Articles Open Access
Building Resilient Enterprise Platforms with Artificial Intelligence Integrated Cloud Computing for Secure Data Engineering
Associate - Cloud Engineering and DevSecOps, Austin, Texas, USA
Abstract
Modern enterprise platforms face unprecedented challenges in maintaining operational resilience while managing massive, heterogeneous data streams. This paper explores the integration of Artificial Intelligence (AI) with cloud computing architectures to establish secure, self-healing, and resilient data engineering pipelines. As cyber threats become more sophisticated and data volumes expand exponentially, traditional static infrastructure falls short of meeting enterprise-grade security and availability standards. By leveraging machine learning algorithms, predictive analytics, and automated orchestration within cloud environments, organizations can dynamically mitigate risks, optimize resource allocation, and ensure continuous data integrity. This study evaluates the synergistic relationship between AI and cloud native technologies, detailing how intelligent automation transforms passive data repositories into active, resilient ecosystems. Furthermore, we examine the protocols necessary for securing data engineering pathways, focusing on automated threat detection, zero-trust architectures, and intelligent cryptographic management. Ultimately, this research provides a comprehensive framework for designing next-generation enterprise platforms capable of autonomous adaptation to operational anomalies and security breaches. The findings demonstrate that AI-driven cloud computing not only fortifies data engineering infrastructure against external failures but also significantly enhances compliance, scalability, and long-term operational sustainability in volatile digital landscapes.
Keywords
DOI
PDF Download
Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 10 |
| Number | 4 |
| Pages | 19261-19268 |
| Published | 2026-07-09 |
| Issue | Vol. 10 No. 4 (2026): International Research Journal of Innovative Engineering (IRJIE) |
References
1. Gopakumar, S. (2026, February). SentiForesight: An AI Framework for Prescriptive Analytics on Social Streams. In 2026 Contemporary Computing Innovations Conference (CCIC) (pp. 1-6). IEEE.
2. Meesala, L. K. (2024). Converging Infrastructure and Security: A Maturity-Based Approach to Cloud-Native Data Protection, SIEM Optimization, and Compliance Automation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 283-289.
3. Kodela, S., Kurada, S. B., Mogili, V. B., & Duggirala, J. (2026, March). Deep Learning-Enhanced Cloud Accounting Model for Real-Time Fraud and Financial Risk Prediction. In 2026 Innovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-6). IEEE.
4. Ayyalusamy, G. K., Srinivas, S., Rao, S. R. S., Ande, K., Ahmad, A., & Shamoon, R. (2025, July). AI-Driven Urban Traffic Optimization for Smart Cities. In 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) (pp. 1-6). IEEE.
5. Vas, M. R. (2025). AI-Augmented DevSecOps for Cloud Environments: Bridging Security Gaps in Modern Continuous Delivery Pipelines. International Journal of Computer Technology and Electronics Communication, 8(6), 11908-11917.
6. Adepu, G. (2023). Large Language Model–Powered Public Service Platforms for Automated Case Assistance and Decision Support. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7744-7748.
7. Devineni, A. (2025). Post-Mortem Intelligence: Using Large Language Models to Build Proactive Reliability Knowledge Graphs from Incident Documentation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 6(3), 170-175.
8. Bandaru, N. (2025). Architecting Compliance Ready Artificial Intelligence for Regulated Digital Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(4), 12463-12471.
9. Karim Chy, M. S. (2026). Securing National Healthcare Infrastructure: Intelligent Monitoring Of Fraudulent Claims Using AI. International Journal of Clinical and Medical Sciences-IJCMS, 2(1), 15-38.
10. Mahajan, A., Uddandarao, D. P., & Konatham, M. R. (2026). Impact of statistically driven AI forecasting of energy demand under dynamic market conditions. Scientific Culture, 12(2, Part 1), 112–123. https://doi.org/10.5281/zenodo.122.12611
11. Rohit Wadhwa. (2024). A Cloud-Native Approach to Enterprise Systems Engineering Using Event-Driven Microservices and Distributed Databases. International Journal of Cloud Computing (IJCC), 2(1), 81–93. DOI: https://doi.org/10.34218/IJCC_02_01_005
12. Sonawane, S. (2025). Zero-touch OEM onboarding: End-to-end automation from deal registration to license provision. International Journal of Applied Mathematics, 38(12S), 563–571. https://doi.org/10.12732/ijam.v38i12s.1400
13. Meesala, A. (2025). An Enterprise-Scale Retrieval-Augmented Generative Intelligence Platform for Real-Time Financial Document Synthesis and Regulatory Compliance Automation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 6(1), 293-299.
14. Chaba, A. (2020). A Reusable Enterprise Commerce Deployment Framework for Accelerated Digital Transformation. International Journal of Research and Applied Innovations, 3(2), 3068-3082.
15. Gujarathi, M. (2025). Guardrails for prompt-to-SQL workflows in applied AI production support. International Journal of Research and Applied Innovations (IJRAI), 8(6), 13200–13210.
16. 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
17. 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
18. 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.
19. 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.
