| name | performing-threat-landscape-assessment-for-sector |
| description | Conducts a sector-specific threat landscape assessment (financial, healthcare, energy, government, etc.) by profiling targeting threat actors, mapping attack vectors and MITRE ATT&CK TTPs with the attackcti/pandas Python stack, and analyzing exploited CVEs and incident trends from ISAC and vendor reports. Use when producing CTI for risk management or board-level reporting on an industry's threat exposure. |
| 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 |
| d3fend_techniques | ["File Metadata Consistency Validation","Application Protocol Command Analysis","Identifier Analysis","Content Format Conversion","Message Analysis"] |
| nist_csf | ["ID.RA-01","ID.RA-05","DE.CM-01","DE.AE-02"] |
| mitre_attack | ["T1591","T1592","T1593","T1589","T1566"] |
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.
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
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.
Workflow
Step 1: Identify Threat Actors Targeting the Sector
from attackcti attack_client
json
:
SECTOR_GROUPS = {
: [, , , , ,
, , ],
: [, , , ,
, ],
: [, , ,
, , ],
: [, , , ,
, , ],
: [, , ,
, ],
: [, , ,
, ],
}
():
.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()
