Protecting Healthcare Data: AI-Powered Strategies for Securing Distributed Systems

Abstract

The exponential growth of digital healthcare systems has amplified concerns about safeguarding sensitive patient data across distributed infrastructures. This paper presents a comprehensive exploration of AI-powered strategies for securing healthcare data in distributed systems, focusing on critical applications such as electronic health records (EHRs), IoT-based medical devices, and telemedicine platforms. Leveraging advanced machine learning algorithms, federated learning models, and real-time anomaly detection, the study investigates their role in fortifying data privacy and ensuring regulatory compliance under frameworks like HIPAA and GDPR. Through a detailed evaluation of AI techniques in encryption, access management, and intrusion detection, the research demonstrates their efficacy in mitigating threats such as ransomware attacks, unauthorized data access, and system breaches. Experimental results highlight the scalability, accuracy, and adaptability of these strategies in addressing the unique challenges of distributed healthcare environments. The paper concludes with a roadmap for future research, emphasizing ethical AI deployment and interoperability standards.