AI Driven CI CD Pipelines for SAP Cloud Digital Banking with Cyber Defense Across Enterprise and Telecom Systems
DOI:
https://doi.org/10.15662/IJEETR.2025.0706046Keywords:
AI-Driven CI/CD, SAP Digital Banking, Cloud-Native DevSecOps, Real-Time Data Analytics, Machine Learning Pipelines, Cyber Defense, Enterprise Microservices, Mobile Banking Systems, Telecom Integration, Secure Cloud ArchitectureAbstract
Artificial Intelligence (AI)–driven Continuous Integration and Continuous Deployment (CI/CD) pipelines are rapidly transforming cloud-native digital banking ecosystems by enabling intelligent automation, resilience, and security at enterprise scale. This paper presents a comprehensive architectural perspective on AI-enabled CI/CD pipelines designed for SAP–enabled cloud digital banking platforms operating across enterprise mobile, telecom, and microservices-based environments. The proposed approach integrates real-time data engineering, machine learning–driven pipeline intelligence, and adaptive cyber-defense mechanisms to support high-availability, regulatory compliance, and zero-trust security requirements.
The framework leverages machine learning models for predictive build optimization, automated testing prioritization, anomaly detection in deployment pipelines, and real-time performance tuning of SAP workloads. Streaming data from mobile banking applications and telecom microservices is continuously analyzed to enable proactive threat detection, fraud prevention, and policy-aware deployment decisions. AI-powered security controls embedded within the CI/CD lifecycle provide continuous risk assessment, secure code validation, and automated incident response across hybrid and multi-cloud infrastructures.
By unifying SAP platforms, cloud-native DevSecOps practices, and AI-driven analytics, the proposed solution enhances deployment velocity, operational reliability, and cyber resilience for modern digital banking systems. The paper demonstrates how intelligent CI/CD pipelines can serve as a foundational enabler for secure, scalable, and real-time financial services across interconnected enterprise and telecom ecosystems
References
1. Sriramoju, S. (2025). Implementing CI/CD Pipelines for MuleSoft APIs Using Jenkins, GitHub, and Azure DevOps. Journal of Computer Science and Technology Studies, 7(8), 77–82.
2. Panchakarla, S. K. (2025). Designing carrier-grade microservices for telecom: Ensuring availability and scale in order fulfillment systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(5), 10600–10604.
3. Mogili, V. B. (2024). Design and evaluation of secure healthcare applications built on Microsoft Power Platform. International Journal of Research Publications in Engineering, Technology and Management, 7(3), 10534–10545.
4. Navandar, P. (2022). The evolution from physical protection to cyber defense. International Journal of Computer Technology and Electronics Communication, 5(5), 5730–5752.
5. Genne, S. (2024). Architecting real-time data synchronization in education platforms using GraphQL. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 7(4), 14475–14485.
6. 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.
7. Amarapalli, L., Keezhadath, A. A., & Kanka, V. (2024). Impact of GAMP 5 guidelines on validation of AI-powered medical device software. Journal of AI-Powered Medical Innovations, 3(1), 126–136.
8. Chennamsetty, C. S. (2023). Standardizing software delivery: Unified data models and scalable infrastructure for subscription ecosystems. International Journal of Computer Technology and Electronics Communication, 6(2), 6658–6665.
9. Gurajapu, A., & Garimella, V. (2025). Green-cloud scheduling: Minimizing energy use in multi-cloud operations within SLAs. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(1), 9336–9339.
10. Surisett, L. S. (2024). AI-driven API security: Architecting resilient gateways for hybrid cloud ecosystems. International Journal of Research Publications in Engineering Technology and Management (IJRPETM), 7(1), 9964–9974.
11. Gangina, P. (2023). Serverless architecture patterns for high-throughput financial transaction processing. International Journal of Research and Applied Innovations (IJRAI), 6(4), 9232–9245.
12. Natta, P. K. (2024). Designing trustworthy AI systems for mission-critical enterprise operations. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13828–13838. https://doi.org/10.15662/IJFIST.2024.0706003
13. Anumula, S. R. (2023). Enterprise architecture for real-time intelligence in distributed environments. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(4), 7301–7312.
14. Gopinathan, V. R. (2024). Real-time financial risk intelligence using secure-by-design AI in SAP-enabled cloud digital banking. International Journal of Computer Technology and Electronics Communication, 7(6), 9837–9845.
15. Ponugoti, M. (2023). Frameworks for ensuring compliance in digital platform governance. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7575–7586.
16. Ramidi, M. (2024). Cross-platform performance optimization strategies for large-scale mobile applications. International Journal of Humanities and Information Technology (IJHIT), 6(1), 44–63.
17. Sugumar, R. (2024). AI-driven cloud framework for real-time financial threat detection in digital banking and SAP environments. International Journal of Technology Management and Humanities, 10(04), 165–175.
