Xenia Mountrouidou is a Principal Applied AI Scientist at AppGate, where she builds AI-powered security systems for Zero Trust Network Access. She brings experience across academia and industry.
Her work spans the full lifecycle of applied ML: from data-driven detection research (network intrusion, IoT anomalies, adversarial patterns) to production system design and deployment. She works across exploratory data analysis, feature engineering, classical classifiers, and LLM fine-tuning, and takes those models into production architecture: benchmarking model latency and accuracy tradeoffs, designing serving infrastructure, and building stateful, low-latency security pipelines for real-time inference. She holds a PhD in Computer Science from North Carolina State University.
Xenia has authored scholarly papers on intrusion detection and security metrics, and regularly presents at academic and industry conferences including SANS AI, BSides, and Cackalackycon.
- Machine Learning for Security
- Intrusion Detection Systems
- Network Security
- IoT Security
- Data Science
- Agentic AI Security
- ML Systems & Deployment
PhD in Computer Science
North Carolina State University
M.S. in Computer Engineering
University of Patras
B.S. in Computer Science
University of Crete
I speak regularly on both the evaluation side (how to rigorously benchmark ML/LLM systems for security) and the systems side (how to architect and deploy them safely) at venues including SANS AI, BSides, and Cackalackycon.
