Learning Personalized Human Decision Models in Cyber Defense

This research proposes a method for AI systems to learn and understand the unique decision-making patterns of individual human operators in cyber defense roles, such as their risk tolerance and curiosity levels. Rather than trying to copy what operators do, the approach uses a kernel-based inverse learning framework (a mathematical technique to infer hidden traits from observed behavior) to build personalized models that can provide better guidance and support. The method was tested with 108 participants and showed it can accurately predict individual decision-making styles even with limited data, helping AI assistants adapt their support to different operators while maintaining mission safety.