18. Panda, M. R., & Chinthalapelly, P. R. (2023). Banking sandbox evaluation for open banking ecosystems using agent-based modeling. European Journal of Quantum Computing and Intelligent Agents, 7, 66–100.
19. Chivukula, V. (2024). The role of adstock and saturation curves in marketing mix models: Implications for accuracy and decision-making. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(2), 10002–10007.
20. Rajasekharan, R. (2024). The evolving role of Oracle Cloud DBAs in the AI era. International Journal of Computer Technology and Electronics Communication (IJCTEC), 7(6), 9866–9879.
21. Kavuri, S. (2022). Large Language Model (LLM)-Based Automation for Software Test Script Generation. Computer Fraud & Security, 17-28.
22. Makkena, B. (2023). PromptOps: Building prompt-driven DevOps workflows for infrastructure-as-code automation. International Journal of Communication Networks and Information Security, 15(10), 12–30.
23. Gopisetty, S. (2024). Why was my transaction flagged? Building counterfactual stories for AI threat detection in real-time ERP systems. QIT Press - International Journal of Artificial Intelligence Research and Development (QITP-IJAIRD), 5(1), 20–56.
24. Polamreddy, V. R. (2025). Incremental Change Processing and Financial Data Integrity in Enterprise Cloud Adoption Programs. International Journal of Research and Applied Innovations, 8(1), 11749-11761.
25. Manda, P. (2023). Leveraging AI to Improve Performance Tuning in Post-Migration Oracle Cloud Environments. International Journal of Research Publications in Engineering Technology and Management (IJRPETM), 6(3), 8714–8725.
26. Appani, C. (2022). Graph Neural Networks for Dynamic Malware Behaviour Analysis and Classification in Advanced Persistent Threats (APT). International Journal of Communication Networks and Information Security.
27. Kotla, M. R. T. (2025). Enterprise integration lessons from four digital frontlines: A comparative analysis of modern IT ecosystems. International Journal of Research Publications in Engineering, Technology and Management, 8(3), 32–42.
28. Gajula, S. (2023). A Review of Anomaly Identification in Finance Frauds using Machine Learning System. International Journal of Current Engineering and Technology, 13(06).
29. Shewale, V. (2023). AI and Machine Learning for Anomaly Detection in ICS Environments. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 6(3), 11631.
30. Parasa, M. (2022). Addressing the underutilization of exit interview data: A structured AI-assisted framework for actionable workforce insights in SAP SuccessFactors. Global Scientific and Academic Research Journal of Multidisciplinary Studies, 1(6), 42–52. https://gsarpublishers.com/abstract-2326/
31. Mudunuri, P. R. (2024). Operational transparency as a compliance mechanism in federal DevOps ecosystems. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(3), 8131–8142.
32. Archana, R., & Anand, L. (2025). Residual U-Net with self-attention based deep convolutional adaptive capsule network for liver cancer segmentation and classification. Biomedical Signal Processing and Control, 105, 107665.
33. Kamadi, S. AI-augmented threat intelligence for autonomous vulnerability management in cloud-native clusters. International Journal of Scientific Research in Computer Science Engineering and Information Technology (IJSRCSEIT).
34. Surisetty, L. S. (2024). Improving Disease Detection Accuracy with Al and Secure Data Exchange through API Gateways. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(3), 10346-10354.
35. Beeram, S. (2025). Optimizing Cloud Traffic Management through Azure Application Gateway: Configuration Strategies and Real-World Use Cases. IJSAT-International Journal on Science and Technology, 16(4).
36. Lokiny, N. (2023). Artificial intelligence driven continuous feedback loops for performance optimization techniques improvement in DevOps. Journal of Artificial Intelligence & Cloud Computing, 2(2), 1-3.
37. Veershetty, G. (2024). AI-Driven Governance Control Plane for Multi-Vendor SAP Service Delivery Ecosystems. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(3), 247-258.
38. Gaddapuri, N. S. (2023). A comparative study of healthcare systems in the United States and India. Power System Protection and Control, 51(2), 18–31.
39. Tamizharasi, S., Rubini, P., Saravana Kumar, S., & Arockiam, D. Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments.
40. Ananth, S., Radha, K., & Raju, S. (2024). Animal detection in farms using OpenCV in deep learning. Advances in Science and Technology Research Journal, 18(1), 1.
41. Itoo, S., Khan, A. A., Ahmad, M., & Idrisi, M. J. (2023). A secure and privacy-preserving lightweight authentication and key exchange algorithm for smart agriculture monitoring system. IEEE Access, 11, 56875–56890.
42. Parathraju, P., & Umasankar, P. (2025). Performance evaluation of ultrathin CdTe-based solar cells with dual absorbers via SCAPS-1D simulation. Scientific Reports, 15(1), 26428.
