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AI-Driven Enterprise Knowledge Intelligence through Retrieval-Augmented Generation and Hybrid Cloud Cybersecurity Architecture

Hari Sangeetham

Senior Solution Specialist at Deloitte, Florida, United States

Abstract

Enterprise organizations increasingly depend on distributed knowledge resources, cloud applications, cybersecurity intelligence, operational databases, technical documents, policies, and business records to support decision-making. However, conventional knowledge-management systems often struggle to provide context-aware, timely, and secure access to heterogeneous enterprise information. Retrieval-Augmented Generation (RAG) offers a promising approach by combining information retrieval with large language models (LLMs), enabling generated responses to be grounded in external knowledge rather than relying exclusively on model parameters. This research proposes an AI-driven enterprise knowledge intelligence architecture that integrates RAG with hybrid cloud cybersecurity mechanisms. The proposed framework combines vector retrieval, keyword search, enterprise knowledge graphs, document repositories, identity-aware access control, encryption, security monitoring, zero-trust principles, and cloud-native AI services. A security-aware retrieval layer ensures that users receive information according to organizational permissions and data-classification policies. The methodology follows a design-oriented research approach involving requirements analysis, architecture modeling, retrieval pipeline development, security-policy integration, prototype evaluation, and comparative performance assessment. The framework evaluates retrieval accuracy, response relevance, latency, security, hallucination reduction, scalability, and explainability. By connecting enterprise knowledge repositories with controlled retrieval and hybrid cloud security mechanisms, the proposed architecture aims to improve organizational knowledge discovery while reducing unauthorized data exposure. The study demonstrates how RAG and cybersecurity can be treated as integrated architectural capabilities rather than independent components of enterprise AI.

Article Information

Journal

International Journal of Advanced Engineering Science and Information Technology (IJAESIT)

Volume (Issue)

Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT)

DOI

https://doi.org/10.15662/IJAESIT.2025.0806017

Pages

17846-17857

Published

November 24, 2025
Copyright

Open Access

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

How to Cite

Hari Sangeetham (2025). AI-Driven Enterprise Knowledge Intelligence through Retrieval-Augmented Generation and Hybrid Cloud Cybersecurity Architecture. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 8 No. 6 (2025): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 17846-17857. https://doi.org/10.15662/IJAESIT.2025.0806017

