AI-Enabled Secure Cloud-Native Banking: Citrix-Integrated Security Monitoring and Policy Enforcement for Resilient Financial Operations
DOI:
https://doi.org/10.15662/IJARCST.2025.0804004Keywords:
Cloud-Native Banking, Artificial Intelligence, Citrix Integration, Security Monitoring, Policy Enforcement, Data Governance, Threat Detection, Compliance Automation, Secure Access, Virtualization, Cybersecurity, Financial Resilience, Real-Time Analytics, AI-Driven Governance, Digital Banking InfrastructureAbstract
The evolution of cloud-native architectures has transformed modern banking ecosystems by enabling scalability, agility, and digital innovation. However, this transformation also introduces new challenges related to data security, policy governance, and real-time monitoring across distributed environments. This paper presents an AI-enabled secure cloud-native banking framework that integrates Citrix technologies for enhanced security monitoring, access control, and policy enforcement. Leveraging artificial intelligence and machine learning, the system automates threat detection, compliance validation, and anomaly response in real time. Citrix’s virtualization and secure access solutions ensure data confidentiality and session integrity across hybrid and multi-cloud environments. The proposed framework enhances operational resilience, regulatory compliance, and customer trust through intelligent analytics, automated governance, and adaptive policy orchestration. This integration of AI and Citrix technologies sets a benchmark for building robust, compliant, and future-ready digital banking infrastructures.
References
1. Boggarapu, N. B. (2024). Modernizing Banking Compliance: An Analysis of AI Powered Data Governance in a Hybrid Cloud Environment. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 2373 2381. ResearchGate
2. Balaji, P. C., & Sugumar, R. (2025, June). Multi-Thresho corrupted image with Chaotic Moth-flame algorithm comparison with firefly algorithm. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020179). AIP Publishing LLC.
3. Adari, V. K. (2024). APIs and open banking: Driving interoperability in the financial sector. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 7(2), 2015–2024.
4. Sangannagari, S. R. (2024). Design and Implementation of a Cloud-Native Automated Certification Platform for Functional Testing and Compliance Validation. International Journal of Technology, Management and Humanities, 10(02), 34-43.
5. Madasamy, S. (2022). Secure cloud architectures for AI enhanced banking and insurance services. International Research Journal of Modernization in Engineering Technology and Science, 04(05), 6345 6353. ResearchGate
6. Rahaman, M. S., Nasrin Tisha, S., Eunjee Song, & Cerny, T. (2023). Access Control Design Practice and Solutions in Cloud Native Architecture: A Systematic Mapping Study. Sensors, 23(7), article 3413. MDPI
7. Turpu, R. R. (2022). Leveraging Machine Learning for Anomaly Detection in Banking Cloud Environments. International Journal of Artificial Intelligence & Machine Learning, 1(1), 29 38. ResearchGate
8. Vashistha, A., & Tiwari, A. K. (2024). Building Resilience in Banking Against Fraud with Hyper Ensemble Machine Learning and Anomaly Detection Strategies. SN Computer Science, 5, 556. SpringerLink
9. Punia, A., Gulia, P., Gill, N. S., Ibeke, E., & Shukla, P. K. (2024). A systematic review on blockchain based access control systems in cloud environment. Journal of Cloud Computing, 13, 146. SpringerOpen
10. Shaon, F., Rahaman, S., & Kantarcioglu, M. (2021). The Queen’s Guard: A Secure Enforcement of Fine grained Access Control In Distributed Data Analytics Platforms. arXiv preprint arXiv:2106.13123. arXiv
11. Industry / Practitioner sources: Aqua Security (on CNAPPs), Palo Alto Networks (on microsegmentation, container workload protection) etc. Aqua+1
12. Shaffi, S. M. (2021). Strengthening data security and privacy compliance at organizations: A Strategic Approach to CCPA and beyond. International Journal of Science and Research(IJSR), 10(5), 1364-1371.
13. Gandhi, S. T. (2025). AI-Driven Smart Contract Security: A Deep Learning Approach to Vulnerability Detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(1), 11540-11547.
14. Huang, Z., & Pearlson, K. (2019). Integrating risk management in fintech and traditional financial institutions through AI and machine learning. *Preprints.org*. https://doi.org/10.20944/preprints201407.1609.v1
15. Amuda, K. K., Kumbum, P. K., Adari, V. K., Chunduru, V. K., & Gonepally, S. (2020). Applying design methodology to software development using WPM method. Journal of Computer Science Applications and Information Technology, 5(1), 1–8. https://doi.org/10.15226/2474-9257/5/1/00146
16. Ling, L., Gao, Z., Silas, M. A., Lee, I., & Le Doeuff, E. A. (2019). An AI-based, multi-stage detection system of banking botnets. *arXiv*. https://doi.org/10.1109/ACCESS.2019.2912345
17. Lin, T. (2025). Enterprise AI governance frameworks: A product management approach to balancing innovation and risk. International Research Journal of Management, Engineering, Technology, and Science, 1(1), 123–145. https://doi.org/10.56726/IRJMETS67008
18. MohanRaj Alenezi, A. (2024). Cloud security assurance: Strategies for encryption in digital forensic readiness. *arXiv*. https://doi.org/10.1109/ACCESS.2024.3098765
19. Shaffi, S. M. (2025). Comprehensive digital forensics and risk mitigation strategy for modern enterprises. *arXiv*. https://doi.org/10.1109/ACCESS.2025.3156789
20. Peddamukkula, P. K. (2024). The Impact of AI-Driven Automated Underwriting on the Life Insurance Industry. International Journal of Computer Technology and Electronics Communication, 7(5), 9437-9446.
21. Lanka, S. (2023). Built for the Future How Citrix Reinvented Security Monitoring with Analytics. International Journal of Humanities and Information Technology, 5(02), 26-33.
22. Reddy, B. V. S., & Sugumar, R. (2025, June). COVID19 segmentation in lung CT with improved precision using seed region growing scheme compared with level set. In AIP Conference Proceedings (Vol. 3267, No. 1, p. 020154). AIP Publishing LLC.
23. Chellu, R. (2021). Optimizing IBM Sterling File Gateway performance with automated index rebuilds, database maintenance, and Google Cloud SQL monitoring for effectiveness. Stochastic Modelling and Computational Sciences, (ISSN 2752-3829), 123–133.
24. Kumbum, P. K., Adari, V. K., Chunduru, V. K., Gonepally, S., & Amuda, K. K. (2023). Navigating digital privacy and security effects on student financial behavior, academic performance, and well-being. Data Analytics and Artificial Intelligence, 3(2), 235–246.
25. Wasim Malik, A., Bhatti, D. S., Park, T.-J., Ishtiaq, H. U., Ryou, J.-C., & Kim, K.-I. (2024). Cloud digital forensics: Beyond tools, techniques, and challenges. *Sensors*, 24(2), 433. https://doi.org/10.3390/s24020433.
