Architecting Self-Governing Digital Enterprises with Intelligent Analytics and Secure Cloud Platforms
DOI:
https://doi.org/10.15662/IJARCST.2023.0603010Keywords:
Self-Governing Digital Enterprise, Intelligent Analytics, Secure Cloud Platforms, Artificial Intelligence, Machine Learning, Cloud Computing, Digital Transformation, Cybersecurity, Business Intelligence, Automation, Predictive Analytics, Enterprise Architecture, Data Governance, Cloud Security, Autonomous SystemsAbstract
The rapid evolution of digital technologies has transformed traditional business operations into highly interconnected and data-driven ecosystems. Self-governing digital enterprises represent the next stage of organizational evolution, where intelligent systems autonomously monitor, analyze, and optimize business processes with minimal human intervention. This research explores the architectural foundations of self-governing enterprises by integrating intelligent analytics and secure cloud platforms. Intelligent analytics utilizes artificial intelligence, machine learning, predictive modeling, and real-time data processing to support autonomous decision-making and operational efficiency. Secure cloud platforms provide scalable infrastructure, robust cybersecurity mechanisms, and seamless integration capabilities necessary for enterprise-wide digital transformation. The study examines how organizations can design adaptive architectures that enable continuous learning, automated governance, proactive risk management, and data-driven innovation. Furthermore, the research highlights the role of cloud-native technologies, zero-trust security frameworks, and intelligent automation in creating resilient digital ecosystems. Through a comprehensive review of existing literature and methodological analysis, the study identifies critical success factors, implementation challenges, and future opportunities associated with self-governing enterprises. The findings suggest that the convergence of intelligent analytics and secure cloud environments significantly enhances organizational agility, operational excellence, compliance management, and strategic competitiveness in the digital economy while supporting sustainable and scalable business growth
References
1. Socrates, S., Shanmugapriya, M., Murugeshwari, B., & Angalaeswari, S. (2024). Efficient Design for Implantable Device Constant Current Induction Doubly Fed Generating Incorporating Grid Connectivity. In Intelligent Solutions for Sustainable Power Grids (pp. 382-392). IGI Global Scientific Publishing.
2. 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.
3. Rajendran, S., Sundarapandi, A. M. S., Krishnamurthy, A., & Thanarajan, T. (2022). An intelligent face recognition technology for iot-based smart city application using condition-cnn with foraging learning pso model. International Journal of Pattern Recognition and Artificial Intelligence, 36(14), 2256018.
4. Anand, L., & Syed Ibrahim, S. P. (2018). HANN: a hybrid model for liver syndrome classification by feature assortment optimization. Journal of medical systems, 42(11), 211.
5. Adepu, G. (2022). Machine learning-driven environmental monitoring systems for real-time regulatory compliance and risk detection. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 22–37.
6. Watham, S. D., & Vimal, V. R. (2013). Design and Implementation of Data Sanitization Technique For Effective Filtering With Enhanced Medical Support System in Cloud Architecture Diagram. International Journal of Emerging Technology and Advanced Engineering, 3(12), 471-473.Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.
7. Shewale, V. (2022). IT/OT Convergence: A Zero Trust Reference Architecture for the Energy Sector. International Journal of Science, Research and Technology, 5(5), 8494-8502.
8. Parasa, M. (2022). Addressing the underutilization of exit interview data: A structured AI-assisted framework for actionable workforce insights in SAP SuccessFactors. Global Scientific and Academic Research Journal of Multidisciplinary Studies, 1(6), 42–52. https://gsarpublishers.com/abstract-2326/
9. Raja, G. V. (2022). Integrating network forensics with data mining for advanced cybercrime investigation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5321–5326.
10. Padwal, R. A., & Mulajkar, R. M. (2016). A COMPARATIVE STUDY OF IMAGE SEGMENTATION METHOD. International Journal of Advance Research in Engineering, Science & Technology, 3(7), 151-163.
11. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
12. Rajasekar, M., Aruldoss, A. C., & Bennet, M. A. (2018). A novel method to detect corrosion in underwater infrastructure using an image processing. ARPN Journal of Engineering and Applied Science, 13(7), 2556-2561.
