AI-Driven Solutions for Securing Distributed Systems in Healthcare and Cloud
Keywords:
Artificial Intelligence (AI), Distributed Systems, Healthcare Security, Cloud Computing, Machine Learning, Deep Learning, Federated Learning, Cybersecurity, Blockchain, Data Privacy, HIPAA, GDPR, Real-Time Threat Detection, Cyber Attacks, Risk Mitigation, Security Frameworks.Abstract
The rapid adoption of cloud computing and distributed systems in healthcare has introduced significant opportunities for enhanced operational efficiency, scalability, and patient care. However, these advancements also expose critical vulnerabilities, making healthcare systems a prime target for cyber threats. Artificial Intelligence (AI) has emerged as a powerful tool for strengthening the security of distributed healthcare systems, offering innovative solutions for real-time threat detection, risk mitigation, and adaptive defense strategies. This paper explores AI-driven solutions designed to secure distributed systems within the healthcare and cloud environments. We discuss the role of machine learning, deep learning, and federated learning in detecting and responding to emerging cyber threats while ensuring the privacy and confidentiality of sensitive healthcare data. Furthermore, the paper delves into the integration of AI with blockchain for enhancing data integrity and access control, and examines its potential to comply with strict regulatory frameworks such as HIPAA and GDPR. Through a comprehensive review of current AI applications, security frameworks, and case studies, this paper outlines the future prospects and challenges of AI in securing distributed healthcare systems. Our findings highlight the transformative impact AI can have on safeguarding patient data, enhancing cybersecurity protocols, and optimizing healthcare system resilience against cyber-attacks.
