Mitigating Malware in Zambian Healthcare System: an AI Driven Approach

Abstract

Cyberattacks pose a severe threat to organizations worldwide, including Zambia. The Global Risks Report 2023 [1] notes that malware is the primary attack vector for cybercrime, which is becoming more common and dynamic. Malware, including worms, trojans, and ransomware, is typically disseminated through various methods, including social engineering and phishing, and can cause significant financial losses and data breaches. According to Siampondo [2], cybercrime is becoming increasingly prevalent in Zambia, highlighting the urgent need to address this issue. Conventional security solutions, such as signature-based detection, struggle to keep pace with malware's constant evolution. As a result, artificial intelligence (AI) has emerged as a powerful tool for detecting, predicting, and preventing threats in real-time. Deep learning and machine learning approaches enable effective threat pattern detection within large datasets. However, much remains to be learned about the adoption and efficacy of AI powered malware detection and protection systems in Zambia's cyberthreat reduction efforts, particularly within the healthcare sector. The Zambian healthcare industry has rapidly digitized with the expanded rollout of Electronic Health Records (EMRs) and networked systems. The healthcare sector is becoming increasingly vulnerable to cyberattacks due to various factors, including insufficient cybersecurity awareness, siloed systems, and outdated infrastructure. These issues, coupled with the sensitive nature of patient data, highlight the critical need for robust cybersecurity solutions. By investigating the feasibility, challenges, and benefits of AI-driven malware prevention and detection, we aim to contribute to enhancing the cybersecurity posture of public, private, and faith-based healthcare organizations. Our study focuses on how artificial intelligence can be utilized to detect and prevent malware in healthcare organizations, secure patient data, ensure service continuity, and develop resilience against cyberattacks, with a particular emphasis on minimizing malware incidents. Statistical analysis will be employed to test assumptions regarding the relative benefits of various AI technologies (e.g., machine learning vs. deep learning) and the impact of advanced security design. The experiences and perspectives of security professionals will be captured through thematic analysis of qualitative data. This research aims to contribute to the ongoing discussion on the adoption and integration of AI-powered security technologies in critical sectors, with a focus on healthcare in developing countries. The findings will be valuable to organizations considering deploying AI, stakeholders (Ministry of Health and cybersecurity vendors), and researchers developing measures to enhance cybersecurity in the field.