Predictive Analysis of Cybersecurity Risks Through Employee Behavior Modeling

This paper examines how employee behavior contributes to organizational cybersecurity risk and reviews approaches that can help organizations recognize and reduce these risks. Common risk-related behaviors include reusing weak passwords, interacting with suspicious messages, mishandling sensitive information, and delaying incident reporting. The paper compares classroom training, online learning, gamified learning, and simulated attack exercises. It proposes a conceptual awareness framework combining baseline assessment, role-based learning, practical simulations, reinforcement, and feedback. The framework is a proposal rather than a validated predictive model. This paper does not present new empirical data or a tested machine-learning algorithm. Future work should validate the framework using a documented dataset, measurable behavioral indicators, and an appropriate evaluation method.