Articles Open Access
Autonomous AI-Based Enterprise Intelligence Using Multi-Cloud Microservices and Secure API Engineering
Software Architect, Helsinki, Finland
Abstract
Autonomous AI-based enterprise intelligence represents a transformative approach for organizations seeking to automate decision-making, operational optimization, and business process management across complex multi-cloud environments. This paper proposes an intelligent enterprise framework that integrates artificial intelligence, multi-cloud microservices, secure API engineering, real-time analytics, and autonomous decision mechanisms. The proposed architecture enables distributed enterprise applications to communicate through secure APIs while AI agents continuously analyze operational, business, and infrastructure data to identify patterns, predict risks, recommend actions, and initiate approved automated responses. Multi-cloud microservices provide scalability, resilience, portability, and workload distribution across heterogeneous cloud platforms, while API engineering establishes standardized and secure communication among applications, services, data platforms, and intelligent agents. The framework incorporates identity-based access control, encryption, API gateways, policy enforcement, continuous monitoring, threat detection, and audit mechanisms to strengthen security. AI components can support predictive analytics, anomaly detection, intelligent resource allocation, service optimization, and autonomous workflow orchestration. The research methodology evaluates the framework according to intelligence, scalability, security, latency, availability, automation effectiveness, and resource utilization. The proposed model aims to reduce operational complexity, improve enterprise responsiveness, minimize manual intervention, and establish a secure foundation for autonomous digital operations. By combining AI-driven intelligence with cloud-native engineering principles, the framework provides a scalable architecture for next-generation enterprise systems.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 9 |
| Number | 6 |
| Pages | 17943-17953 |
| Published | 2025-12-16 |
| Issue | Vol. 9 No. 6 (2025): International Research Journal of Innovative Engineering (IRJIE) |
References
1. Alam, M. T. U., Azam, M. N., Raihena, S. S., Al-Imran, M., Chowdhury, M. S., & Mozomder, A. S. (2025). Predicting Donor Churn and Customer Sentiment from Reviews Using Logistic Regression and NLP: A Data-Driven Approach to Retention and Sentiment Analysis. Journal of Business and Management Studies, 7(4), 340–350.
2. Gopinathan, V. R. (2025). Developing Large Language Model Integrated Cloud-Native DevSecOps for Software Delivery and Continuous Security Validation. International Research Journal of Innovative Engineering, 9(5), 17558–17565.
3. Rajula, A. (2022). Cloud-based virtual patient engagement with intelligent scheduling and secure document management. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9245.
4. Sikarwar, V. (2025). Intelligent Metadata-Driven Data Engineering: Accelerating Standardized, Scalable Data Pipelines. International Journal of Research and Applied Innovations, 8(1), 11690–11699.
5. Tyagi, N. (2024). Deep reinforcement learning for algorithmic trading strategies. International Journal of Research and Applied Innovations, 7(2), 10415–10422.
6. Challa, R. (2024). High-Availability HPC Cluster Design for Mission-Critical Public Infrastructure: Lessons from Energy Grid and Government. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 9305–9309.
7. Soundappan, S. J. (2023). Designing Intelligent Enterprise Platforms Using Machine Learning Driven API Engineering and Cloud Native Security. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9074–9081.
8. Pareek, C. (2023). Unmasking bias: A framework for testing and mitigating AI bias in insurance underwriting models. J Artif Intell Mach Learn & Data Sci, 1(1), 1736–1741.
9. Vemireddy, S. (2022). Modernizing enterprise financial platforms through distributed cloud architectures. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7420–7426.
10. Kundurthy, O. H., Kaata, S. K., Vikram, S., Somayajula, R., & Gangavarapu, R. (2025, September). A Framework for Lightweight Generative AI: Enabling Secure, Scalable, and Cloud-to-Edge Intelligence with MicroLLMs. In 2025 International Conference on Electronics and Computing, Communication Networking Automation Technologies (ICEC2NT) (pp. 1–8). IEEE.
