Data Security in Multi-Tenant Clusters

This article presents a comprehensive analysis of the set of threats that are characteristic of heterogeneous Kubernetes deployments. The work aims to systematize and examine these threats, as well as to develop an integrated security model suitable for practical implementation. The methodological foundation consisted of a rigorous literature review encompassing both academic papers and engineering reports from major cloud providers. Special attention was given to publications on container isolation, inter-pod network policy, secrets management, and data encryption protocols. Based on this analysis, a multi-layer threat map is presented, detailing the attack vectors at each layer. The proposed protective measures are integrated into a unified DevSecOps lifecycle framework and can be automated within CI/CD pipelines. The conclusions drawn and the model developed are intended for security engineers, DevOps teams, and cloud platform architects who need to design and maintain multi-tenant Kubernetes clusters with a guaranteed level of data protection.

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Publication Details

Journal
The American Journal of Engineering And Technology
Published
2025-08-22
DOI
https://doi.org/10.37547/tajet/volume07issue08-22
Primary Topic
Privacy-Preserving Technologies in Data
Type
article
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article

Data Security in Multi-Tenant Clusters

Megha Aggarwal
The American Journal of Engineering And Technology
Privacy-Preserving Technologies in Data
article

Data Security in Multi-Tenant Clusters

Megha Aggarwal
article en

Abstract

This article presents a comprehensive analysis of the set of threats that are characteristic of heterogeneous Kubernetes deployments. The work aims to systematize and examine these threats, as well as to develop an integrated security model suitable for practical implementation. The methodological foundation consisted of a rigorous literature review encompassing both academic papers and engineering reports from major cloud providers. Special attention was given to publications on container isolation, inter-pod network policy, secrets management, and data encryption protocols. Based on this analysis, a multi-layer threat map is presented, detailing the attack vectors at each layer. The proposed protective measures are integrated into a unified DevSecOps lifecycle framework and can be automated within CI/CD pipelines. The conclusions drawn and the model developed are intended for security engineers, DevOps teams, and cloud platform architects who need to design and maintain multi-tenant Kubernetes clusters with a guaranteed level of data protection.

The American Journal of Engineering And TechnologyVol. 7(08)
Amazon (United States) (US)
Openalex Percentile: Top 89%
Privacy-Preserving Technologies in Data
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Data Security in Multi-Tenant Clusters — Megha Aggarwal · The American Journal of Engineering And Technology (2025) | TGRS Research Map | TGRS