Zero-Trust and AI-Powered Security Framework for Cloud-Native Enterprise Platforms and SAP-Based Digital Ecosystems
Main Article Content
Abstract
This research proposes a zero-trust and AI-powered security framework designed for cloud-native enterprise platforms and SAP-based digital ecosystems. The framework focuses on continuous authentication, identity-based access control, real-time threat detection, and automated response mechanisms. AI techniques such as machine learning and behavioral analytics are integrated to detect anomalies, predict potential security threats, and enhance system resilience. Additionally, the framework incorporates secure data integration, encryption protocols, and identity governance mechanisms to protect sensitive enterprise information.
The proposed architecture aims to enhance security visibility, reduce attack surfaces, and support proactive cybersecurity strategies in modern enterprise environments. The study highlights the potential benefits and challenges associated with implementing AI-driven zero-trust security frameworks within complex cloud-native and SAP ecosystems.
Article Details
Section
How to Cite
References
1. Kamadi, S. (2025). Machine learning and AI architecture: A comprehensive framework for production-grade intelligent systems. World Journal of Advanced Research and Reviews, 27(1), 2789–2799. https://doi.org/10.30574/wjarr.2025.27.1.2654
2. Raju, S., & Sindhuja, D. (2024). Transparent encryption for external storage media with mobile-compatible key management by Crypto Ciphershield. PatternIQ Mining, 1(3), 12–24.
3. Adari, V. K. (2024). How cloud computing is facilitating interoperability in banking and finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465–11471.
4. Bapatla, S. K. S. (2025). Ethical AI in healthcare: A framework for equity-by-design. Journal of Multidisciplinary, 5(7), 143–153.
5. 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.
6. Muthirevula, G. R., Kotapati, V. B. R., & Ponnoju, S. C. (2020). Contract Insightor: LLM-generated legal briefs with clause-level risk scoring. European Journal of Quantum Computing and Intelligent Agents, 4, 1–31.
7. Panda, S. S. (2025). The evolving landscape of hardware and firmware engineering in cloud infrastructure. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(4), 12473–12484.
8. Karnam, A. (2024). Next-gen observability for SAP: How Azure Monitor enables predictive and autonomous operations. International Journal of Computer Technology and Electronics Communication, 7(2), 8515–8524. https://doi.org/10.15680/IJCTECE.2024.0702006
9. Gopinathan, V. R. (2024). Meta-learning–driven intrusion detection for zero-day attack adaptation in cloud-native networks. International Journal of Humanities and Information Technology, 6(01), 19–35.
10. Nallamothu, T. K. (2024). Empowering analysts with AI: Evaluating Nuance DAX Copilot in business intelligence environments. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10624–10633.
11. Grandhe, K. (2025). Designing a scalable data lake architecture on AWS using Glue and S3. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 6(3), 60–63.
12. Kiran, A., & Kumar, S. (2024). A methodology and an empirical analysis to determine the most suitable synthetic data generator. IEEE Access, 12, 12209–12228.
13. Gurajapu, A., Anumolu, S., Garimella, V., Chundi, V. M. S. R., & Gubbala, V. S. A. P. (2025). Goal-driven autonomous agents for SLA-aware network orchestration. Frontiers in Computer Science and Artificial Intelligence, 4(1), 78–83.
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. Prasanna, D., & Manishvarma, R. (2025, February). Skin cancer detection using image classification in deep learning. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1–8). IEEE.
16. Sanepalli, U. R. (2024). Enterprise lakehouse architecture for customer analytics: AI and machine learning–synchronized ingestion and compute optimization. World Journal of Advanced Research and Reviews, 23(2), 2949–2959. https://doi.org/10.30574/wjarr.2024.23.2.2418
17. Sridevi, V., Azath, H., Vijayakumar, R., Anbuselvan, N., Amirthalingam, V., & Arunkumar, S. (2024, April). Augmented reality shopping and IoT-enabled virtual try-on with cloud services for interactive product displays. In 2024 10th International Conference on Communication and Signal Processing (ICCSP) (pp. 880–885). IEEE.
18. Ambati, K. C. (2025). An event-driven architecture for autonomous supply chain risk detection and decision automation. International Journal of Computer Technology and Electronics Communication (IJCTEC), 8(1), 1202–1211.
19. Mulla, F. (2024). Choosing the best architecture for mobile applications. International Journal of Research in Computer Applications and Information Technology, 7, 2350–2363. https://doi.org/10.34218/IJRCAIT_07_02_173
20. Suddala, V. R. A. K. (2024). Driving innovation and compliance in global payment platforms through predictive analytics and DevOps automation. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10662–10672.
21. Konda, S. K. (2024). Sustainable energy optimization through cloud-native building automation and predictive analytics integration. World Journal of Advanced Research and Reviews, 24(3), 3619–3628. https://doi.org/10.30574/wjarr.2024.24.3.3803
22. Ireddy, R. K. (2024). Deep learning architecture for banking risk management: Cloud and AI-driven predictive analytics solution. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. https://doi.org/10.32628/CSEIT24113395
23. Fazilath, M., & Umasankar, P. (2025, February). Comprehensive analysis of artificial intelligence applications for early detection of ovarian tumours: Current trends and future directions. In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1–9). IEEE.
24. Ganesan, G. B. K. (2024). A zero-trust enterprise integration reference architecture for regulated industries. International Journal of Research and Applied Innovations, 7(4), 11086–11095.
25. Nandhini, T., Babu, M. R., Natarajan, B., Subramaniam, K., & Prasanna, D. (2024). A novel hybrid algorithm combining neural networks and genetic programming for cloud resource management. Frontiers in Health Informatics, 13(8).
26. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
27. Ananthakrishnan, V., Kondaveeti, D., & Mohammed, A. S. (2025). GenAI-driven semantic ETL: Synthesizing self-optimizing SQL & PL/SQL. Journal of Knowledge Learning and Science Technology, 4(2), 29–43.
28. Rengarajan, A., & Rajagopalan, S. (2021). Chaos blend LFSR-duo approach on FPGA for medical image security. In Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020 (Vol. 3, p. 155).
29. Nagarajan, C., & Madheswaran, M. (2012). Experimental verification and stability state space analysis of CLL-T series parallel resonant converter. Journal of Electrical Engineering, 63(6), 365–372.
30. Charumathi, M. V., & Inbavalli, M. Familiarizing the pine nut oil by fusing it into different food products. PG and Research Department of Foods & Nutrition, Marudhar Kesari Jain College for Women, Vaniyambadi.
31. Jothilingam, P. (2025). Edge computing for industrial automation and control: Enabling real-time processing, scalable architectures and secure operations. Certified Journal of International Research (CJIR), 5(1), 1-8.