AI-native cyber threat detection

AI agents deployed by attackers automate reconnaissance, adapt strategies in real time, and coordinate multi-vector campaigns that overwhelm human-paced defenses. Traditional tools — signature-based detection and rules-based threat intelligence — cannot detect AI agent attack patterns early enough.

Cybersecurity Ichnos-0 changes this by identifying AI-driven attack patterns and non-compliant AI agent behaviour before they materialize as incidents.

Key capabilities

Technology stack vulnerability inference: identifies architectural patterns exploitable by AI-driven attack tools before deployment.

AI attack pattern recognition from non-traditional signals: reads patent filings, security research, and architecture documents to identify emerging AI attack methodologies early.

Supply chain attack vector mapping: maps software dependencies and API integrations that create attack pathways.

Proactive exposure assessment: continuously updates AI-driven cyber exposure as new tools and threat actor capabilities emerge.

Institutional applications

Cyber insurance underwriting with inference-derived assessments of policyholder architecture vulnerability and AI attack surface.

Enterprise proactive defense prioritization against the AI attack vectors most likely to target specific architecture.

Financial system cyber risk management for banks, exchanges, and payment systems facing disproportionate AI agent attack exposure.