Simulated Spear Phishing Branching for Targeted User Training

The patent segments the user base into different risk groups based on their susceptibility scores, which are determined through machine learning analysis of user interactions with simulated phishing messages. This segmentation allows the system to provide targeted training only to users who need it, rather than uniformly training all users, thereby reducing overall resource consumption while maintaining effective user awareness improvement.

The system dynamically changes training parameters (such as training content, intensity, and timing) based on individual user susceptibility scores and interaction patterns. By adjusting these parameters according to user-specific risk profiles, the system optimizes resource allocation to provide appropriate training levels to different users, reducing waste on low-risk users while ensuring adequate training for high-risk users.

The patent uses machine learning models to create simplified representations (copies) of complex user behavior patterns and susceptibility profiles. These models analyze user interactions with simulated phishing messages and generate susceptibility scores that capture essential risk characteristics without requiring complex manual analysis. This copying approach enables personalized training recommendations while keeping the underlying system complexity manageable through automated pattern recognition.

The system implements self-service capabilities where users automatically receive personalized training based on their own interaction data with simulated phishing messages. The machine learning system autonomously analyzes user behavior, generates susceptibility scores, and recommends appropriate training modules without requiring manual intervention from trainers or administrators for each user, thereby reducing operational complexity while maintaining personalization.

The patent implements a cushioning mechanism by sending simulated phishing messages in a controlled, gradual manner rather than all at once. The system uses branching message templates that adapt based on user responses, and it limits the frequency and intensity of simulated messages to prevent overwhelming users or causing excessive stress. This beforehand cushioning approach allows for precise susceptibility measurement while mitigating potential harm from repeated exposure to phishing-like content.

The system converts the potentially harmful exposure to phishing messages into a beneficial training opportunity. By carefully designing simulated messages that are recognizable as simulations and by providing immediate feedback and training based on user responses, the system transforms what could be harmful exposure into valuable learning experiences that improve user awareness and reduce actual phishing vulnerability.