Articles Open Access
Secure Enterprise Knowledge Discovery in Private Cloud Environments Using Retrieval-Augmented Generation and Semantic Search
Department of Computer Science, Federal University of Technology Akure, Akure, Nigeria
Abstract
The increasing volume of enterprise information stored across private cloud environments creates significant challenges for secure knowledge discovery, information retrieval, and organizational decision-making. Traditional keyword-based search systems often struggle to identify contextually relevant information from heterogeneous enterprise repositories containing documents, technical reports, operational records, policies, and structured data. This research proposes a secure enterprise knowledge discovery framework that integrates retrieval-augmented generation (RAG), semantic search, and privacy-aware access control within private cloud infrastructure. The framework combines document ingestion, text preprocessing, embedding generation, vector indexing, hybrid retrieval, and large language model-based response generation to deliver contextually relevant and evidence-grounded answers. Semantic search identifies information according to conceptual similarity, while keyword retrieval improves the identification of exact terminology, technical identifiers, and domain-specific expressions. Security mechanisms, including identity-based authorization, document-level access filtering, encryption, audit logging, and secure model integration, protect confidential enterprise knowledge throughout the retrieval and generation lifecycle. The proposed methodology evaluates retrieval relevance, answer faithfulness, response accuracy, query latency, and unauthorized information exposure using representative enterprise knowledge tasks. Comparative experiments against keyword-based and semantic-only retrieval approaches are designed to assess the effectiveness of the integrated framework. The research aims to improve enterprise knowledge accessibility, reduce information discovery time, and support trustworthy AI-assisted decision-making while maintaining confidentiality, governance, and operational control in private cloud environments.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 9 |
| Number | 5 |
| Pages | 17596-17605 |
| Published | 2025-10-13 |
| Issue | Vol. 9 No. 5 (2025): International Research Journal of Innovative Engineering (IRJIE) |
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35. Jinna, K. R., & Bandari, U. (2023). Progressive delivery for machine-learning inference services: A comparative study of blue-green, canary and shadow strategies with model-aware gates on enterprise container platforms. International Journal of Research Publications in Engineering, Technology and Management, 6(4), 9092–9101.
2. Neela, S. (2023). Resilient middleware ecosystems: Integrating fault tolerance, recovery-oriented design, and observability for real-time enterprise integration. International Journal of Emerging Research in Engineering and Technology, 4(1), 186–189. https://doi.org/10.63282/3050-922X.IJERET-V4I1P120
3. Tarigoppula, S. (2024). Autonomous cloud operations: Integrating AIOps with AWS infrastructure for predictive incident prevention. International Journal of Engineering & Extended Technologies Research, 6(2), 7956–7967.
4. Lingala, B. (2025). Transforming Enterprise Transaction Data into Intelligent Decision Systems. International Journal of Computer Technology and Electronics Communication, 8(5), 11488-11494.
5. 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.
6. Gajjela, H., & Kari, M. (2024). Secure Multi-Tenant AI Architecture Enhances Enterprise AI Adoption Through Tenant Trust Using PLS-SEM in India’s Salesforce Cloud Industry. International Journal of Engineering Science & Humanities, 14(2), 253-271.
7. Parupalli, A. (2024). Business-oriented employee performance assessment via machine learning in ERP systems. TIJER–International Research Journal, 11(11), a185–a192.
8. Polamarasetty, V. K. (2021). Modernizing SAP sales and distribution systems through ABAP-based enterprise solutions. International Journal of Research and Applied Innovations, 4(2), 4925-4930.
9. Seknametla, P. R., & Sunkara, R. (2024). Threat Modeling Integration in DevSecOps Pipelines: Early-Stage Security Risk Identification Using Shift-Left Approaches. International Journal of Emerging Research in Engineering and Technology, 5(1), 126-133.
10. Gunasekaran, R. M. N. (2024). From Data to Decisions: How Quality Drives Machine Learning Success. The Eastasouth Journal of Information System and Computer Science, 2(01), 120-130.
11. Karuturi, Y. (2023). Energy-aware capacity governance for large-scale SAP HANA deployments. International Journal of Research and Applied Innovations, 6(3), 6000–6009.
12. Ahuja, D. (2025). DevOps and Ethical AI: Ensuring Responsible Deployment. Journal Of Multidisciplinary, 5(6), 1-14.
13. Chinnam, N. B. (2022). Engineering real-time supply chain visibility and ETA intelligence platforms for large-scale retail distribution networks. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7918–7922.