20. Mohammed, S. (2025). Secure hybrid cloud engineering with compliance-driven governance models. International Journal of Research and Applied Innovations, 8(2), 11262-11265.
21. Narayanan, S. (2024). Cyber risk orchestration for systemic financial stability: An autonomous financial impact forecasting. International Journal of Research in Computer Applications and Information Technology, 7(2), 2927–2939. https://philarchive.org/archive/NARCRO
22. Mathew, A., & Izek, B. (2026). Beyond the Pixel Veil: Forensic Analysis of IDAT Signatures and Generator-Specific Artifacts in AI-Generated Images. International Journal of Computer Technology and Electronics Communication, 9(2), 616-620.
23. 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.
24. 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.
25. Kandula, S. T. R., & Boyapati, P. K. (2026, February). Advancing Cybersecurity in Critical Infrastructure Systems via Machine Learning-Based Threat Detection and Mitigation. In 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC) (pp. 1-7). IEEE.
26. Gentyala, S., Tejasri, N., & Mudusu, S. K. (2026, June). A Multi-Stage NLP Framework for Enterprise Data Protection in Public LLM Interactions. In 2026 7th International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 2057-2064). IEEE.
27. Soni, H. (2026, April 1). AI-driven enterprise architecture and value realization framework. In Proceedings of the Artificial Super Intelligence (ASI) Conference 2026. Kennesaw State University. https://digitalcommons.kennesaw.edu/asi/2026/proceedings/16/
28. B. Alti. (2025). AI-powered governance-as-code for secure Linux operations in FinTech infrastructure. Computer Fraud & Security.
29. Prasanna Kumar Natta. (2022). AI-driven inventory intelligence for large-scale retail operations: A framework for real-time store-level stock accuracy. International Journal of Advanced Engineering Science and Information Technology, 5(2), 8740–8751. https://doi.org/10.15662/IJAESIT.2022.0502002
30. Kumar, R., Pandey, C. P., & Upadhyay, H. (2026). The Future of Responsible Investment: AI, Automation, and Human Judgment. In AI and Automation in Green Investment Platforms: Next-Generation ESG (pp. 271-288). IGI Global Scientific Publishing.
31. Ali, M., Rahman, M. W., Hossain, M. S., & Hossain, M. S. (2026). Developing AI-Driven Business Intelligence Systems to Strengthen Economic Competitiveness AND Policy Innovation in the United States. Repository Antis Publisher, 692631.
32. Chalicham, H. (2025). Best Practices for Quality Assurance in Multi-Layered LLM Systems (Frontend-Backend-Cloud). World Research of Business Administration Journal, 5(3).
33. Anumula, S. K., Talluru, N., Gangavarapu, R., Gupta, S., & Tatavarthy, K. Quantum-Inspired Computing: Classical Approaches to Machine Learning. In Quantum-Inspired Neural Networks (pp. 74-88). CRC Press.
34. Gollapudi, R. (2026). Designing self-healing database fabrics for real-time payment rails. International Journal of Electrical and Computer Engineering (IJECE), 16(3), 1360–1368. https://doi.org/10.11591/ijece.v16i3.pp1360-1368
35. Alam, M. M., Khan, M. I., & Sayem, S. M. (2026). Hybrid Cybersecurity: AI Models for Predicting Threats Across Multi-Cloud for US Business Environment. Frontiers in Computer Science and Artificial Intelligence, 5(9), 59-66.
2. Meesala, L. K. (2024). Converging Infrastructure and Security: A Maturity-Based Approach to Cloud-Native Data Protection, SIEM Optimization, and Compliance Automation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 283-289.
3. Kodela, S., Kurada, S. B., Mogili, V. B., & Duggirala, J. (2026, March). Deep Learning-Enhanced Cloud Accounting Model for Real-Time Fraud and Financial Risk Prediction. In 2026 Innovations in Machine, Engineering, and Digital Conference (IMED) (pp. 1-6). IEEE.
4. Ayyalusamy, G. K., Srinivas, S., Rao, S. R. S., Ande, K., Ahmad, A., & Shamoon, R. (2025, July). AI-Driven Urban Traffic Optimization for Smart Cities. In 2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS) (pp. 1-6). IEEE.
5. Vas, M. R. (2025). AI-Augmented DevSecOps for Cloud Environments: Bridging Security Gaps in Modern Continuous Delivery Pipelines. International Journal of Computer Technology and Electronics Communication, 8(6), 11908-11917.
6. Adepu, G. (2023). Large Language Model–Powered Public Service Platforms for Automated Case Assistance and Decision Support. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7744-7748.
7. Devineni, A. (2025). Post-Mortem Intelligence: Using Large Language Models to Build Proactive Reliability Knowledge Graphs from Incident Documentation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 6(3), 170-175.
8. Bandaru, N. (2025). Architecting Compliance Ready Artificial Intelligence for Regulated Digital Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(4), 12463-12471.
9. Karim Chy, M. S. (2026). Securing National Healthcare Infrastructure: Intelligent Monitoring Of Fraudulent Claims Using AI. International Journal of Clinical and Medical Sciences-IJCMS, 2(1), 15-38.