11. Bellundagi, M. (2022). Performance Optimization Techniques for Enterprise Java Applications Using Middleware and Messaging Systems. International Journal of Computer Technology and Electronics Communication, 5(3), 5158–5168.
12. Chundi, V. R. K. (2025). AI-based Sustainable Vehicle Monitoring System for Existing Internal Combustion Vehicles. London Journal of Research In Computer Science and Technology, 25(3), 1–7.
13. Chaba, A. (2021). API-driven enterprise commerce architecture for composable digital ecosystems. International Journal of Engineering & Extended Technologies Research, 3(6), 4082–4086.
14. Mohan, A. (2025). Causal inference in data science: A framework for attribution systems. European Journal of Computer Science and Information Technology, 13(36), 107–113.
15. Narra, S. L. (2025). Demystifying Zero Trust Architecture: Why It's Not Just a Buzzword. International Journal of Computing and Engineering, 7(10), 1–16.
16. Bandaru, P. K. (2021). Leveraging hardware-in-the-loop simulation for continuous automotive software testing. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3730–3735.
17. Abd-Rouf, M. S. K., Adigun, P. O., Alalade, E. O., Oyekanmi, T. T., Faniyi, A. J., Oladapo, B., Awopejo, T. E., Adegoke, O. S., Jamiu, A., Michael, O. B., Obisesan, A., Ajala, S., Adekanye, M. A., Yambali, P. M., & Abd-Rouf, A. B. (2024). From molecular profiling to predictive algorithms: A conceptual machine-learning framework for mechanism-informed therapy selection in multidrug-resistant cancer. International Journal of Science, Research and Technology (IJSRAT), 7(3), 12085–12101.
18. Reddy, G. C. P. (2019). Asymptotic Analysis in Cloud Resource Allocation: Scalability of Virtual Machines, Scheduling Complexity, and Auto-Scaling Mechanisms. Journal of Computational Analysis and Applications, 27(7).
19. Vimal Raja, G. (2024). Intelligent data transition in automotive manufacturing systems using machine learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515–518.
20. Sikarwar, V. (2024). AI-Driven Data Quality Framework for Modern Data Lakes: An Architecture Overview. Journal of Artificial Intelligence General science (JAIGS), 6(1), 662–688. ISSN: 3006-4023.
21. Beeram, S. (2024). AI-driven intelligent traffic management in Microsoft Azure: Predictive routing for resilient cloud applications. International Journal of Advanced Engineering Science and Information Technology, 7(4), 14534–14538.
22. Hoque, M. J., Akter, F., & Mohammad, A. R. (2019). Data-Driven Decision Support System for Restaurant Business Optimization. American Journal of Economics and Business Management, 2(2).
23. Gopinathan, V. R. (2024). Generative AI Enabled Enterprise Cloud Platforms for Intelligent Business Process Automation and Secure Data Integration. International Journal of Emerging Trends in Engineering and Management Research, 9(5), 16445.
24. 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.
25. Das, P., Gogineni, A., Patel, K., & Jain, A. (2025, May). Integration of IoT and Machine Learning to Monitor the Air Quality by Measuring the Block Carbon Levels. In 2025 International Conference on Engineering, Technology & Management (ICETM) (pp. 1–6). IEEE.
26. 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.
27. Alam, A., Gazi, M. S., Abdullah, S. M., Tasnim, M., Himeluzzaman, M., Nabil, M. A., ... & Akter, S. (2021). Quantum-resilient federated intrusion detection: A hybrid quantum-classical framework for safeguarding US critical infrastructure in the post-quantum era. International Journal of Advances in Signal and Image Sciences, 7(1), 57–72.
28. Vani, M., & Dadlani, D. (2023). Smart home – “Aashraya” IoT automation system. International Journal of Computer Technology and Electronics Communication, 6(1), 6393–6403.
29. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900–2903.
30. Ferdausi, N. S., Fatema, N. K., Mahmud, N. M. R., Hoque, N. R., & Ali, N. M. (2025). Transforming telehealth with Artificial Intelligence: Predictive and diagnostic advances in remote patient care. World J Adv Eng Technol Sci, 16(1), 355–65.
