A Multi-Layered Zero-Trust–Driven Cybersecurity Framework Integrating Deep Learning and Automated Compliance for Heterogeneous Enterprise Clouds
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V6I4P102Keywords:
Zero Trust Architecture, Deep Learning, Cloud Security, Automated Compliance, Enterprise Clouds, Kubernetes Security, Policy EnforcementAbstract
Modern enterprises increasingly operate across heterogeneous computing environments encompassing public cloud platforms (e.g., AWS, Azure, and GCP), private clouds, on-premises data centers, Kubernetes clusters, and edge infrastructures. While this architectural diversity enables scalability and agility, it also significantly expands the attack surface and complicates the enforcement of consistent security and regulatory controls. Traditional perimeter-based security models and static compliance mechanisms are insufficient to address the dynamic, distributed, and identity-centric nature of these environments. This paper proposes a multi-layered Zero Trust–driven cybersecurity framework that unifies identity-centric access control, continuous verification, and adaptive policy enforcement across heterogeneous enterprise clouds. The framework integrates deep learning–based behavioral analytics to enable real-time threat detection, risk scoring, and anomaly identification across users, workloads, and services. In parallel, an automated compliance engine implements compliance-as-code principles, continuously validating security posture against regulatory requirements and dynamically enforcing policies across multi-cloud and containerized environments. The proposed architecture is evaluated using a representative enterprise multi-cloud deployment, incorporating public cloud services, Kubernetes workloads, and on-premises resources. Experimental results demonstrate improved threat detection accuracy, reduced mean time to detection and response, and higher compliance adherence compared with conventional rule-based and perimeter-centric approaches, while maintaining acceptable system overhead. The key contribution of this work lies in delivering a unified, scalable, and intelligent Zero Trust framework that tightly couples deep learning–driven security analytics with automated compliance enforcement, providing a practical and extensible foundation for securing modern heterogeneous enterprise cloud ecosystems
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