AI-Based Cybersecurity Risk Management for Cloud-Based Pharmacy Information Systems
Keywords:
Artificial Intelligence, Cybersecurity Risk Management, Cloud Computing, Digital Pharmacy, Pharmacy Information Systems, Cloud Security, Risk Assessment, Healthcare Data, Machine Learning, Data Privacy.Abstract
Cloud computing has become an important component of modern pharmacy information systems by providing scalable storage, remote accessibility, centralized data management, and integration with healthcare applications. Cloud-based pharmacy platforms can support electronic prescriptions, patient medication records, inventory management, billing, pharmaceutical supply-chain coordination, and online pharmacy services. However, the migration of pharmacy information to cloud environments creates cybersecurity risks involving unauthorized access, data breaches, ransomware, insecure interfaces, account compromise, misconfiguration, insider threats, and third-party service dependencies. Traditional risk-management approaches may not be sufficient for continuously changing cloud environments because threats, user behavior, applications, and infrastructure can change rapidly. Artificial Intelligence (AI) provides an opportunity to strengthen cybersecurity risk management through automated risk identification, behavioral analysis, anomaly detection, threat prediction, vulnerability prioritization, and continuous security assessment. This research examines the role of AI in managing cybersecurity risks in cloud-based pharmacy information systems. Unlike a conventional threat-detection approach, the study focuses on the complete risk-management cycle, including risk identification, assessment, prioritization, mitigation, monitoring, and reassessment. A conceptual methodology is used to analyze the relationship between cloud characteristics, pharmacy information assets, cyber risks, AI-based assessment, and organizational risk decisions. The study finds that AI can support more dynamic and proactive risk management by identifying unusual activities and prioritizing high-impact security risks. However, AI adoption itself introduces challenges related to data privacy, model reliability, explainability, cloud dependency, false alarms, and adversarial manipulation. The study concludes that AI-supported risk management should complement established cloud-security controls and organizational governance to create secure, resilient, and trustworthy pharmacy information systems.
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