Payton Rudnick

I’m Payton Rudnick, a data scientist specializing in leveraging machine learning and AI for advanced network security solutions for the U.S. Air Force. I hold two undergraduate degrees in Finance and Management Information Systems, as well as a Master’s degree in Management Information Systems from the University of Arizona. My experience working in a security operations center and monitoring network traffic has given me deep insights into the limitations of traditional rule-based security implementations. I am passionate about developing AI-driven solutions that shift from static rules to behavior-driven insights, enabling security analysts to detect threats more efficiently.

My current research focuses on identifying domain generation algorithms used for command-and-control channels, detecting data exfiltration over DNS, and application of time-series anomaly detection (TSAD) to various cybersecurity challenges. Leading Z Collective’s TSAD efforts, my aim is to create scalable solutions applicable across multiple use cases – such as network traffic volume, user registrations, and C2 beaconing – while remaining accessible and interpretable for network analysts.