The role of Artificial Intelligence in SOC operations: Adoption, perception, and workforce impact

This paper examines the incorporation of artificial intelligence (AI) in cybersecurity operations, with a specific focus on applied machine learning within Security Operations Centres (SOCs). A mixed-methods approach was used, surveying 58 cybersecurity professionals from sectors including banking, healthcare, and technology, to investigate adoption, perceived advantages and drawbacks, and the implications for the human workforce. Quantitative results indicate extensive use of machine learning for alert triage and behavioural analytics, while qualitative findings underscore conditional trust, deficiencies in training, and the evolving dynamics of analyst roles. Thematic analysis identified fundamental categories such as AI as copilot, explainability and trust, and ethical risk. These findings indicate that although machine learning improves efficiency and alleviates cognitive burden, its adoption relies on transparent governance, continuous human monitoring, and professional development. This study enhances academic and industrial discussions by prioritising practitioner perspectives and clarifying the socio-technical considerations of machine-learning adoption in SOCs.