Explainable AI Security Models for Enterprise Healthcare Systems in Hybrid Cloud Environments

Abstract

Explainable Artificial Intelligence (XAI) is rapidly becoming essential in securing enterprise healthcare systems, particularly in hybrid cloud environments where sensitive patient data is processed and stored across private and public infrastructures. Traditional machine learning and AI-driven security solutions often lack interpretability, making it difficult for administrators, auditors, and regulators to understand or trust the decisions made by automated systems that protect critical healthcare assets. This research investigates XAI security models that enhance transparency, resilience, and trustworthiness without compromising performance or compliance with healthcare regulations such as HIPAA and GDPR. We examine how explainability frameworks—such as model-agnostic explanation tools, attention mechanisms, and rule-based interpretable algorithms—can be integrated with real-time threat detection, access control, anomaly identification, and compliance monitoring in hybrid cloud deployments. Through a mixed-method research design that includes case studies, simulation environments, and prototype implementation, we assess model effectiveness across key metrics (accuracy, interpretability, latency, threat coverage, user trust). Findings suggest that XAI models improve incident response and stakeholder confidence while uncovering potential vulnerabilities that traditional “black-box” systems may overlook. The study contributes to both academic understanding and practical frameworks for deploying explainable, secure AI in healthcare ecosystems.