Next Generation Machine Learning Enabled AI Cloud Infrastructure for Healthcare Governance, Security and Risk Management
DOI:
https://doi.org/10.15680/IJCTECE.2024.0706022Keywords:
AI cloud infrastructure, machine learning, healthcare governance, cybersecurity, risk management, compliance, data privacy, cloud security, predictive analytics, access controlAbstract
The healthcare sector is undergoing a digital transformation driven by the integration of cloud computing and artificial intelligence (AI). This transformation, while improving patient outcomes and operational efficiency, introduces complex challenges related to governance, security, and risk management. This research investigates the design and implementation of next-generation AI-enabled cloud infrastructures tailored to healthcare governance, emphasizing machine learning (ML) approaches that support data integrity, access control, threat detection, and regulatory compliance. The study proposes an integrated framework that leverages ML algorithms to monitor, predict, and mitigate security incidents, ensuring that sensitive patient data remains protected across distributed cloud environments. The framework also addresses governance by providing transparent audit trails, automated policy enforcement, and adaptive risk assessment. To validate the proposed model, a mixed-method approach combining qualitative expert interviews and quantitative performance analysis is adopted. The research outcomes are expected to demonstrate improved risk detection rates, reduced response times, and enhanced compliance effectiveness compared to traditional cloud security approaches. Ultimately, the study aims to offer healthcare organizations a scalable, AI-driven cloud infrastructure that balances innovation with robust governance and risk management practices
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