Your Behavior Can Expose Fraud
Behavioral biometrics analyze device-specific patterns such as typing speed, touch pressure, and hand orientation to distinguish human users from automated fraud bots. By correlating these metrics with geolocation and IP address anomalies, systems can flag discrepancies that bypass traditional multi-factor authentication and one-time PINs. This approach shifts fraud detection from static credentials to dynamic user behavior, identifying non-human actors attempting to enroll in digital payments. The technology effectively exposes fraud by matching real-time interaction data against established behavioral baselines.
A Conversation With Ariful and Jakub
Founded in 2023, the company deploys autonomous agents to automate the four core functions of security operations: detection, triaging, investigation, and response. The system utilizes anomaly-based statistical modeling and traditional machine learning to generate pre-baked detections for vectors like GitHub repositories and identity compromise without requiring manual rule creation. While detection relies on deterministic algorithms, the platform leverages Large Language Models specifically for triaging and natural language query conversion to transform small analyst teams into high-output units. This hybrid approach enables organizations to achieve 24/7 monitoring within days while scaling existing Security Operations Centers without proportional headcount increases.