Trustworthy AI in Cloud-Native Data Platforms for Secure and Explainable Smart Grids
Abstract
The rapid adoption of Artificial Intelligence (AI) within cloud-native, data-driven ecosystems has significantly transformed modern enterprise platforms across industries. However, the increasing reliance on AI-driven decision-making systems raises critical concerns related to trust, transparency, security, and ethical accountability. This paper explores the design and implementation of trustworthy AI systems by integrating explainability, security, and governance mechanisms within cloud-native architectures. It highlights the importance of Explainable AI (XAI) techniques in enhancing model interpretability, enabling stakeholders to understand, validate, and trust automated decisions. Additionally, the study examines security challenges such as data breaches, adversarial attacks, and model vulnerabilities, proposing robust mitigation strategies including secure data pipelines, encryption, and zero-trust architectures.The research further investigates how modern cloud-native technologies—such as microservices, containerization, and distributed data platforms—support scalable, resilient, and secure AI deployment. A comprehensive framework is proposed that combines explainability models, privacy-preserving techniques, and real-time monitoring to ensure reliability and compliance in AI systems. This framework aims to bridge the gap between high-performance AI and ethical, transparent operations. The findings demonstrate that integrating explainability and security into AI lifecycle management not only improves system trustworthiness but also enhances regulatory compliance and user confidence. Ultimately, the paper contributes to advancing trustworthy AI practices for next-generation cloud-based intelligent platforms.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
15339-15348 |
Published |
December 19, 2025 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dr.P.Umasankar (%2025). Trustworthy AI in Cloud-Native Data Platforms for Secure and Explainable Smart Grids. International Journal of Science, Research and Technology , Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 15339-15348. https://doi.org/10.15662/IJSRAT.2025.0806007 |
References
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34. Kothokatta, L. (2025). Cross-Platform Automation Strategy for Hybrid OTT and SaaS Applications. International Journal of Computer Technology and Electronics Communication, 8(4), 11106-11116.
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40. 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.
2. Vimal Raja, G. (2024). Intelligent Data Transition in Automotive Manufacturing Systems Using Machine Learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515-518.
3. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
4. Meka, S. (2022). Engineering Insurance Portals of the Future: Modernizing Core Systems for Performance and Scalability. International Journal of Computer Science and Information Technology Research, 3(1), 180-198.
5. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
6. Konda, S. K. (2025). A smart energy consumption system architecture for sustainable semiconductor manufacturing and AI workload operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(2), 9678–9694. https://doi.org/10.15662/IJEETR.2025.070200
7. Ghanta, S. (2023). From Observability to Understanding: Automated Incident Triage Using Large Language Model Reasoning Over Logs, Metrics, and Traces. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(5), 7242-7249.
8. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
9. Appani, C., & Guda, D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security, 2023(7), 20–31. Retrieved from: https://computerfraudsecurity.com/index.php/journal/article/view/661
10. Niture, N., & Abdellatif, I. (2025). A systematic review of factors, data sources, and prediction techniques for earlier prediction of traffic collision using AI and machine learning. Multimedia Tools and Applications, 84(18), 19009-19037.
11. Aashiq Banu, S., Sucharita, M. S., Soundarya, Y. L., Nithya, L., Dhivya, R., & Rengarajan, A. (2020). Robust Image Encryption in Transform Domain Using Duo Chaotic Maps—A Secure Communication. In Evolutionary Computing and Mobile Sustainable Networks: Proceedings of ICECMSN 2020 (pp. 271-281). Singapore: Springer Singapore.
12. Parepalli, S. (2020). Data-Centric Prediction of ETL Throughput and Resource Utilization Using Classical Machine Learning Models. Journal of Artificial Intelligence, Machine Learning and Data Science, 1, 3164-3174.
13. Gopinathan, V. R. (2023). Cloud-First AI Security Architecture for Protecting Enterprise Digital Ecosystems and Financial Networks. International Journal of Research and Applied Innovations, 6(6), 10031-10039.
14. Madheswaran, M., & Vijayakumar, R. (2014, July). Estimation of various parameters of fractured femur with different load conditions using Finite element analysis. In Fifth International Conference on Computing, Communications and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
15. Indurthy, V. S. K. (2024). The surge in AI-powered data analytics revolutionizing business intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13956–13964. https://doi.org/10.15662/IJFIST.2024.0706015
16. Sugumar, R. (2025). Explainable Generative ML–Driven Cloud-Native Risk Modeling with SAP HANA–Apache Integration for Data Safety. International Journal of Research and Applied Innovations, 8(6), 12955-12962.
17. Gentyala, R. (2023). Chameleon signatures for patient privacy: Balancing immutable audit trails with the right to erasure in medical data provenance. European Journal of Advances in Engineering and Technology, 10(4), 115–121.
