As generative artificial intelligence (AI) technologies, such as OpenAI’s GPT-4 and other large language models (LLMs), advance, the cybersecurity landscape faces a pressing challenge: the rise of AI-generated phishing attacks. These attacks exploit AI’s ability to convincingly mimic human language at scale, making them significantly harder to detect than traditional, human-generated phishing attempts. The purpose of this study is to address this growing threat by analysing the unique characteristics of AI-driven phishing emails, focusing on the linguistic and contextual features that distinguish them from human-authored emails. The research identifies a critical limitation in current phishing detection systems, which exhibit low precision and recall when dealing with AI-generated content. To solve this issue, we propose a novel detection framework that combines advanced machine learning and natural language processing (NLP) techniques with behavioural analysis. Experimental results demonstrate the effectiveness of the proposed model, achieving a precision rate of 91% and a recall rate of 89% and an F1 score of 90% in identifying AI-generated phishing emails—substantially outperforming existing systems. This paper’s main contributions include a comprehensive analysis of AI-generated phishing email characteristics, the development of an innovative detection framework, and the presentation of adaptive strategies to enhance cybersecurity defences against the growing threat of generative AI.