AI Driven Multi Layered Security Framework for Autonomous Healthcare Governance and Intelligent Clinical Systems (International Journal of Engineering & Extended Technologies Research (IJEETR), 2024)
The increasing digitization of healthcare systems has significantly improved clinical outcomes and operational efficiency, but it has also introduced critical challenges related to data security, privacy, and governance. This paper proposes an AI-driven multi-layered security framework designed to ensure autonomous healthcare data governance while supporting intelligent clinical decision systems. The framework integrates advanced artificial intelligence techniques with layered cybersecurity mechanisms, including identity and access management, encryption, anomaly detection, and behavioral analytics. By leveraging machine learning and deep learning models, the system continuously monitors, analyzes, and adapts to evolving threats in real time. The multi-layered approach ensures defense-in-depth, reducing vulnerabilities across data, application, network, and infrastructure layers. Additionally, the framework supports intelligent clinical decision-making by enabling secure and reliable data access for predictive analytics and diagnostic systems. Cloud-native technologies and interoperable standards facilitate scalability, flexibility, and seamless data exchange across healthcare ecosystems. The proposed solution addresses key issues such as data breaches, insider threats, and fragmented data governance. Ultimately, this framework provides a comprehensive approach to secure, efficient, and intelligent healthcare systems, ensuring trust, compliance, and improved patient care outcomes.
Keywords: Artificial Intelligence; Healthcare Data Governance; Multi-Layered Security; Clinical Decision Systems; Machine Learning; Deep Learning; Cybersecurity; Data Privacy; Cloud Computing; Predictive Analytics