ITEL 351 Data-Driven AI Security and Assurance

This course examines how data science is used to design, evaluate, and secure AI applications in cybersecurity environments. Students analyze the role of security-relevant data in supporting AI models, including data used for threat detection, anomaly identification, vulnerability prioritization, fraud detection, malware analysis, and LLM security. Emphasis is placed on preparing trustworthy data, evaluating model outputs, identifying data-driven risks such as bias, poisoning, leakage, and drift, and using analytical evidence to improve the security and reliability of AI-enabled cybersecurity applications.