Sarcouncil Journal of Multidisciplinary

Abstract: Healthcare organizations face unprecedented cybersecurity challenges as Electronic Health Records systems become increasingly targeted by sophisticated cyber threats, including ransomware attacks and advanced persistent threats that exploit vulnerabilities specific to medical environments. Traditional signature-based security approaches prove inadequate against evolving attack vectors, necessitating the development of proactive, behavior-based defense mechanisms that can identify previously unknown threats while maintaining strict compliance with healthcare regulations. This article presents a comprehensive artificial intelligence-driven cybersecurity framework that leverages machine learning algorithms and large language models to enhance threat detection capabilities in cloud-based EHR environments through real-time analysis of network traffic, user behavior patterns, and system activities. The article integrates privacy-preserving techniques such as federated learning and differential privacy to ensure compliance with HIPAA Privacy and Security Rules while enabling advanced threat detection that moves beyond reactive paradigms toward predictive security measures. Implementation validation through simulated ransomware scenarios demonstrates the framework's effectiveness in identifying malicious activities within healthcare-appropriate timeframes while maintaining minimal false positive rates that could disrupt critical patient care operations. The article addresses fundamental challenges in balancing enhanced cybersecurity capabilities with regulatory compliance requirements, providing healthcare organizations with practical guidance for implementing AI-driven security solutions that protect sensitive patient data without compromising operational efficiency. Performance evaluation reveals competitive advantages over traditional security approaches, particularly in detecting zero-day threats and insider attacks that conventional systems might overlook. The article contributes to the growing body of knowledge surrounding AI applications in healthcare cybersecurity while establishing a foundation for future developments in intelligent, adaptive security frameworks specifically designed for the unique requirements of healthcare environments.