Artificial Intelligence (AI) and Machine Learning (ML) are rapidly transforming the cybersecurity field by enabling automation, predictive threat analysis, and advanced data interpretation. Research from books, journals, and industry publications, especially those released around 2021, shows that cybersecurity professionals now work in complex AI-supported environments that demand skills beyond traditional security knowledge. Studies like AI and Cybersecurity Integration (Rao & Simmons, 2021) and reports from the World Economic Forum (2021) indicate that modern roles increasingly require proficiency in data-driven decision-making, algorithmic thinking, model evaluation, and defending against adversarial ML threats. Articles from major newspapers such as The Guardian and The New York Times (2020–2023) also highlight growing public concerns around deep fakes, AI misuse, and the need for trained specialists.

This review brings together findings from academic research, technical publications, and credible news sources to explore how AI and ML skills are defined, taught, and applied in cybersecurity roles. Results show a widening gap between the competencies expected by employers and the skills currently taught in universities. Even though many organisations rely on AI-driven security systems, academic programs and competency models have not kept pace. The review argues that preparing the cybersecurity workforce for AI-intensive environments will require standardised competency frameworks, modernised curricula, and continuous validation of training approaches.