18. Yamsani, N. (2024). Large Language Models for Intelligent Data Stewardship in Enterprises: Architectures, Provenance, and Evidence-Mapped Governance. International Journal of Computer Technology and Electronics Communication, 7(1), 8210-8219.
19. Tusher, M. I., Hossain, M. R., Akter, A., Mahin, M. R. H., Akhi, S. S., Chy, M. S. K., ... & Shaima, M. (2025). Deep learning meets early diagnosis: A hybrid CNN-DNN framework for lung cancer prediction and clinical translation. International Journal of Medical Science and Public Health Research, 6(05), 63-72.
20. Anand, L. (2024). AI-Powered Cloud Cybersecurity Architecture for Risk Prediction and Threat Mitigation in Healthcare and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(Special Issue 1), 5-12.
21. Varma, K. K., & Anand, L. (2025, March). Deep Learning Driven Proactive Auto Scaler for High-Quality Cloud Services. In International Conference on Computing and Communication Systems for Industrial Applications (pp. 329-338). Singapore: Springer Nature Singapore.
22. Barigidad, S. (2025). Edge-Optimized Facial Emotion Recognition: A High-Performance Hybrid Mobilenetv2-Vit Model. International Journal of AI, BigData, Computational and Management Studies, 6(2), 1-10.
23. Sanepalli, Uttama Reddy. (2023). Distributed Multi-Cloud Data Lake Architecture for Enterprise-Scale Workplace Benefits Analytics: A Federated Approach to Heterogeneous Financial Data Integration. International Journal of Computer Engineering and Technology (IJCET), 14(1), 268-282.
24. 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.
25. Kale, A. (2025). CAC Payback Period Optimization Through Automated Cohort Analysis. International Journal of Management and Business Development, 2(10), 15-20.
26. Pothireddy, S. R. (2025). An efficient and secure data sharing scheme for edge-enabled IoT. International Journal of Advances in Engineering and Management (IJAEM), 7(1), 597–603. https://ijaem.net/issue_dcp/An%20Efficient%20and%20Secure%20Data%20Sharing%20Scheme%20for%20Edge%20Enabled%20IoT.pdf
27. Padala, S. (2024). AI-Powered Intelligent IVR in Healthcare. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(1), 186-191.
28. Boddupally, H. L. (2022). Designing intelligent support bot frameworks for scalable enterprise production systems. Journal of Scientific and Engineering Research, 9(10), 108–115. https://doi.org/10.5281/zenodo.18085293
29. Thota, M. R. (2025). AI-native infrastructure for the autonomous enterprise: Advancing self-optimizing database, big data, and cloud ecosystems. International Journal of Scientific Research in Science and Technology, 12(14), 527–533. https://doi.org/10.32628/IJSRST25121450
30. Md, S., Md Saiful, I., Mohammad, Y., Mahzabin Binte, R., & Jannatul, F. (2024). AI-Driven Business Analytics for Early Prediction and Prevention of High-Cost Healthcare Utilization. AI-Driven Business Analytics for Early Prediction and Prevention of High-Cost Healthcare Utilization, 7(12), 1830-1856.
31. Potel, R. (2024). Enhancing Web Application and API Security Through Intelligent WAFs and Proactive Threat Management. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11641-11651.
32. Bheemisetty, N. (2024). AI-powered recommendation systems: Best practices and real-world applications. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13928–13926. https://doi.org/10.15662/IJFIST.2024.0706011
33. Giri, A., Akib, A. A. S., Hasib, A., Acharya, A., Prithibi, M. A., Rahman, R. H., ... & Taha, H. I. C. (2025, April). Design and development of a cost effective and modular cnc plotter for educational and prototyping applications. In 2025 IEEE 4th International Conference on Computing and Machine Intelligence (ICMI) (pp. 1-6). IEEE.
34. Kothokatta, L. (2025). Cross-Platform Automation Strategy for Hybrid OTT and SaaS Applications. International Journal of Computer Technology and Electronics Communication, 8(4), 11106-11116.
35. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
36. Vankayala, S. C. (2024). Quality intelligence: Leveraging quality analytics to drive business intelligence and customer experience. International Journal of Scientific Research in Science, Engineering and Technology. https://d1wqtxts1xzle7.cloudfront.net/126069916/qualityIntelligence14133-libre.pdf
37. 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.
38. Sundaresh, G., Ramesh, S., Malarvizhi, K., & Nagarajan, C. (2025, April). Artificial Intelligence Based Smart Water Quality Monitoring System with Electrocoagulation Technique. In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (pp. 1-6). IEEE.
39. Akula, A., Budha, G., Bingi, G., Chanda, U., Borra, A. R., Yadav, D. B., & Saravanan, M. (2026). Emotion recognition from facial expressions using CNNs. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(1), 120-125.
40. 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.