​The integration of generative AI in cybersecurity marks a transformative leap in combating the growing complexity of cyber threats. This chapter examines generative AI models like generative adversarial networks, variational autoencoders, and transformers, showcasing their role in threat simulation, synthetic data generation, and anomaly detection. Applications discussed include proactive defense testing, malware analysis, and intrusion detection, highlighting generative AI's ability to predict, detect, and mitigate sophisticated attacks. Emerging techniques, such as federated learning and hybrid generative models, promise further advancements. However, generative AI poses challenges, including misuse of synthetic data and adversarial vulnerabilities. Addressing these risks requires ethical guidelines, robust frameworks, and collaboration. With its predictive and adaptive potential, generative AI is reshaping cybersecurity, enabling resilient and intelligent defenses for the digital age.