This paper investigates the susceptibility to phishing attacks using explainable artificial intelligence (XAI) techniques. Phishing attacks, which involve deceptive websites to steal sensitive information, are a growing threat. The study aims to understand the factors influencing individual susceptibility and develop personalized strategies to mitigate phishing risks. A deep neural network (DNN) model was trained with SHAP (SHapley Additive exPlanations), an XAI method, to predict phishing susceptibility and provide explanations for individual predictions. The model achieved an accuracy of 78%, with a recall of 71% and precision of 57%. The analysis revealed that psychological factors such as impulsivity and conscientiousness, along with online security habits, significantly affect susceptibility. The study also offers personalized recommendations based on individual characteristics to reduce phishing risks. The research highlights the importance of human and demographic factors in phishing susceptibility and provides insights into how these factors influence individual vulnerability. The findings suggest that improving security behaviors, such as checking website security and email legitimacy, can reduce the risk of falling victim to phishing attacks. The study also discusses the limitations of previous research and proposes future directions, including the integration of post-survey data and the development of more adaptive models. The results emphasize the need for personalized approaches in cybersecurity education and training to address individual vulnerabilities effectively.This paper investigates the susceptibility to phishing attacks using explainable artificial intelligence (XAI) techniques. Phishing attacks, which involve deceptive websites to steal sensitive information, are a growing threat. The study aims to understand the factors influencing individual susceptibility and develop personalized strategies to mitigate phishing risks. A deep neural network (DNN) model was trained with SHAP (SHapley Additive exPlanations), an XAI method, to predict phishing susceptibility and provide explanations for individual predictions. The model achieved an accuracy of 78%, with a recall of 71% and precision of 57%. The analysis revealed that psychological factors such as impulsivity and conscientiousness, along with online security habits, significantly affect susceptibility. The study also offers personalized recommendations based on individual characteristics to reduce phishing risks. The research highlights the importance of human and demographic factors in phishing susceptibility and provides insights into how these factors influence individual vulnerability. The findings suggest that improving security behaviors, such as checking website security and email legitimacy, can reduce the risk of falling victim to phishing attacks. The study also discusses the limitations of previous research and proposes future directions, including the integration of post-survey data and the development of more adaptive models. The results emphasize the need for personalized approaches in cybersecurity education and training to address individual vulnerabilities effectively.