AI-powered threat correlation across 50M endpoints finds attack patterns humans can't see
A zero-day exploit hit three Fortune 500 customers simultaneously. Their SOC team took 4 hours to correlate the events across endpoints and identify the attack vector. By then, lateral movement had compromised 1,200 additional machines. The customer called it 'unacceptable.' The threat was already in the wild on security Twitter before their alert went out.
This Irvine cybersecurity firm monitored 50M endpoints generating 2TB of telemetry daily. Their rule-based detection system caught known threats effectively but couldn't identify novel attack patterns or correlate seemingly unrelated events across the customer base. Adding new detection rules was a manual process that took 2-3 weeks per rule. Meanwhile, attackers were innovating daily.
We built an AI threat correlation engine that analyzes behavioral patterns across the entire endpoint fleet in real time. Anomaly detection models identify novel attack techniques by recognizing deviations from established baselines — without needing pre-written rules. Automated threat hunting runs continuously, and our overnight security engineering team investigates AI-flagged anomalies, writes detection signatures, and deploys updates before US business hours.
We found an APT campaign targeting three customers in different industries that shared one common vendor. No human analyst would have connected those dots across 50 million endpoints. The AI saw the pattern in 8 minutes.— VP of Threat Research
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