Articles Open Access
Enterprise Digital Intelligence with Cloud Native Computing and Artificial Intelligence Driven Automation for Secure Enterprise Operations
Sr Software Engineer, Access2care LLC (MTM), Saint Louis, MO, USA
Abstract
Automation for secure enterprise operations has become an essential strategy for organizations seeking to improve operational efficiency while maintaining robust cybersecurity. The increasing complexity of enterprise infrastructures, including cloud computing, Internet of Things (IoT), remote work environments, and hybrid networks, has significantly expanded the attack surface for cyber threats. Traditional manual security operations are often inadequate for responding to sophisticated attacks in real time. Enterprise automation integrates technologies such as Artificial Intelligence (AI), Machine Learning (ML), Security Orchestration, Automation, and Response (SOAR), robotic process automation, and automated compliance management to strengthen security while reducing operational costs. Automated systems continuously monitor network activities, detect anomalies, prioritize threats, execute predefined response actions, and generate compliance reports with minimal human intervention. These capabilities enable faster incident response, improved threat intelligence, reduced human error, and enhanced regulatory compliance. However, organizations also face challenges related to implementation costs, system integration, workforce readiness, data privacy, and maintaining transparency in automated decision-making. This study examines the significance of automation in secure enterprise operations by reviewing existing research, identifying technological advancements, and proposing an appropriate research methodology for evaluating automation effectiveness. The findings are expected to provide insights into how intelligent automation enhances enterprise resilience, supports business continuity, and establishes a proactive cybersecurity framework capable of adapting to evolving digital threats
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 9 |
| Number | 4 |
| Pages | 17110-170118 |
| Published | 2025-08-08 |
| Issue | Vol. 9 No. 4 (2025): International Research Journal of Innovative Engineering (IRJIE) |
References
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25. Adari, V. K. (2024). Interoperability and Data Modernization: Building a Connected Banking Ecosystem. International Journal of Computer Engineering and Technology (IJCET), 15(6), 653-662.
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27. Devineni, A. (2023). Automated Compliance-Driven Patch Management and Security Hardening in Multi-Cloud Banking Infrastructure Using IaC and Python Orchestration. The American Journal of ET, 5(12), 68-80.
28. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
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31. Gupta, S., Barigidad, S., Hussain, S., Dubey, S., & Kanaujia, S. (2025, February). Hybrid Machine Learning for Feature-Based Spam Detection. In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) (pp. 801-806). IEEE.
32. Manda, P. (2024). The role of machine learning in automating complex database migration workflows. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10451–10459.
33. Makkena, B. (2023). PromptOps: Building prompt-driven DevOps workflows for infrastructure-as-code automation. International Journal of Communication Networks and Information Security, 15(10), 12–30.
34. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
35. Meesala, L. K. (2023). A layered security framework for enterprise operations in the Generative AI and Agentic AI era in regulated cloud environments. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11752–11760.
36. Parasa, M. (2023). A structured recruitment analytics framework for candidate screening and talent pool utilization in SAP SuccessFactors Recruiting. Global Journal of Engineering and Technology, 2(11), 29–39. https://gsarpublishers.com/gjet-vol-2-issue-11-november-2023/
2. Prasad, P. K. (2024). Establishing AI governance frameworks within CloudOps to accelerate safe, compliant AI adoption at scale. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 14030.
3. Mohammed, S., & Polamarasetty, V. K. (2023). Azure cloud landing zone architecture for enterprise-scale cloud adoption. International Journal of Information Technology and Management Information Systems, 14(1), 117–147. https://doi.org/10.34218/IJITMIS_14_01_012
4. Sudakara, B. B. (2023). Integrating Cloud-Native Testing Frameworks with DevOps Pipelines for Healthcare Applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(5), 9309-9316.
5. 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.
6. 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
7. 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
8. Meesala, A. (2022). Adaptive Spread Anomaly Intelligence Framework (ASAIF): A cloud-native AI framework for real-time bid-ask spread anomaly detection and cross-venue liquidity risk intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 5(6), 9597–9604.
9. 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.
10. Polamreddy, V. R. (2024). Hybrid On-Premise to Cloud Data Migration: Architectural Patterns for Controlled One-Way Synchronization. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(3), 8143-8156.
