Identifying the drivers and inhibitors of using AI for cyber threat detection and mitigation

Abstract

Small organisations in Malta face the greatest risk from cyber threats while also encountering the most significant challenges in adopting AI-based defences, a paradox that existing AI adoption does not adequately address. The majority of present research on AI-driven cybersecurity focuses on sectors such as finance, healthcare, and e-commerce. This study analyses the drivers and inhibitors influencing the AI adoption for cyber threat detection and mitigation. The findings were explained using the TAM model (Davis, 1989), using perceived usefulness and perceived ease of use as analytical constructs. The study used a qualitative and exploratory approach. Primary data was collected using structured interviews with eleven senior cybersecurity professionals, including CTOs and senior managers. Academic literature and market analysis were examined to put the findings into perspective within broader trends in AI adoption. The key factors that influence AI adoption are the increasing complexity of cyber threats, the need for monitoring, regulatory requirements like the NIS2 Directive and the European AI Act, and the need for productivity. The main constraints are implementation costs, shortage of professionals with cybersecurity expertise, technical complexity when integrating with legacy systems, and limitations related to data quality and availability. The study suggests that instead of adapting enterprise-level tools downward, smaller organisations focus on phased AI implementation, invest in targeted upskilling, and adopt governance frameworks scaled to their organisational context.