AI-Driven Enterprise Transformation: Integrating Cloud-Native Architecture Data Governance Cybersecurity and Intelligent Automation
Abstract
Artificial Intelligence (AI) has emerged as a transformative force that is reshaping modern enterprises by enabling intelligent decision-making, operational efficiency, and digital innovation. Organizations across industries are increasingly adopting AI-driven enterprise transformation strategies that integrate cloud-native architecture, robust data governance frameworks, advanced cybersecurity mechanisms, and intelligent automation technologies. This integrated approach supports agility, scalability, resilience, and data-driven business models while addressing the growing complexity of digital ecosystems. Cloud-native architectures provide the foundational infrastructure required for rapid deployment, scalability, and continuous innovation. Data governance ensures the quality, security, compliance, and ethical use of organizational data, which serves as the primary fuel for AI systems. Cybersecurity plays a critical role in protecting digital assets, mitigating risks, and maintaining trust in interconnected environments. Intelligent automation, powered by machine learning, robotic process automation, and cognitive technologies, enhances productivity and enables organizations to streamline operations. This essay explores the interconnected roles of these technological domains in facilitating enterprise transformation. It examines existing scholarly perspectives, analyzes the mechanisms through which organizations integrate these capabilities, and proposes a comprehensive research methodology for investigating AI-driven transformation initiatives. The study contributes to a deeper understanding of how enterprises can achieve sustainable competitive advantage through the strategic convergence of AI, cloud computing, governance, security, and automation
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 5 No. 3 (2022): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
7769-7776 |
Published |
May 10, 2022 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Mahasweta Devi (%2022). AI-Driven Enterprise Transformation: Integrating Cloud-Native Architecture Data Governance Cybersecurity and Intelligent Automation. International Journal of Science, Research and Technology , Vol. 5 No. 3 (2022): International Journal of Science, Research and Technology (IJSRAT) , pp. 7769-7776. https://doi.org/10.15662/IJSRAT.2022.0503001 |
References
2. Vankayala, S. C. (2017). Embedding Quality Intelligence in API-First Architectures: Assurance Frameworks for Real-Time Financial Transactions. Journal of Scientific and Engineering Research, 4(6), 227-241.
3. Yamsani, N. (2020). Architecting Enterprise-Wide Master Data Platforms for Cloud-Enabled Organizations Using EBX-Centered Governance and Integration Design. European Journal of Advances in Engineering and Technology, 7(8), 150-162.
4. Boddupally, H. L. (2021). A telemetry-centric approach to identifying recurrent defect structures in software systems. Available at SSRN 6270478.
5. 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.
6. Adepu, R. (2021). Architecting Scalable Virtualized Data Center Infrastructures for High-Availability Enterprise Systems. International Journal of Research and Applied Innovations, 4(2), 3442-3455.
7. Adepu, G. (2021). Zero-Trust Digital Government Platforms: Secure Identity, API Governance, and Cloud-Native Service Architecture. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(3), 3089-3093.
8. Shewale, V. (2022). Securing Remote Access to SCADA During the Pandemic Era. International Journal of Computer Technology and Electronics Communication, 5(2), 4844-4851.
9. Parasa, M. (2021). Encryption-aware data integrity and quality controls in SAP SuccessFactors integrations using machine learning and cryptographic hash chains for tamper detection. International Journal of Computer Technology and Electronics Communication, 4(6), 4304–4316. https://doi.org/10.15680/IJCTECE.2021.0406014
10. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.
11. Wen, B., Li, Y., & Bresler, Y. (2020). Image recovery via transform learning and low-rank modeling: The power of complementary regularizers. IEEE Transactions on Image Processing, 29, 5310-5323.
12. Rajasekar, M., Celine Kavida, A., & Anto Bennet, M. (2020). A pattern analysis based underwater video segmentation system for target object detection. Multidimensional Systems and Signal Processing, 31(4), 1579-1602.
13. Santhoshini, G., & Anbazhagan, K. (2014, February). An object based software tool for software measurement. In International Conference on Information Communication and Embedded Systems (ICICES2014) (pp. 1-5). IEEE.
14. 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
15. Namdeo, A. (2021). Quantum-accelerated cloud BI query optimization. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(5), 3715–3724.
16. Panyala, V. R. (2021). Innovative reliability engineering solutions for internet-scale cloud consumer platforms. International Journal of Computer Technology and Electronics Communication, 4(1), 1–13.
17. 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.
18. Choudhury, P., & Imtiaz, N. (2020). Overcoming Data Excess to Improve Decision-Making and Information Systems Plans for Organizational Performance. Journal of Primeasia, 1(3), 1-7.
19. Jagannathan, P., Gurumoorthy, S., Stateczny, A., Divakarachar, P. B., & Sengupta, J. (2021). Collision-aware routing using multi-objective seagull optimization algorithm for WSN-based IoT. Sensors, 21(24), 8496.
20. Vayyasi, N. K. (2020). Intelligent transaction prediction and fraud detection in crypto markets using Java and generative AI. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 3(1), 2765–2779.
21. Mulajkar Rahul, M., & Gohakar, D. M. V. Design of Efficient Method for Extraction of Scene Depth Information for 2D-TO-3D Conversion.
22. Sugumar, R., & Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.