Healthcare Security Practice Analysis, Modelling and Incentivization

The human aspect of information security practice has become a global concern. According to Verizon’s 2022 data breaches report, over 80% of data breaches were caused by the human aspect, and this trend has been consistent over the past three years. Among the industries, 22% of the violations occurred within healthcare. These breaches are widely caused by external actors (61%) who are motivated by financial gains. Ransomware through phishing attacks has been the preferred tactic. Such incidents have caused financial loss to some hospitals and resulted in the loss of human life. The security practice in relation to the human aspect is about how people comply with organizational security requirements towards safe-guiding the confidentiality, integrity and availability (CIA) of assets within an IT infrastructure. Technological security configurations have predominantly been relied on as the default and traditional information security controls. Through consistent development, the technological aspect has comparatively been enhanced and matured, thereby, increasing the puzzle for cyber-criminal to circumvent. As a result, cyber-criminals tend to exploit the human aspect as an easy entry point. This research work, therefore, delves into the human aspect of security practice aimed to contribute towards the fortification of ”the human firewall”, through incentivising the security practice of healthcare staff. Some research activities have been conducted in this area. However, initial state of-the-art studies revealed in-comprehensiveness in the existing efforts. As a result, comprehensive approaches were first explored for modelling and analyzing the security practice of healthcare staff in the aspects of data-driven and artificial intelligence or machine learning approaches, attack and defence simulations and psychological, social, cultural and work factors. Furthermore, motivational methods for incentivizing security practices were also explored. This is deemed to be a holistic approach towards enhancing security practices among healthcare staff. Within the area of data-driven, various methods, including, K-means clustering with iterative and discriminate clustering, were used to assess the security practice in electronic health records (EHR) logs in this research work. Through the assessment, an unusual session duration was revealed in which an average session of about 12,330 hours was detected. Meanwhile, at maximum, a healthcare staff session is estimated to