AI-Powered Predictive Cybersecurity in Identifying Emerging Threats through Machine Learning

With an emphasis on the incorporation of the Wasserstein Generative Adversarial Network (WGAN) algorithm, this study examines the revolutionary potential of AI-Powered Predictive Cybersecurity in detecting new risks using machine learning. The adoption of an innovative cybersecurity approach within CyberGuard Bank is investigated in this paper through the use of a fictitious case study set in the financial sector. With the use of cutting-edge machine learning techniques and WGAN-generated synthetic data, the model exhibits improved threat detection, false positive reduction, real-time threat mitigation, and scalability capabilities. The implementation places a strong emphasis on ethical issues, including as bias prevention and privacy compliance, which positions the company as a responsible steward of AI-driven cybersecurity. The results highlight the strategic importance of WGAN and AI in strengthening defences against the constantly changing and dynamic array of cyberthreats.