Machine learning (ML) is increasingly used to combat cyberthreats. ML enables tools known as security classifiers to identify potential cyberthreats, e.g., to detect malicious software ("malware") or a network intrusion. Such classifiers are typically developed by collecting data on threats (e.g., malware samples) and benign entities (e.g., legitimate software), then building an ML model that learns patterns in the gathered training data that suggest the presence of threats. The model is then used in real systems to help identify new undetected threats. However, for many security problems, good training data is hard to find. Threats may be relatively rare, or not shared by people and companies that experience them. This leads to unbalanced datasets that contain mostly benign cases, which ML models often struggle with. Threats also change over time, as malicious software is constantly evolving, and models may quickly go out of date. This project will develop ways to address these data challenges by developing methods for Generative Artificial Intelligence (GenAI) tools to create synthetic but useful data for network and application security tasks. Through this, the project will advance knowledge of both GenAI systems and more practical, effective defenses against cyberthreats. The project team will also create novel educational resources on AI and security topics and provide educational opportunities for pre-college teachers and students and research opportunities for undergr