Autonomous Security Enforcement through AI-Driven Micro-Segmentation in Healthcare Clouds
Abstract
The extent and rate at which companies are moving to cloud-native architecture within the healthcare industry has increased the security risks regarding the confidentiality of data, police adherence and spread of threats in a lateral manner. The research paper provides an AI-driven autonomous security enforcement platform that is grounded on dynamic micro-segmentation of the healthcare clouds. It has a framework designed with real-time traffic analytics, behavioral aberration awareness, policy coordination and automatic containment to reduce attack surface and offer least-privilege access to distributed workloads. It introduces a multilayer system comprised of data acquisition agents, feature engineering modules, machine learning-based threat detectors, a policy decision engine and software-defined enforcement points which are present in virtual networks as well as containerized workloads.
This system relies on supervised and semi-supervised learning models to determine suspicious east-west traffic flows that occur frequently with ransomware flow, insider threat and misuse of credentials. Isolation of segmentation policy based on risk score, compliance requirements, such as HIPAA, and workload sensitivity quotas continue to occur in reinforcement learning processes. The implementation of automated policy generation and orchestration based on cloud-native API to ensure minimal overhead in terms of latency and adaptive scalability is carried out. The experimental evaluation of the simulated healthcare datasets ands hybrid cloud workloads demonstrate the existence of a significant reduction in the lateral attack propagation, the increased average time to containment and the network performance when compared to the fixed segmentation strategies.
The proposed framework presents the concepts of the zero-trust architecture through providing an autonomic and context-aware enforcement that does not need to be configured with a rule set manually. The research is significant in providing a smart and scalable way of securing the electronic health records and clinical applications in numerous clouds of healthcare systems
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
17715-17726 |
Published |
December 18, 2025 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dr Somasundaram Krishnan (2025). Autonomous Security Enforcement through AI-Driven Micro-Segmentation in Healthcare Clouds. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 17715-17726. https://doi.org/10.15662/IJAESIT.2025.0806005 |
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