31. Koganti, H. (2024). The computational complexity of hybrid algorithms: When branch-and-bound meets machine learning heuristics. International Journal of Research and Applied Innovations (IJRAI), 7(4), 11184–11190.
32. Yepuri, V. K., Polamarasetty, V. K., Donthi, S., & Gondi, A. K. R. (2023). Containerization of a polyglot microservice application using Docker and Kubernetes. arXiv preprint arXiv:2305.00600.
33. 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.
34. Mathew, A., & Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science Vol. 4, 154–164.
35. Padmanabham, S. (2022). Enterprise identity and access management architecture for large financial institutions. International Journal of Research and Applied Innovations, 5(1), 9486–9490.
36. Raja, G. V. (2022). AI Enabled Cloud and Data Engineering Frameworks for Secure, Scalable, and Intelligent Cyber Physical Analytics Systems. International Journal of Research and Applied Innovations, 5(4), 7395–7409.
2. Gopinathan, V. R. (2025). Developing Large Language Model Integrated Cloud-Native DevSecOps for Software Delivery and Continuous Security Validation. International Research Journal of Innovative Engineering, 9(5), 17558–17565.
3. Rajula, A. (2022). Cloud-based virtual patient engagement with intelligent scheduling and secure document management. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9245.
4. Sikarwar, V. (2025). Intelligent Metadata-Driven Data Engineering: Accelerating Standardized, Scalable Data Pipelines. International Journal of Research and Applied Innovations, 8(1), 11690–11699.
5. Tyagi, N. (2024). Deep reinforcement learning for algorithmic trading strategies. International Journal of Research and Applied Innovations, 7(2), 10415–10422.
6. Challa, R. (2024). High-Availability HPC Cluster Design for Mission-Critical Public Infrastructure: Lessons from Energy Grid and Government. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(6), 9305–9309.
7. Soundappan, S. J. (2023). Designing Intelligent Enterprise Platforms Using Machine Learning Driven API Engineering and Cloud Native Security. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9074–9081.
8. Pareek, C. (2023). Unmasking bias: A framework for testing and mitigating AI bias in insurance underwriting models. J Artif Intell Mach Learn & Data Sci, 1(1), 1736–1741.
9. Vemireddy, S. (2022). Modernizing enterprise financial platforms through distributed cloud architectures. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7420–7426.
10. Kundurthy, O. H., Kaata, S. K., Vikram, S., Somayajula, R., & Gangavarapu, R. (2025, September). A Framework for Lightweight Generative AI: Enabling Secure, Scalable, and Cloud-to-Edge Intelligence with MicroLLMs. In 2025 International Conference on Electronics and Computing, Communication Networking Automation Technologies (ICEC2NT) (pp. 1–8). IEEE.
11. Bellundagi, M. (2022). Performance Optimization Techniques for Enterprise Java Applications Using Middleware and Messaging Systems. International Journal of Computer Technology and Electronics Communication, 5(3), 5158–5168.
12. Chundi, V. R. K. (2025). AI-based Sustainable Vehicle Monitoring System for Existing Internal Combustion Vehicles. London Journal of Research In Computer Science and Technology, 25(3), 1–7.
13. Chaba, A. (2021). API-driven enterprise commerce architecture for composable digital ecosystems. International Journal of Engineering & Extended Technologies Research, 3(6), 4082–4086.
14. Mohan, A. (2025). Causal inference in data science: A framework for attribution systems. European Journal of Computer Science and Information Technology, 13(36), 107–113.
15. Narra, S. L. (2025). Demystifying Zero Trust Architecture: Why It's Not Just a Buzzword. International Journal of Computing and Engineering, 7(10), 1–16.
16. Bandaru, P. K. (2021). Leveraging hardware-in-the-loop simulation for continuous automotive software testing. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3730–3735.
