Developing Secure Enterprise Platforms through Generative AI Autonomous Operations and Intelligent Cloud Infrastructure
DOI:
https://doi.org/10.15662/IJEETR.2025.0705017Keywords:
Generative AI, Autonomous Operations, Intelligent Cloud Infrastructure, Secure Enterprise Platforms, Artificial Intelligence, Cloud Computing, Digital Transformation, Intelligent Automation, Cloud-Native Architecture, Cybersecurity, Zero Trust Security, DevOps, Machine Learning, AI Governance, Infrastructure as Code, Microservices, Continuous Monitoring, Enterprise Architecture, Predictive Analytics, Secure Software DevelopmentAbstract
The rapid evolution of digital technologies has created a strong demand for secure, intelligent, and scalable enterprise platforms capable of supporting modern business operations. Generative Artificial Intelligence (Generative AI), autonomous operations, and intelligent cloud infrastructure have emerged as transformative technologies that enable organizations to enhance productivity, automate complex processes, and improve operational resilience. Generative AI provides advanced capabilities for content generation, software development assistance, predictive analysis, and intelligent decision support, while autonomous operations enable self-monitoring, self-optimization, and automated problem resolution across enterprise environments. Intelligent cloud infrastructure delivers the scalability, flexibility, and computational power required for deploying AI-driven applications through cloud-native architectures, automated resource management, and distributed computing models. This study explores the development of secure enterprise platforms by integrating Generative AI, autonomous operational frameworks, and intelligent cloud infrastructure. The proposed approach emphasizes security-by-design principles, AI governance, continuous monitoring, automated compliance, zero-trust architecture, and resilient cloud engineering practices. The integration of these technologies enables enterprises to improve agility, reduce operational complexity, strengthen cybersecurity, and accelerate digital transformation initiatives. Furthermore, the study highlights the importance of responsible AI management, secure data handling, and adaptive infrastructure optimization for building trustworthy enterprise ecosystems. The findings demonstrate that combining Generative AI with autonomous operations and intelligent cloud infrastructure provides a foundation for creating secure, scalable, and future-ready enterprise platforms capable of supporting continuous innovation.
References
1. Soundappan, S. J. (2024). Generative AI Enabled Enterprise Systems with Autonomous Operations and Cloud-Native Architectures. International Journal of Computer Technology and Electronics Communication, 7(4), 9247-9253.
2. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
3. Potdar, A., Kodela, V., Srinivasagopalan, L. N., Khan, I., Chandramohan, S., & Gottipalli, D. (2025, July). Next-Generation Autonomous Troubleshooting Using Generative AI in Heterogeneous Cloud Systems. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-7). IEEE.
4. Govindan, V. (2025). Vendor dependency to enterprise sovereignty: A phased migration approach for enterprise applications. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(4), 11176–11185. https://doi.org/10.15680/IJCTECE.2025.0804021
5. Konakalla, K. (2022). Automating customer feedback integration in the sales cycle: Enhancing sales performance and accountability through Salesforce and Medallia. Journal of Marketing & Supply Chain Management, 1-3.
6. Rao, G. R. (2023). Index lifecycle and shard allocation optimization in large-scale Elasticsearch clusters: A performance–cost trade-off analysis. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(4), 6903–6907.
7. Syed, S. (2025). Enterprise asset management digitalization for multi-site pharmaceutical manufacturing: A transatlantic Oracle eAM implementation. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(4), 11138–11146. https://doi.org/10.15680/IJCTECE.2025.0804018
8. Juvvadi, R. R. (2019). Smart contracts in supply chain finance: Automating accounts payable and the three-way match. Journal of Information Systems Engineering and Management, 4(1), 1–12.
9. Mathew, A. (2024). Cloud data sovereignty governance and risk implications of cross-border cloud storage. Information Systems Audit and Control Association.
10. Sarngadharan, S. (2025). Self-optimizing pipelines: ML systems that tune themselves in production. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(2), 10468–10476. https://doi.org/10.15680/IJCTECE.2025.0802015
11. Velishala, S. (2025). Leveraging machine learning in DevOps pipelines to enhance patient data management systems. ISCSITR–International Journal of Computer Science and Engineering, 6(1), 31–49.
12. Makkena, B. (2025). Zero-Downtime Deployment Strategies In Financial-Grade CI/CD Pipelines: Enabling Continuous Compliance And Resilience. International Journal of Environmental Sciences, 11(19s), 2025.
