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Résumé : This paper examines the evolution of cybersecurity assault prediction methods, which have evolved from basic Machine Learning (ML) methods to more intricate Generative AI methods. In the past, standard machine learning models such as Neural Networks, Decision Trees, and Random Forests were frequently employed to identify deviant behavior. The complexity of cyberthreats has increased over time. Generative AI technologies, such as GANs and large language models, are employed to anticipate and mitigate hazards prior to their occurrence. Modern cybersecurity systems are more effective at safeguarding against cyberattacks due to the implementation of new technologies that enhance the accuracy of predictions, automate hazard analysis, and enhance the detection of unusual behavior.