
]
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",
, ],
},
: {
: [, ,
, ],
: [, ,
, ],
},
}
vectors = sector_vectors.get(assessment.sector, {})
assessment.assessment[] = vectors
vectors
Step 3: Generate Sector Threat Report
def generate_sector_report(assessment):
data = assessment.assessment
report = f"""# {data['sector'].title()} Sector Threat Landscape Assessment
Generated: {__import__('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 in enumerate(data.get("common_techniques", [])[:15], 1):
actors = ", ".join(tech["actors_using"][:3])
report += f"| {i} | {tech['technique']} | {tech['name']} | {actors} |\n"
vectors = data.get(, {})
report +=
v vectors.get(, []):
report +=
report +=
v vectors.get(, []):
report +=
report +=
(, ) f:
f.write(report)
()
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
References
