Artificial Intelligence for Cybersecurity in Banking: A Taxonomy of Barriers and Possible Mitigation Strategies

In today’s technologically advanced era, the banking sector still faces significant challenges in mitigating cyberattacks, despite the availability of innovative technologies such as cybersecurity expert systems (CSES). Barriers hinder the effective implementation of these technologies. The primary objective of SLR is to identify and map the barriers and mitigation strategies for the use of AI in cybersecurity. Attributed to recent developments in artificial intelligence, its scalability, and adaptability in cybersecurity, the current study limits its research span to the period between 2018 and 2024 (November). Based on the inclusion criteria, 65 articles were selected for the study. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, literature from selected databases is extracted and synthesized to identify barriers to the use of AI in cybersecurity. This research identified 23 barriers and 12 mitigation strategies, and further categorized these findings within the Technological, Organizational, and Environmental (TOE) framework using thematic analysis. Hence, the current research makes a novel contribution by providing a framework for future studies, diverting attention toward adoption barriers to accelerate the use of AI for cybersecurity.