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Self-Supervised AI for Intelligent Enterprise Knowledge Discovery in Multi-Tenant Cloud Environments

Dr. Ravie Chandren Muniyandi

Associate Professor, Department of Computing (CCI), College of Computing and Informatics, Universiti Tenaga Nasional, Malaysia

Abstract

Multi-tenant cloud environments enable multiple organizations, departments, and users to share computing infrastructure while maintaining logical separation of applications and data. Although this architecture improves resource utilization, scalability, and cost efficiency, the enormous volume and diversity of enterprise data generated across tenants makes knowledge discovery increasingly difficult. Conventional knowledge-discovery approaches often depend on manually labeled datasets, predefined rules, or isolated analysis pipelines, limiting their ability to identify hidden relationships and emerging patterns. Self-Supervised Artificial Intelligence (SSAI) provides a promising solution by learning meaningful representations from large quantities of unlabeled enterprise data through automatically generated learning objectives. This paper investigates the application of self-supervised AI for intelligent enterprise knowledge discovery in multi-tenant cloud environments. The proposed approach combines representation learning, cloud data integration, tenant-aware processing, semantic analysis, anomaly detection, clustering, and knowledge-graph construction. Enterprise artifacts such as documents, logs, application metadata, service interactions, operational records, and usage patterns are transformed into contextual representations without requiring extensive manual labeling. The methodology evaluates the proposed framework according to knowledge-discovery accuracy, representation quality, anomaly-detection capability, clustering effectiveness, scalability, processing efficiency, and tenant-isolation requirements. The study also examines important challenges involving data privacy, security, noisy data, computational cost, model bias, explainability, and cross-tenant information leakage. The research demonstrates that self-supervised AI can provide an adaptive foundation for discovering previously hidden enterprise knowledge while preserving the operational and security requirements of multi-tenant cloud computing.

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 11 No. 4 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2026.1104004

Pages

21589-21601

Published

August 13, 2026
Copyright

Open Access

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

How to Cite

Dr. Ravie Chandren Muniyandi (%2026). Self-Supervised AI for Intelligent Enterprise Knowledge Discovery in Multi-Tenant Cloud Environments. International Journal of Emerging Trends in Engineering and Management Research , Vol. 11 No. 4 (2026): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 21589-21601. https://doi.org/10.15662/ijetemr.2026.1104004

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