Generative AI in cybersecurity refers to using advanced models—like large language models (LLMs), generative adversarial networks, etc.—to both protect and expose digital systems. On the defense side, it enables threat detection, anomaly spotting, automated incident response, vulnerability scanning, and synthetic data generation for secure testing. On the risk side, it also empowers attackers to craft convincing phishing, social engineering, deepfake content, malware variants, and automated exploits. Key focus areas include governance, explainability, privacy, secure model training, and building controls to distinguish malicious vs benign uses.