AI-Driven Continuous Compliance and Intelligent Risk Management in Multi-Cloud Healthcare Ecosystems: A Review

Abstract

The growing use of multi-cloud architecture in the medical sector has helped organizations to improve their scalability, interoperability, and data-driven decisions. Nonetheless, this distributed digital space is also accompanied by increased challenges in complying with rigorous regulatory requirements and reducing the emerging cybersecurity threats. This review analyzes how AI-driven continuous compliance and intelligent risk management can be applied to the governance of multi-cloud healthcare ecosystems. An extensive review is carried out of risk management systems in which predictive modeling, multi-cloud threat intelligence and dynamic vulnerability assessment are used to anticipate security breaches and mitigate attack surfaces. The major finding of the review is an understanding of the privacy-preserving methods, such as federated learning, differential privacy, and secure multiparty computation, and how they play a crucial role in safeguarding sensitive patient data throughout distributed analytics. Additionally, explainable AI helps in promoting transparency, which allows healthcare administrators and auditors to interpret system decisions and promote responsible governance. Irrespective of these developments, there are still issues of standardizing compliance rules, interoperability with heterogeneous cloud platforms, as well as the problem of model drift. Altogether, this review highlights the possibility of AI to enhance the security, compliance, and resilience of the multi-cloud healthcare setting.