17. Abd-Rouf, M. S. K., Adigun, P. O., Alalade, E. O., Oyekanmi, T. T., Faniyi, A. J., Oladapo, B., Awopejo, T. E., Adegoke, O. S., Jamiu, A., Michael, O. B., Obisesan, A., Ajala, S., Adekanye, M. A., Yambali, P. M., & Abd-Rouf, A. B. (2024). From molecular profiling to predictive algorithms: A conceptual machine-learning framework for mechanism-informed therapy selection in multidrug-resistant cancer. International Journal of Science, Research and Technology (IJSRAT), 7(3), 12085–12101.
18. Reddy, G. C. P. (2019). Asymptotic Analysis in Cloud Resource Allocation: Scalability of Virtual Machines, Scheduling Complexity, and Auto-Scaling Mechanisms. Journal of Computational Analysis and Applications, 27(7).
19. Vimal Raja, G. (2024). Intelligent data transition in automotive manufacturing systems using machine learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515–518.
20. Sikarwar, V. (2024). AI-Driven Data Quality Framework for Modern Data Lakes: An Architecture Overview. Journal of Artificial Intelligence General science (JAIGS), 6(1), 662–688. ISSN: 3006-4023.
21. Beeram, S. (2024). AI-driven intelligent traffic management in Microsoft Azure: Predictive routing for resilient cloud applications. International Journal of Advanced Engineering Science and Information Technology, 7(4), 14534–14538.
22. Hoque, M. J., Akter, F., & Mohammad, A. R. (2019). Data-Driven Decision Support System for Restaurant Business Optimization. American Journal of Economics and Business Management, 2(2).
23. Gopinathan, V. R. (2024). Generative AI Enabled Enterprise Cloud Platforms for Intelligent Business Process Automation and Secure Data Integration. International Journal of Emerging Trends in Engineering and Management Research, 9(5), 16445.
24. 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.
25. Das, P., Gogineni, A., Patel, K., & Jain, A. (2025, May). Integration of IoT and Machine Learning to Monitor the Air Quality by Measuring the Block Carbon Levels. In 2025 International Conference on Engineering, Technology & Management (ICETM) (pp. 1–6). IEEE.
26. 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.
27. Alam, A., Gazi, M. S., Abdullah, S. M., Tasnim, M., Himeluzzaman, M., Nabil, M. A., ... & Akter, S. (2021). Quantum-resilient federated intrusion detection: A hybrid quantum-classical framework for safeguarding US critical infrastructure in the post-quantum era. International Journal of Advances in Signal and Image Sciences, 7(1), 57–72.
28. Vani, M., & Dadlani, D. (2023). Smart home – “Aashraya” IoT automation system. International Journal of Computer Technology and Electronics Communication, 6(1), 6393–6403.
29. Raja, G. V. (2020). Metadata gets a makeover: The machine learning approach. International Journal of Computer Technology and Electronics Communication, 3(6), 2900–2903.
30. Ferdausi, N. S., Fatema, N. K., Mahmud, N. M. R., Hoque, N. R., & Ali, N. M. (2025). Transforming telehealth with Artificial Intelligence: Predictive and diagnostic advances in remote patient care. World J Adv Eng Technol Sci, 16(1), 355–65.
31. Koganti, H. (2024). The computational complexity of hybrid algorithms: When branch-and-bound meets machine learning heuristics. International Journal of Research and Applied Innovations (IJRAI), 7(4), 11184–11190.
32. Yepuri, V. K., Polamarasetty, V. K., Donthi, S., & Gondi, A. K. R. (2023). Containerization of a polyglot microservice application using Docker and Kubernetes. arXiv preprint arXiv:2305.00600.
33. 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.
34. Mathew, A., & Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science Vol. 4, 154–164.
35. Padmanabham, S. (2022). Enterprise identity and access management architecture for large financial institutions. International Journal of Research and Applied Innovations, 5(1), 9486–9490.
36. Raja, G. V. (2022). AI Enabled Cloud and Data Engineering Frameworks for Secure, Scalable, and Intelligent Cyber Physical Analytics Systems. International Journal of Research and Applied Innovations, 5(4), 7395–7409.
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