Articles Open Access
Zero Trust Architecture for Machine Learning Enabled Digital Enterprise Security and Adaptive Operational Resilience
Department of Computer Sciences, University of Botswana, Gaborone, Botswana
Abstract
The rapid adoption of cloud computing, machine learning, distributed applications, remote workforces, and interconnected enterprise platforms has increased the complexity of digital security and operational resilience. Traditional perimeter-based security models are increasingly inadequate because enterprise resources, users, applications, and data are distributed across heterogeneous environments. This study proposes a Zero Trust Architecture (ZTA) integrated with machine learning (ML) to strengthen digital enterprise security and enable adaptive operational resilience. The proposed framework continuously verifies identities, evaluates contextual risk, monitors behavioral patterns, and dynamically adjusts access privileges according to changing security conditions. Machine learning models are employed to identify anomalous user activities, detect deviations in application behavior, classify potential threats, and support adaptive risk scoring. The architecture combines identity-aware access control, micro-segmentation, continuous monitoring, encryption, policy enforcement, security analytics, and automated incident response. A research methodology based on architecture design, synthetic enterprise security data generation, ML model development, simulation, and comparative evaluation is presented. Performance is assessed using detection accuracy, precision, recall, F1-score, false-positive rate, response time, policy enforcement effectiveness, and resilience indicators. The proposed approach provides a foundation for continuously adaptive enterprise protection while supporting secure business operations across hybrid, multi-cloud, and distributed digital environments.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 8 |
| Number | 6 |
| Pages | 15697-15704 |
| Published | 2024-12-10 |
| Issue | Vol. 8 No. 6 (2024): International Research Journal of Innovative Engineering (IRJIE) |
References
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2. Vedula, J. (2024). A security-integrated agile governance model for IT and operational technology modernisation in the oil and gas sector. International Journal of Research and Applied Innovations, 7(4), 11191–11196.
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. Bandhela, R. R., Onteddu, A. R., & Kundavaram, R. R. (2022). Enhancing precision healthcare machine learning for advanced diagnostics and personalized treatment. South Eastern European Journal of Public Health. https://doi.org/10.70135/seejph.vi.6690
5. Koganti, H. (2024). Beyond reactive scaling: A review of AI-driven proactive and context-aware auto-scaling in cloud-edge environments. International Journal of Engineering & Extended Technologies Research, 6(3), 8175–8183.
6. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
7. Ramasamy, M. (2022). A reference architecture for AI-driven network assurance in large-scale enterprise networks. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4336–4346.
8. Badam, L. R. (2023). Explainable AI framework for real-time financial fraud detection in banking systems. International Journal of Emerging Trends in Engineering and Management Research, 8(2), 13241–13251.
9. 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.
10. Rahul Rao Juvvadi. (2024). Large language models in FP&A: An architecture for grounded variance commentary and driver-based narrative generation. Enterprise Development and Microfinance, 34(1), 71–84.
11. Punithavathi, R., Selvi, R. T., Latha, R., Kadiravan, G., Srikanth, V., & Shukla, N. K. (2022). Robust node localization with intrusion detection for wireless sensor networks. Intelligent Automation and Soft Computing, 33(1), 143–156.
12. Anand, L. (2023). Leveraging Artificial Intelligence for Enterprise Modernization with Intelligent Automation and Cloud Native Computing. International Journal of Future Innovative Science and Technology (IJFIST), 6(5), 11375.
13. Jain, R. (2018). Beyond stateless: A production architecture for running distributed databases on Kubernetes at scale. International Journal of Emerging Trends in Engineering and Management Research, 3(5), 4165–4172.
14. Gowda, M. K. S. (2024). Leveraging machine learning to enhance accuracy and efficiency in regulatory compliance. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10683–10692.
15. Raja, G. V., & Mali, R. K. (2021). Federated learning frameworks for privacy-preserving artificial intelligence applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(3), 4946-4950.
16. Matrouk, K., V, S., Kumar, S., Bhadla, M. K., Sabirov, M., & Saadh, M. J. (2023). Deep learning-based dynamic user alignment in social networks. ACM Journal of Data and Information Quality, 15(3), 1–26.
17. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of Medical Systems, 43(4), 96.
18. Gopinathan, V. R. (2023). Intelligent Cloud Security through Continuous Threat Detection and Risk Assessment. International Research Journal of Innovative Engineering, 7(6), 13571-13581.
19. Bellundagi, M. (2023). Design of an intelligent clinical decision support system using machine learning techniques. International Journal of Research and Applied Innovations, 6(6), 10075–10081.
20. Ambati, K. C. (2024). Enterprise-wide procurement consolidation: Ivalua-SAP-EDW integration architecture for global supply chain excellence. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(4), 14309–14318.
21. Mathew, A. (2021). Artificial intelligence for offence and defense-the future of cybersecurity. Educational Research, 3(3), 159-163.
22. Manda, P. (2024). Oracle E-Business Suite modernization: The transition from 12.1.3 to 12.2.8. International Journal of Research and Applied Innovations, 7(3), 10838–10842.
23. 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.
24. Badam, L. R. (2024). AI-based early warning system for financial scams targeting consumers. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(3), 10593–10602.
25. Vemireddy, S. (2023). Building resilient enterprise platforms using event-driven and distributed computing models. International Journal of Research and Applied Innovations, 6(6), 10106–10112.
26. Bitragunta, S. L. V. (2022, November 20). High level modeling of high-voltage gallium nitride (GaN) power devices for sophisticated power electronics applications. Journal of Artificial Intelligence, Machine Learning and Data Science, 1(1), 2011–2015.
27. Selvarajan, K. (2023). Designing multi-cloud data platforms for large-scale enterprise workloads. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(1), 5992–6002.
28. Reddy, K. V. (2024). Accelerating functional coverage closure through iterative machine learning. International Journal of Computer Applications & Information Technology, 7, 1401–1411.
29. Padmanabham, S. (2023). Secure API gateway architecture for open banking. International Journal of Computer Technology and Electronics Communication, 6(6), 8166–8174.
30. Khare, A., Khare, S., Goel, O., & Goel, P. (2024). Strategies for successful organizational change management in large digital transformation. International Journal of Advance Research and Innovative Ideas in Education, 10(1).
31. Bandaru, P. K. (2022). Hardware-in-the-loop testing for connected vehicles: Enhancing software reliability through continuous validation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(2), 4645–4651.
32. Sivakumer, D. (2023). Depth of ServiceNow platform utilization and business delivery performance in enterprise project management. International Journal of Computer Technology and Electronics Communication, 6(6), 8167–8177.
33. Sandhu, Y. S. (2024). Real time ETL optimization using change data capture incremental. International Journal of Emerging Trends in Engineering and Management Research, 9(3), 15711–15721.
34. Sugumar, R. (2022). Explainable Deep Learning Framework for Financial Fraud Detection and Intelligent Risk Analysis. International Journal of Emerging Trends in Engineering and Management Research, 7(5), 12528.
35. Sengupta, B., & Lakshminarayanan, A. (2021). DistriTrust: Distributed and low-latency access validation in zero-trust architecture. Journal of Information Security and Applications, 63, 103023. https://doi.org/10.1016/j.jisa.2021.103023
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