This paper explores the application of zero-shot learning, a novel machine learning approach, in predicting and mitigating cybersecurity threats and defenses. Our research bridges the gap between the theory and practice of using zero-shot learning, a technique that allows models to adapt to unseen scenarios, in anticipating and managing cybersecurity threats. We propose new attack methodologies based on zero-shot learning and discuss the development of robust defense mechanisms. An interdisciplinary approach, combining social engineering, behavioral psychology, and AI governance, underpins our work. Our research also introduces a quantitative model to assess the impact of these emerging threats and a proof-of-concept prototype. In closing, we discuss ethical considerations and advocate for responsible AI practices and robust regulations to prevent misuse. This paper underscores the potential of zero-shot learning to revolutionize cybersecurity practices and preparations for future threats.