Stay ahead of emerging threats with comprehensive threat intelligence services. Advanced threat detection, attribution analysis, and cyber threat exposure management for proactive security.
High-level threat landscape analysis and strategic threat actor intelligence.
TTPs, IOCs, and technical threat intelligence for operational security.
Advanced threat actor tracking, profiling, and campaign attribution.
Continuous assessment and management of organizational threat exposure.
Dark web intelligence gathering and brand protection monitoring.
Custom threat intelligence platform deployment and management.
Comprehensive threat intelligence gathering, analysis, and dissemination program
Continuous monitoring and management of your organization’s cyber threat exposure
Comprehensive APT group monitoring with campaign analysis, infrastructure tracking, and victim notification.
High-fidelity IOC feeds with context, attribution, and machine-readable threat intelligence formats.
Industry-specific threat landscape reporting with trend analysis and risk assessment.
Brand protection, executive monitoring, and digital asset exposure assessment.
collection_requirements:
strategic_intelligence:
- threat_actor_capabilities
- geopolitical_threat_landscape
- industry_threat_trends
- emerging_attack_techniques
tactical_intelligence:
- indicators_of_compromise
- tactics_techniques_procedures
- malware_analysis_reports
- infrastructure_analysis
operational_intelligence:
- imminent_threat_warnings
- vulnerability_exploitation
- campaign_notifications
- incident_attribution
# Example: APT campaign tracking framework
class APTCampaign:
def __init__(self, campaign_id, threat_group):
self.campaign_id = campaign_id
self.threat_group = threat_group
self.timeline = []
self.victims = []
self.ttps = []
self.infrastructure = []
self.malware_families = []
def add_incident(self, incident):
self.timeline.append(incident)
self.victims.extend(incident.targets)
self.ttps.extend(incident.techniques)
def analyze_patterns(self):
return {
'target_sectors': self.get_target_sectors(),
'geographic_distribution': self.get_geography(),
'attack_evolution': self.analyze_ttp_evolution(),
'infrastructure_reuse': self.track_infrastructure()
}
# Example: Executive digital footprint monitoring
class ExecutiveMonitoring:
def __init__(self, executive_profile):
self.profile = executive_profile
self.monitoring_sources = [
'social_media_platforms',
'professional_networks',
'data_breach_databases',
'dark_web_markets',
'credential_dumps'
]
def scan_for_exposure(self):
exposures = []
for source in self.monitoring_sources:
results = self.scan_source(source, self.profile)
exposures.extend(results)
return self.assess_risk(exposures)
def assess_risk(self, exposures):
risk_factors = {
'credential_exposure': self.check_credentials(exposures),
'personal_info_leak': self.check_pii(exposures),
'targeting_indicators': self.check_targeting(exposures),
'impersonation_risk': self.check_impersonation(exposures)
}
return risk_factors
misp_configuration:
data_model:
- events: threat_incidents_and_campaigns
- attributes: iocs_and_observables
- objects: complex_threat_structures
- relationships: entity_associations
sharing_groups:
- internal_threat_intel_team
- industry_sharing_consortium
- government_sharing_program
- trusted_vendor_partners
feeds:
- commercial_threat_intelligence
- open_source_feeds
- government_advisories
- industry_specific_feeds