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Intelligent Enterprise Data Platforms using Retrieval-Augmented AI and Secure Multi-Cloud Integration Frameworks

Dr Anisha Tandon

Department of Computer Science, Jagan Institute of Management Studies (JIMS), Rohini, New Delhi, India

Abstract

The rapid expansion of enterprise data has created a demand for intelligent platforms capable of integrating heterogeneous information sources, retrieving relevant knowledge, and supporting reliable organizational decision-making. Retrieval-Augmented Generation (RAG) offers an important approach by combining generative artificial intelligence with external knowledge retrieval, enabling AI systems to ground responses in enterprise-specific information rather than relying exclusively on static model knowledge. At the same time, organizations increasingly adopt multi-cloud strategies to improve scalability, availability, flexibility, and vendor diversification. However, integrating RAG with multiple cloud environments introduces challenges involving data fragmentation, security, interoperability, identity management, privacy, latency, governance, and operational complexity. This study proposes a conceptual framework for intelligent enterprise data platforms that combines retrieval-augmented AI with secure multi-cloud integration. The framework integrates distributed enterprise data sources, vector and conventional databases, retrieval services, foundation models, API gateways, cloud orchestration, zero-trust security, encryption, identity and access management, observability, and governance mechanisms. The proposed architecture enables enterprise users and applications to retrieve contextually relevant information and generate grounded outputs while maintaining controlled access to sensitive resources distributed across cloud environments. A design-oriented research methodology incorporating literature synthesis, architectural modelling, scenario-based evaluation, security analysis, and performance assessment is adopted. The study argues that effective enterprise RAG requires more than a capable language model; it requires trustworthy data pipelines, secure retrieval, contextual authorization, source traceability, and resilient multi-cloud infrastructure. The framework provides a foundation for developing scalable, secure, and knowledge-intensive enterprise applications

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 8 No. 5 (2025): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2025.0805009

Pages

14691-14701

Published

September 12, 2025
Copyright

Open Access

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

How to Cite

Dr Anisha Tandon (%2025). Intelligent Enterprise Data Platforms using Retrieval-Augmented AI and Secure Multi-Cloud Integration Frameworks. International Journal of Science, Research and Technology , Vol. 8 No. 5 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 14691-14701. https://doi.org/10.15662/IJSRAT.2025.0805009

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