Explainable AI Framework for Private Cloud Cybersecurity Analytics and Zero-Trust Infrastructure Protection

Sadia Sharmin; Sadia Sharmin

doi:10.15662/IJSRAT.2026.0904004

Explainable AI Framework for Private Cloud Cybersecurity Analytics and Zero-Trust Infrastructure Protection

Sadia Sharmin

Independent Researcher, Los Angeles, United States

Abstract

The rapid adoption of private cloud infrastructures has transformed enterprise computing by enabling organizations to achieve scalability, flexibility, and improved control over sensitive digital resources. However, increasing cyber threats, sophisticated attack techniques, and complex distributed environments have created significant challenges for traditional cybersecurity approaches. Artificial Intelligence (AI)-driven security analytics provides advanced capabilities for detecting anomalies, predicting threats, and automating incident responses, but many AI models operate as opaque systems that limit transparency, trust, and regulatory compliance. This research proposes an Explainable Artificial Intelligence (XAI) framework for private cloud cybersecurity analytics integrated with Zero-Trust Infrastructure Protection principles. The framework combines machine learning-based threat detection, explainability mechanisms, continuous authentication, behavioral analysis, and adaptive access control to improve security decision-making. The proposed approach emphasizes transparency by generating understandable explanations for security alerts, risk assessments, and automated responses. A research methodology based on framework design, data-driven experimentation, performance evaluation, and comparative analysis is presented to assess effectiveness. The framework aims to enhance threat intelligence, reduce false positives, support security analysts, and strengthen organizational confidence in AI-enabled cybersecurity systems. By integrating explainability with Zero-Trust security models, the proposed solution provides a reliable approach for protecting private cloud environments against evolving cyber threats

Article Information

Journal	International Journal of Science, Research and Technology
Volume (Issue)	Vol. 9 No. 4 (2026): International Journal of Science, Research and Technology (IJSRAT)
DOI	https://doi.org/10.15662/IJSRAT.2026.0904004
Pages	1330-1338
Published	July 20, 2026
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Sadia Sharmin (%2026). Explainable AI Framework for Private Cloud Cybersecurity Analytics and Zero-Trust Infrastructure Protection. International Journal of Science, Research and Technology , Vol. 9 No. 4 (2026): International Journal of Science, Research and Technology (IJSRAT) , pp. 1330-1338. https://doi.org/10.15662/IJSRAT.2026.0904004

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