Data-Driven and Artificial Intelligence (AI) Approach for Modelling and Analyzing Healthcare Security Practice: A Systematic Review Prosper Kandabongee Yeng 1 , Livinus Obiora Nweke 1 , Ashenafi Zebene Woldaregay 2 , Bian Yang 1 and Einar Arthur Snekkenes 1 1 Norwegian University of Science and Technology, Technolgien 22, 2815 Gjøvik, Norway 2 University of Tromsø, The Arctic University of Norway, Hansine Hansens veg 18, 9019 Tromsø, Norway Prosper.yeng@ntnu.no Abstract. Data breaches in healthcare continue to grow exponentially, calling for a rethinking into better approaches of security measures towards mitigating the menace. Traditional approaches including technological measures, have significantly contributed to mitigating data breaches but what is still lacking is the development of the “human firewall,” which is the conscious care security practices of the insiders. As a result, the healthcare security practice analysis, modeling and incentivization project (HSPAMI) is geared towards analyzing healthcare staffs’ security practices in various scenarios including big data. The intention is to determine the gap between staffs’ security practices and required security practices for incentivization measures. To address the state-of-the art, a systematic review was conducted to pinpoint appropriate AI methods and data sources that can be used for effective studies. Out of about 130 articles, which were initially identified in the context of human-generated healthcare data for security measures in healthcare, 15 articles were found to meet the inclusion and exclusion criteria. A thorough assessment and analysis of the included article reveals that, KNN, Bayesian Network and Decision Trees (C4.5) algorithms were mostly applied on Electronic Health Records (EHR) Logs and Network logs with varying input features of healthcare staffs’ security practices. What was found challenging is the performance scores of these algorithms which were not sufficiently outlined in the existing studies. Keywords: Artificial Intelligence, Machine Learning, Healthcare, Security Practice 1 Introduction The enormous increase in data breaches within healthcare is frightening and has become a source of worry for many stakeholders such as healthcare providers, patients, national and international bodies. In 2018, the healthcare sector recorded about 15 million records which were compromised in about 503 data breaches [1, 2]. This was a triple of 2017 data breaches in healthcare [1, 2]. In the middle of 2019, the number of compromised records in healthcare were more than 25 million, implying that by the end of 2019, the number of compromised records might have sky rocketed [2]. Greater