AI skills in cybersecurity: global job trends analysis

Purpose This study aims to identify the key artificial intelligence (AI) skills in demand for cybersecurity roles globally and examines their relationships with cybersecurity tasks across different countries. It aims to address the knowledge gap in AI skill requirements and how they vary regionally to inform workforce development and educational programs. Design/methodology/approach Using semantic network analysis (SNA), the study analyzes 8,262 job postings from nine countries, including the USA, UK, UAE, France, Germany, Canada, Belgium, Australia and Italy. Data was collected from Indeed.com using a Python tool, followed by text preprocessing and network mapping of AI skills. Findings The analysis shows that AI skills such as machine learning (ML), natural language processing (NLP), predictive analytics and neural networks are in high demand globally. These skills are closely tied to cybersecurity functions such as threat intelligence, anomaly detection and automated incident response. Regional differences exist, with the USA and UK focusing on threat intelligence, while the UAE emphasizes automated incident response. Research limitations/implications The study is limited to job postings from Indeed.com. Expanding to other job platforms and regions would provide a broader perspective. The subjective interpretation of SNA may also introduce bias in identifying skill relationships. Practical implications Educational institutions, job seekers and employers can use these findings to tailor curricula, job descriptions and training programs, addressing the most critical AI skills in cybersecurity. Originality/value To the best of the author’s knowledge, this study is among the first to use SNA to map global AI skills demand in cybersecurity, offering valuable cross-country insights that fill a critical research gap.