This is like giving your security team an AI co-pilot that watches everything in your environment in real time, spots attacker behavior (including AI-generated attacks) faster than humans can, and automatically helps block and contain those attacks before they spread.
Traditional security tools and human analysts cannot keep up with the speed, volume, and sophistication of AI-assisted cyberattacks. CrowdStrike’s AI-powered defense stack uses machine learning and large-scale behavioral analytics to detect and respond to threats (including novel, AI-generated ones) at machine speed, reducing dwell time and the risk of major breaches.
Large proprietary threat-intel and telemetry corpus (endpoint, identity, cloud events), mature detection models built over years of adversary data, embedded position in customer security stack (endpoint agents, SOC workflows) and strong brand/trust in cybersecurity.
Classical-ML (Scikit/XGBoost)
Vector Search
High (Custom Models/Infra)
Inference latency and cost at high event volumes, plus data privacy/compliance constraints for using customer telemetry in AI models.
Early Majority
Positions AI as both an offense and defense accelerant: focuses on countering AI-enabled adversaries by combining long-standing ML-based detection with newer generative/assistant capabilities, all plugged into existing telemetry at massive scale.