13. Chenna, S. (2024). Reinforcement learning-based dynamic load assignment for automated 3PL tendering systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7917–7932. https://doi.org/10.15662/IJEETR.2024.0602015
14. Veershetty, G. (2019). From Legacy Back Office to Intelligent Utility Enterprise a Practitioner Case Study of SAP Cloud Transformation and Utility IT Landscape Modernization. American International Journal of Computer Science and Technology, 1(1), 23-27.
15. Kotla, M. R. T. (2024). Optimizing enterprise integration pipelines using cloud-native data engineering and middleware solutions. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(5), 11311-11314.
16. Mohammed, S. (2024). Resilient multi-region cloud architecture for high availability and disaster recovery. International Journal of Multidisciplinary and Scientific Emerging Research, 12(3), 1412–1427. https://doi.org/10.15662/IJMSERH.2024.1203046
17. Gurram, S. K. (2024). Federated learning for anomaly detection in distributed systems. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 14031–14040.
18. Polamreddy, V. R. (2022). Architecting Hybrid Synchronization Models to Enable Safe International Platform Transitions. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(1), 6216-6229.
19. Hassan, S. Z., Deshapaga, M., Bansod, M., Soni, H., & Rajendran, R. N. (2025, August). From Tokens to Tactics: Operationalizing Generative AI in Enterprise Workflows. In 2025 IEEE 2nd International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS) (pp. 1-8). IEEE.
20. Gummadi, V. P. K. (2019). Microservices architecture with APIs: Design, implementation, and MuleSoft integration. Journal of Electrical Systems, 15(4), 130-134.
21. Gandikota, S. P. (2025). High-availability network diagnostics and configuration platform for real-time financial service delivery. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(4), 11147–11160. https://doi.org/10.15680/IJCTECE.2025.0804019
22. Gopinathan, V. R., & Mali, R. K. (2024). Enterprise digital transformation through AI Salesforce automation secure cloud infrastructure and event-driven architectures. International Research Journal of Innovative Engineering (IRJIE), 8(5), 15322–15331.
23. Velishala, S. (2025). AI-based decision support systems for healthcare DevOps: Improving reliability and decision-making in software development. Journal of Advanced Research in Engineering and Technology, 2(1).
24. Anbazhagan, K. (2024). Trustworthy and Adaptive AI Systems for Enterprise Analytics Cybersecurity and Decision Optimization Using API-First and Cloud-Native Architectures. International Journal of Technology, Management and Humanities, 10(03), 65-74.
25. Gopisetty, S. (2024). What the Jenkins Logs Won’t Tell You: Using an AI Agent to Capture the Lost ‘Bank Memory’Behind a 76% Sprint Velocity Gain and Whether Another Community Bank Can Borrow It Without the Original Team. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(3), 259-276.
26. Navandar, P. (2024). Quantum safe public key infrastructure: Hybrid classical PQC certificate chains and migration framework for enterprise TLS. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8153–8160. https://doi.org/10.15662/IJEETR.2024.0604014
27. Mathew, A. (2025). Secure and Scalable AI-Integrated Cloud Infrastructure for HIPAA-Compliant Healthcare Financial Operations. International Journal of Future Innovative Science and Technology (IJFIST), 8(4), 15296.
28. Mannem, S. (2024). From requirements to production: Managing cross-regional API deployments at Capital One. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7892–7898. https://doi.org/10.15662/IJEETR.2024.0602013
29. Chettiyar, S. S. S. (2024). Agentic AI orchestrated conversational payment pipelines with drift-aware transaction. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(3), 8166–8174. https://doi.org/10.15662/IJEETR.2024.0603008
30. Kanji, R. K. (2021). Real-Time Big Data Processing with Edge Computing. European Journal of Advances in Engineering and Technology, 8(11), 152-155.
31. Sugumar, R. (2025). Open Ecosystems in Finance: Balancing Innovation, Security, and Compliance. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(1), 11548-11554.
32. Devineni, A. (2025). Automated Remediation Guardrails: A Risk-Aware Framework for Validating AI-Generated Production Scripts in Regulated Financial Infrastructure. International Journal of AI, BigData, Computational and Management Studies, 6(2), 113-118.
33. Kandula, S. T. R. (2025, July). Comparison and Performance Assessment of Intelligent ML Models for Forecasting Cardiovascular Disease Risks in Healthcare. In 2025 International Conference on Sensors and Related Networks (SENNET) Special Focus on Digital Healthcare (64220) (pp. 1-6). IEEE.
34. Gurram, S. K. (2024). Federated learning for anomaly detection in distributed systems. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 14031–14040.