References

1. Gopinathan, V. R., & Mali, R. K. (2022). Building cognitive technology frameworks through artificial intelligence SAP cloud automation and enterprise intelligence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7152-7162.
2. Nisar, K. (2025). Quantifying the cost and risk of enterprise LLM adaptation strategies. International Journal of Research Publications in Engineering, Technology and Management, 8(3), 12162–12169.
3. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
4. Rajula, A. (2024). Drift-aligned personalization for privacy-preserving edge health monitoring. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(5), 8895–8906.
5. Bellundagi, M. (2023). Integrating Machine Learning with Business Rule Management Systems for Adaptive Enterprise. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8023-8039.
6. Wadhwa, R. (2024). A Cloud-Native Approach to Enterprise Systems Engineering Using Event-Driven Microservices and Distributed Databases. Journal ID, 2563, 4512.
7. Rahman, M. S., Siddiqui, M. I. H., Rashid, S. U., Kabir, A. A., Mahmud, F. U., & Shammah, R. S. (2024). Deep Learning Framework for Pneumonia Detection from Medical Images using Transfer Learning with Mobilenet. Journal of Adaptive Learning Technologies, 1(11), 12-22.
8. Pareek, C. S. (2022). Never Trust, Always Verify: Zero Trust Security Testing Framework. vol, 1, 1-5.
9. Pasumarthi, H. (2024). AI-driven forecasting and optimization in distributed systems: Lessons from retail, lending, and healthcare platforms. International Journal of Research and Applied Innovations, 7(3), 10786-10790.
10. Juvvadi, R. R. (2017). From record-to-report to insight-to-action: Re-engineering the finance operating model for the cloud ERP era. International Research Journal of Innovative Engineering (IRJIE), 1(2), 371–376.
11. Tyagi, N. (2025). Explainability-Driven Differentiation: Responsible AI as a Trust Catalyst in Digital Banking Ecosystems. International Journal of Research and Applied Innovations, 8(3), 13043-13052.
12. Kanji, R. K. (2021). Real-Time Big Data Processing with Edge Computing. European Journal of Advances in Engineering and Technology, 8(11), 152-155.
13. Bajarang Bhagwat, V. (2023). Optimizing payroll to general ledger reconciliation: Identifying discrepancies and enhancing financial accuracy. Journal of Advance and Future Research, 1(4).
14. Singh, I. K. (2024). DevSecOps for enterprise ontology platforms: Continuous validation, security, and compliance of semantic knowledge systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(2), 10359-10369.
15. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
16. Meesala, L. K. AI-Driven Cyber Defense: A Hybrid Deep Learning Framework for Real-Time Threat Prediction and Response. International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN, 2395-1990.
17. Onteddu, A. R., Bandhela, R. R., & Kundavaram, R. R. (2024). Enhancing E-Commerce Product Recommendations through Data Engineering and Machine Learning. Economic Sciences, 20(1), 171-183.
18. Challa, R. (2024, March). Secure hyperconverged infrastructure for government-scale digital transformation: A technical blueprint. International Journal of Research and Applied Innovations, 7(2), 10504–10509.
19. Vollem, S. (2021). Governance Models for Microservice Architectures in Regulated Enterprise Environments. J Artif Intell Mach Learn & Data Sci, 3(3), 3343-3349.
20. Karnam, V. S. (2024). Adaptive and federated test automation using AI in distributed multi-cloud and hybrid infrastructure environments. International Journal of Future Innovative Science and Technology (IJFIST), 7(4), 111–121.
21. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
22. Narapareddy, V. S. R., & Yerramilli, S. K. (2024). Zero-TouchEmployee UX. Universal Library of Engineering Technology., 01 (02), 55–63.
23. Raj, A. A., & Sugumar, R. (2022, October). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) (pp. 1-6). IEEE.
24. Vasa, M. R. (2024). AI-Augmented Semantic Layer for Governed Fund Data Consolidation and Lakehouse Ingestion. International Journal of Future Innovative Science and Technology (IJFIST), 7(1), 12056.
25. Gummadi, V. P. K. (2021). Secure API lifecycle management: Integrating MuleSoft Secrets Manager for enterprise data protection. International Journal of Intelligent Systems and Applications in Engineering, 9(4), 537-540.
26. Gujarathi, M. (2023). Active-active data architecture for high-availability enterprise systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(1), 5976–5986.
27. Awopejo, T. E., Adigun, P. O., & Oyekanmi, T. T. (2023). Emerging applications of artificial intelligence and machine learning in modern earthquake science. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8142–8154.
28. Reddy, B. P. (2021). Optimizing smart grid performance using Machine Learning: A Data-Driven Approach to Energy Efficiency. J. Electrical Systems, 17(4), 149-156.
29. Anand, L. (2022). Integrating Kubernetes Microservices with Privileged Access Security and Real-Time Fraud Detection for Modern Enterprise Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7453-7461.
30. Seetala, S. R. (2020). Architecting accountability: A layered enterprise data governance model for regulated industries. European Journal of Advances in Engineering and Technology, 7(1), 95-103.
31. Pokala, H. K. (2022). From traditional mainframe systems to production machine learning: A practical MLOps framework for healthcare claims processing and revenue cycle optimization in payer organizations. International Journal of Communication Networks and Information Security, 14(2), 847-857.
32. Meesala, L. K. AI-Driven Cyber Defense: A Hybrid Deep Learning Framework for Real-Time Threat Prediction and Response. International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN, 2395-1990.
33. Kale, P. (2023). A Federated Learning Approach to Distributed DevOps Automation in Platform Engineering Architectures. International Journal of AI, BigData, Computational and Management Studies, 4(4), 200-208.
34. Soundappan, S. J. (2022). Integrated Risk Governance Framework for Financial Compliance Supply Chain Resilience and Enterprise Data Management. International Journal of Computer Technology and Electronics Communication, 5(6), 16254-16263.
35. Raja, G. V. (2022). AI-Driven Enterprise Architecture Integrating Zero-Trust Security for Secure Scalable and Compliant Cloud-Native Platforms. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12178.
36. Sivakumer, D. (2024). The role of work models in influencing organizational performance and employee wellbeing: A comparative study on full-time, remote, and hybrid paradigms. International Journal of Advanced Engineering Science and Information Technology, 7(4), 14496–14510.
37. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.