13. Subramanyam, S. P. (2022). CyberArk integrated privileged access security for Azure DevOps environments. International Journal of Research and Applied Innovations (IJRAI), 5(1), 9478–9485. https://doi.org/10.15662/IJRAI.2022.0501008
14. Namdeo, A. (2022). Graph neural networks for real-time supply chain risk. International Journal of Humanities and Information Technology, 4(1–3), 175–192.
15. Fung, J., & Panyala, V. R. (2020). Automating multi-region scalable CI/CD framework for managing AWS CloudWatch alerts. International Journal of Engineering & Extended Technologies Research, 2(5), 1854–1858.
16. Kasireddy, J. R. (2022). From Raw Trades to Audit-Ready Insights Designing Regulator-Grade Market Surveillance Pipelines. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4609-4616.
17. Adepu, R. (2022). Building secure multi-cloud infrastructure for mission-critical enterprise workloads. The International Journal of Research Publications in Engineering, Technology and Management, 5(5), 14–32.
18. Narayanan, S. (2022). Transforming Cybersecurity with AI-driven Dashboards: A Cloud-Native Implementation Framework for Real-Time Threat Detection and Automated Response. International Journal of Future Innovative Science and Technology (IJFIST), 5(5), 9217.
19. Sudarsan, V., & Sugumar, R. (2019). Building a distributed K‐Means model for Weka using remote method invocation (RMI) feature of Java. Concurrency and Computation: Practice and Experience, 31(14), e5313.
20. V. B. Sarabu. (2018). Building foundational data integrity in enterprise retail systems: A structured approach to early-stage data governance. International Journal of Research Publications in Engineering, Technology and Management, 1(1), 2457–2465
21. Bhende, M., Thakare, A., Saravanan, V., Anbazhagan, K., Patel, H. N., & Kumar, A. (2022). [Retracted] Attention Layer‐Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer. BioMed Research International, 2022(1), 3947434.
22. Sengupta, J., & Alzbutas, R. (2022). Intracranial hemorrhages segmentation and features selection applying cuckoo search algorithm with gated recurrent unit. Applied Sciences, 12(21), 10851.
23. Kunadi, S. K. (2022). Designing high-performance data pipelines using Snowflake and cloud-native architectures. International Journal of Research and Applied Innovations (IJRAI), 5(6), 8220–8230.
24. Prasad, P. K. (2021). Kubernetes everywhere: Operating hybrid and multi-cloud infrastructure at scale. International Journal of Engineering & Extended Technologies Research, 3(4), 3393–3401.
25. Dama, H. B. (2023). Designing highly available multi-cloud database architectures for global financial services. International Journal of Research and Applied Innovations, 6(1), 8329-8336.
26. Boddupally, H. L. (2022). Architectural-driven intelligent refactoring for resilient cloud-native. NET systems. Available at SSRN 6270479.
27. Raj, A. A., & Sugumar, R. (2022, October). Estimation of Social Distance for COVID19 Prevention using K-Nearest Neighbor Algorithm through deep learning. In 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon) (pp. 1-6). IEEE.
28. Dhinakaran, D., Prathap, P. J., Selvaraj, D., Kumar, D. A., & Murugeshwari, B. (2022). Mining privacy-preserving association rules based on parallel processing in cloud computing. International Journal of Engineering Trends and Technology, 70(3), 284-294.
29. Pasumarthi, H. (2023). A Deep Dive into Enterprise B2B Integrations: Designing High-Availability File and API Workflows with IBM Datapower and Autosys. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(2), 8363-8370.
30. Vimal, V. R., Anandan, P., & Kumaratharan, N. (2022). Heart Disease Diagnosis Using Electrocardiography (ECG) Signals. Intelligent Automation & Soft Computing, 32(1).
31. Vanitha, C., Sanmugam, A., Yogananth, A., Rajasekar, M., Kuppusamy, P. G., & Devasagayam, G. (2022). A facile synthesis of polyaniline-WO3 hybrid nanocomposite for enhanced dopamine detection. Materials Letters, 328, 133149.
32. Mathew, A. (2022). Leveraging Big Data Analytics to Power AI and ML (Machine Learning) Automation. Educational Research (IJMCER), 4(5), 131-134.
33. Bharti, N. S., & Mulajkar, R. M. (2015). Detection and classification of plant diseases. International Research Journal of Engineering and Technology, 2(2), 2267-2272.
