Scalable AI-Powered Enterprise Transformation: Integrating Data Governance, Cloud Performance Engineering, and Secure Business Applications
DOI:
https://doi.org/10.15680/IJCTECE.2020.0405008Keywords:
Artificial Intelligence, Enterprise Transformation, Data Governance, Cloud Performance Engineering, Business Applications, Cybersecurity, Digital Transformation, Cloud Computing, Data Management, Enterprise Architecture, AI Integration, Organizational InnovationAbstract
The rapid advancement of Artificial Intelligence (AI) has transformed how enterprises manage operations, make decisions, and deliver services. Organizations increasingly adopt AI-driven technologies to improve efficiency, automate processes, and gain competitive advantages. However, successful enterprise transformation requires more than AI implementation; it demands the integration of robust data governance frameworks, cloud performance engineering practices, and secure business applications. This study explores a scalable AI-powered enterprise transformation model that combines these critical components to enhance organizational performance and resilience. Data governance ensures data quality, compliance, accessibility, and ethical use of information, forming the foundation for reliable AI systems. Cloud performance engineering provides scalable infrastructure, resource optimization, and high availability to support AI workloads across dynamic business environments. Secure business applications protect organizational assets from cyber threats while enabling seamless collaboration and innovation. The research examines existing literature, identifies key challenges and opportunities, and proposes a comprehensive methodology for integrating these domains within enterprise ecosystems. Findings indicate that organizations adopting a holistic approach achieve improved operational efficiency, stronger regulatory compliance, enhanced security, and better decision-making capabilities. The study contributes to the growing body of knowledge on digital transformation by presenting a framework that supports sustainable, scalable, and secure AI-enabled enterprise modernization.References
1. Vayyasi, N. K. (2020). Decoding token volatility patterns with generative models deployed on cloud-native Java environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(4), 1552–1565.
2. Mathew, A. (2021, March). Sixth-Gen Wireless Tech with Optical Wireless Communication. In Proceedings of International Conference on Sustainable Expert Systems: ICSES 2020 (pp. 119-124). Singapore: Springer Singapore.
3. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
4. Sarabu, V. B. (2018). A framework-driven approach to data validation and reconciliation for operational accuracy. International Journal of Research and Applied Innovations, 1(1), 2130-2140.
5. Parasa, M. (2021). TEAL-HCM: A tamper-evident AI lineage framework for securing cloud-based SAP Success Factors integrations. SAMRIDDHI: A Journal of Physical Sciences, Engineering and Technology, 13(2), 180–194. https://doi.org/10.18090/samriddhi.v13i02.18
6. Murugeshwari, B., Sudharson, K., Panimalar, S. P., Shanmugapriya, M., & Abinaya, M. (2020). SAFE–Secure Authentication in Federated Environment using CEG Key code.
7. Sengupta, J. (2019). Automated Inception Network based Cardiac Image Segmentation Analysis. International Journal of Advanced Science and Technology, 28(20), 953-962.
8. 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.
9. 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.
10. Yamsani, N. (2016). Advancing Data Consistency and Control Across Global Financial Institutions by Enterprise Master Data Platforms. International Journal of Technology, Management and Humanities, 2(01), 22-35.
11. Pushparathi, V. G., Sudha, M., David, D. J., Anbazhagan, K., & Vethamani, S. E. (2020). A Continuous Decision Based Multi Kernel Median Filter for Noise Removal on Brain MRI Images. Advanced imaging, 1(3), 5.
12. Mathew, A. (2020). Wavelet-based visual share creation for image security. Int. J. Eng. Trends. Appl.(IJETA), 7(4), 29-34.
