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Machine Learning Driven Zero Trust Security Framework for Cloud-Native Enterprise Platforms and Digital Ecosystems

Michael Wooldridge

Abstract

The increasing adoption of cloud-native architectures and digital ecosystems has amplified enterprise exposure to sophisticated cybersecurity threats. Traditional perimeter-based security models are inadequate for modern cloud environments, which require continuous authentication, dynamic access controls, and real-time threat detection. Zero Trust Security (ZTS) has emerged as a strategic approach to mitigate these risks by enforcing the principle of “never trust, always verify” across users, devices, and network components.

 

This research proposes a machine learning-driven Zero Trust Security framework designed for cloud-native enterprise platforms and digital ecosystems. The framework integrates machine learning algorithms for behavioral analytics, anomaly detection, and predictive threat mitigation, providing adaptive security controls across multi-cloud and hybrid infrastructures. Identity and access management, continuous verification, and micro-segmentation are core components, ensuring granular control of data, applications, and services.

 

By leveraging AI and ML, the framework dynamically assesses risk, detects abnormal behavior in real time, and automates response mechanisms to prevent breaches or unauthorized access. The architecture supports scalability, resilience, and seamless integration with cloud-native applications while maintaining compliance with enterprise security standards. This study provides a comprehensive methodology for designing, implementing, and evaluating a ML-driven Zero Trust framework to enhance cybersecurity, operational efficiency, and trustworthiness in enterprise digital ecosystems.

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 7 No. 5 (2024): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2024.0705004

Pages

12796-12804

Published

October 7, 2024
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Michael Wooldridge (%2024). Machine Learning Driven Zero Trust Security Framework for Cloud-Native Enterprise Platforms and Digital Ecosystems. International Journal of Science, Research and Technology , Vol. 7 No. 5 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 12796-12804. https://doi.org/10.15662/IJSRAT.2024.0705004

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