10. Mahajan, A., Uddandarao, D. P., & Konatham, M. R. (2026). Impact of statistically driven AI forecasting of energy demand under dynamic market conditions. Scientific Culture, 12(2, Part 1), 112–123. https://doi.org/10.5281/zenodo.122.12611
11. Rohit Wadhwa. (2024). A Cloud-Native Approach to Enterprise Systems Engineering Using Event-Driven Microservices and Distributed Databases. International Journal of Cloud Computing (IJCC), 2(1), 81–93. DOI: https://doi.org/10.34218/IJCC_02_01_005
12. Sonawane, S. (2025). Zero-touch OEM onboarding: End-to-end automation from deal registration to license provision. International Journal of Applied Mathematics, 38(12S), 563–571. https://doi.org/10.12732/ijam.v38i12s.1400
13. Meesala, A. (2025). An Enterprise-Scale Retrieval-Augmented Generative Intelligence Platform for Real-Time Financial Document Synthesis and Regulatory Compliance Automation. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 6(1), 293-299.
14. Chaba, A. (2020). A Reusable Enterprise Commerce Deployment Framework for Accelerated Digital Transformation. International Journal of Research and Applied Innovations, 3(2), 3068-3082.
15. Gujarathi, M. (2025). Guardrails for prompt-to-SQL workflows in applied AI production support. International Journal of Research and Applied Innovations (IJRAI), 8(6), 13200–13210.
16. 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
17. 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
18. 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.
19. 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.
20. Mohammed, S. (2025). Secure hybrid cloud engineering with compliance-driven governance models. International Journal of Research and Applied Innovations, 8(2), 11262-11265.
21. Narayanan, S. (2024). Cyber risk orchestration for systemic financial stability: An autonomous financial impact forecasting. International Journal of Research in Computer Applications and Information Technology, 7(2), 2927–2939. https://philarchive.org/archive/NARCRO
22. Mathew, A., & Izek, B. (2026). Beyond the Pixel Veil: Forensic Analysis of IDAT Signatures and Generator-Specific Artifacts in AI-Generated Images. International Journal of Computer Technology and Electronics Communication, 9(2), 616-620.
23. 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.
24. 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.
25. Kandula, S. T. R., & Boyapati, P. K. (2026, February). Advancing Cybersecurity in Critical Infrastructure Systems via Machine Learning-Based Threat Detection and Mitigation. In 2026 IEEE 5th International Conference on AI in Cybersecurity (ICAIC) (pp. 1-7). IEEE.
26. Gentyala, S., Tejasri, N., & Mudusu, S. K. (2026, June). A Multi-Stage NLP Framework for Enterprise Data Protection in Public LLM Interactions. In 2026 7th International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 2057-2064). IEEE.
27. Soni, H. (2026, April 1). AI-driven enterprise architecture and value realization framework. In Proceedings of the Artificial Super Intelligence (ASI) Conference 2026. Kennesaw State University. https://digitalcommons.kennesaw.edu/asi/2026/proceedings/16/
28. B. Alti. (2025). AI-powered governance-as-code for secure Linux operations in FinTech infrastructure. Computer Fraud & Security.
29. Prasanna Kumar Natta. (2022). AI-driven inventory intelligence for large-scale retail operations: A framework for real-time store-level stock accuracy. International Journal of Advanced Engineering Science and Information Technology, 5(2), 8740–8751. https://doi.org/10.15662/IJAESIT.2022.0502002
30. Kumar, R., Pandey, C. P., & Upadhyay, H. (2026). The Future of Responsible Investment: AI, Automation, and Human Judgment. In AI and Automation in Green Investment Platforms: Next-Generation ESG (pp. 271-288). IGI Global Scientific Publishing.
31. Ali, M., Rahman, M. W., Hossain, M. S., & Hossain, M. S. (2026). Developing AI-Driven Business Intelligence Systems to Strengthen Economic Competitiveness AND Policy Innovation in the United States. Repository Antis Publisher, 692631.
32. Chalicham, H. (2025). Best Practices for Quality Assurance in Multi-Layered LLM Systems (Frontend-Backend-Cloud). World Research of Business Administration Journal, 5(3).
33. Anumula, S. K., Talluru, N., Gangavarapu, R., Gupta, S., & Tatavarthy, K. Quantum-Inspired Computing: Classical Approaches to Machine Learning. In Quantum-Inspired Neural Networks (pp. 74-88). CRC Press.
34. Gollapudi, R. (2026). Designing self-healing database fabrics for real-time payment rails. International Journal of Electrical and Computer Engineering (IJECE), 16(3), 1360–1368. https://doi.org/10.11591/ijece.v16i3.pp1360-1368
35. Alam, M. M., Khan, M. I., & Sayem, S. M. (2026). Hybrid Cybersecurity: AI Models for Predicting Threats Across Multi-Cloud for US Business Environment. Frontiers in Computer Science and Artificial Intelligence, 5(9), 59-66.
Statistics
78
Views
53
Downloads