14. Aragani, V. M., Maroju, P. K., & Mudunuri, L. N. R. (2024, December). Implementing Non-functional Production Regression Testing with Kubernetes. In International Conference on Information and Communication Technology for Competitive Strategies (pp. 231-244). Singapore: Springer Nature Singapore.
15. Kalabhavi, V. (2024). Promotional spend analytics using AI for PFME allocation and coupon redemption optimization. International Journal of Emerging Trends in Engineering and Management Research, 9(2), 15394–15406.
16. Reddy, K. V. (2021). UVM methodology for industry-specific applications using AI in hardware verification. International Journal of Science, Research and Technology, 4(3), 5731–5744.
17. Chandran, A. S. (2024). Engineering billion-query search systems: Lessons from large-scale metadata platforms. International Journal of Research and Applied Innovations, 7(2), 10510–10519.
18. Uddandarao, D. P., Mahajan, A., & Krishnamsetty, P. (2024). Double machine learning for downstream causal measurement: A hybrid econometric-ML framework for feature valuation. International Journal of Computational and Experimental Science and Engineering, 10(4), 4477–4485.
19. Padmanabham, S. (2025). Security and Compliance in Integration Architectures: A Framework for Modern Enterprises. International Journal of Computing and Engineering, 7(16), 45-55.
20. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285-2287.
21. Sugumar, R. (2022). Explainable Deep Learning Framework for Financial Fraud Detection and Intelligent Risk Analysis. International Journal of Emerging Trends in Engineering and Management Research, 7(5), 12528.
22. Mysula, S. (2024). Event-driven integration architecture using publication methods for real-time DealCloud business operations & workflows. International Journal of Research and Applied Innovations, 7(4), 11233–11239.
23. Lenin, S. T., & Venkatasalam, K. (2025). Effective classification of heart disease using convolutional neural networks. Circuits, Systems, and Signal Processing, 44(2), 911-935.
24. Rengarajan, A. (2022). Developing AI Powered Enterprise Integration Platforms Through Cloud Native Observability and Automated Governance. International Journal of Science, Research and Technology, 5(6), 8970-8977.
25. Kundavaram, R. R., & Onteddu, A. R. (2023). Enhancing automotive performance and real-time data analytics through cloud computing. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 1382–1392.
26. Shaik, N. (2022). Beyond vendor lock-in: Building intelligent multi-cloud Kubernetes orchestration. International Research Journal of Innovative Engineering, 6(2), 9985–9996.
27. Bitragunta, S. L. V. (2024). Intelligent power feedback control for motor-generator pairs: A machine learning-based approach. International Journal of Leading Research Publication, 5(12), 1–9.
28. Kopparthi, G. S. (2023). Trustworthy artificial intelligence for high-stakes decision-making: Explainability, human oversight, and system reliability. International Research Journal of Innovative Engineering, 7(4), 12847–12860.
29. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
30. Gopinathan, V. R. (2022). Advanced Predictive Decision Intelligence Using Machine Learning for Strategic Enterprise Management Platforms. International Journal of Research and Applied Innovations, 5(6), 8162-8169.
31. Singh, M. P. (2024). Building Cyber-Resilient Cloud Infrastructure Integrating Zero Trust, Identity Security, and Continuous Threat Monitoring. IACSE-International Journal of Cyber Security (IACSE-IJCS), 5(2), 7-23.
32. Chinnam, N. B. (2022). Engineering real-time supply chain visibility and ETA intelligence platforms for large-scale retail distribution networks. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7918–7922.
33. Gadige, C. D. (2023). Scalable CRM automation for high-volume customer onboarding: An event-driven Salesforce architecture. International Journal of Emerging Trends in Engineering and Management Research, 8(6), 14762–14772.
34. Srikanth, D. V. (2025). AI-powered entrepreneurial ecosystems: a computational model for sustainable startup growth in green tech sectors. *Int. J. Environ. Sci, 1068-1077.
35. Jinna, K. R., & Bandari, U. (2023). Progressive delivery for machine-learning inference services: A comparative study of blue-green, canary and shadow strategies with model-aware gates on enterprise container platforms. International Journal of Research Publications in Engineering, Technology and Management, 6(4), 9092–9101.
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