11. Sharma, Ankit and Mulgund, Pavankumar and Srivastava, Adarsh and Agrawal, Lavlin, Beyond Cryptocurrency: There’s More to Blockchain (January 07, 2020). Beyond Cryptocurrency: There's More to Blockchain, Amplify, Cutter Consortium, January 7, 2020., Available at SSRN: https://ssrn.com/abstract=6098906 or http://dx.doi.org/10.2139/ssrn.6098906
12. Sarngadharan, S. (2024). A secure, zero-trust mobile expert locator for global professional services firms. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(3), 8157–8165. https://doi.org/10.15662/IJEETR.2024.0603007
13. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
14. Syed, S. (2024). A zero-defect high sea sale automation framework for real-time ownership transfer and compliance in maritime trade systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7878–7891. https://doi.org/10.15662/IJEETR.2024.0602012
15. Govindan, V. (2024). Automating vulnerability remediation: A continuous SAST and FOSS integration framework for production support pipelines. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7899–7916. https://doi.org/10.15662/IJEETR.2024.0602014
16. Veershetty, G. (2023). SAP S/4HANA Transformation in the Electric Power and Grid Utility Sector: Combination Migration Strategy and Customer-Managed Deployment A Practitioner's Analysis. International Journal of Emerging Research in Engineering and Technology, 4(1), 218-227.
17. Sunnam, S. V. B. R. (2021). Big Data Analytics Using Hadoop and Spark: Applications, Challenges, and Future Direction. Journal of Computer Science and Technology Studies, 3(1), 50-62.
18. Gandikota, S. P. (2024). Scaling national wireless services: A technical case study of T-Mobile's middleware evolution on AWS cloud. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7869–7877. https://doi.org/10.15662/IJEETR.2024.0602011
19. Dama, H. B. (2024). Cross-Cloud Data Consistency Models for Always-On Banking Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8468-8476.
20. Devineni, A. (2022). Proactive incident detection in multi-tenant financial cloud platforms. International Journal of Science, Research and Technology, 5(4), 8136-8139.
21. Mathew, A., & Mai, C. (2018, May). Study of Various Data Recovery and Data Back Up Techniques in Cloud Computing & Their Comparison. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT) (pp. 2021-2024). IEEE.
22. 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
23. Navandar, P. (2024). Governance, risk, and compliance (GRC) in the age of identity and access governance (IAG): A framework for integrated enterprise security and compliance. International Journal of Research and Applied Innovations (IJRAI), 7(2), 10483–10493. https://doi.org/10.15662/IJRAI.2024.0702011
24. 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.
25. Adari, V. K. (2024). Interoperability and Data Modernization: Building a Connected Banking Ecosystem. International Journal of Computer Engineering and Technology (IJCET), 15(6), 653-662.
26. Gopisetty, S. (2024). Why was my transaction flagged? Building counterfactual stories for AI threat detection in real-time ERP systems. QIT Press - International Journal of Artificial Intelligence Research and Development (QITP-IJAIRD), 5(1), 20–56.
27. Devineni, A. (2023). Automated Compliance-Driven Patch Management and Security Hardening in Multi-Cloud Banking Infrastructure Using IaC and Python Orchestration. The American Journal of ET, 5(12), 68-80.
28. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900-2903.
29. Anumula, S. K. (2025). Next-gen supply chains: A product lifecycle management–based approach to resilient and sustainable operations. International Journal of Managing Value and Supply Chains (IJMVSC), 16.
30. Raj, A. A., & Sugumar, R. (2022, December). Monitoring of the Social Distance between Passengers in Real-time through Video Analytics and Deep Learning in Railway Stations for Developing the Highest Efficiency. In 2022 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (Vol. 1, pp. 1-7). IEEE.
31. Gupta, S., Barigidad, S., Hussain, S., Dubey, S., & Kanaujia, S. (2025, February). Hybrid Machine Learning for Feature-Based Spam Detection. In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN) (pp. 801-806). IEEE.
32. Manda, P. (2024). The role of machine learning in automating complex database migration workflows. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10451–10459.
33. Makkena, B. (2023). PromptOps: Building prompt-driven DevOps workflows for infrastructure-as-code automation. International Journal of Communication Networks and Information Security, 15(10), 12–30.
34. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
35. Meesala, L. K. (2023). A layered security framework for enterprise operations in the Generative AI and Agentic AI era in regulated cloud environments. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11752–11760.
36. Parasa, M. (2023). A structured recruitment analytics framework for candidate screening and talent pool utilization in SAP SuccessFactors Recruiting. Global Journal of Engineering and Technology, 2(11), 29–39. https://gsarpublishers.com/gjet-vol-2-issue-11-november-2023/
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