Secure and Adaptive Digital Infrastructure AI-Driven Cybersecurity and Policy-Aware Cloud Reliability in SAP-Enabled Enterprises
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806014Keywords:
AI-Driven Cybersecurity, Policy-Aware Systems, Cloud Reliability, SAP Cloud Platforms, Digital Infrastructure, Enterprise Resilience, Predictive Analytics, Compliance Automation, Adaptive SecurityAbstract
As enterprises increasingly rely on cloud-based SAP platforms to support mission-critical business operations, ensuring security, reliability, and regulatory compliance has become a strategic priority. Traditional cloud infrastructures and static security models are insufficient to address the dynamic threat landscape, complex compliance requirements, and high availability demands of modern enterprises. This paper presents a secure and adaptive digital infrastructure framework that leverages artificial intelligence (AI)-driven cybersecurity and policy-aware system design to enhance cloud reliability in SAP-enabled enterprises. The proposed approach integrates AI-based threat detection, predictive analytics, and automated policy enforcement within SAP cloud ecosystems to enable proactive risk mitigation and resilient operations. By aligning cybersecurity controls with policy-aware reliability mechanisms, the framework supports continuous compliance, adaptive defense, and uninterrupted service delivery. The study demonstrates how AI-enabled and policy-aware cloud infrastructures can transform enterprise systems from reactive security postures to intelligent, self-adaptive, and trustworthy digital platforms.References
1. ENISA. (2021). Cloud security for enterprises: Challenges and best practices. European Union Agency for Cybersecurity.
2. Usha, G., Babu, M. R., & Kumar, S. S. (2017). Dynamic anomaly detection using cross layer security in MANET. Computers & Electrical Engineering, 59, 231-241.
3. 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.
4. Ramakrishna, S. (2023). Cloud-Native AI Platform for Real-Time Resource Optimization in Governance-Driven Project and Network Operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6282-6291.
5. Adari, V. K. (2024). The Path to Seamless Healthcare Data Exchange: Analysis of Two Leading Interoperability Initiatives. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11472-11480.
6. Meka, S. (2025). Fortifying Core Services: Implementing ABA Scopes to Secure Revenue Attribution Pipelines. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(2), 11794-11801.
7. Bussu, V. R. R. (2023). Governed Lakehouse Architecture: Leveraging Databricks Unity Catalog for Scalable, Secure Data Mesh Implementation. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6298-6306.
8. Poornima, G., & Anand, L. (2024, May). Novel AI Multimodal Approach for Combating Against Pulmonary Carcinoma. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-6). IEEE.
9. Rajurkar, P. AI-Driven Fenceline Monitoring for Real-Time Detection of Hazardous Air Pollutants in Industrial Corridors. (Tjosvold, 1998)
10. Humble, J., & Farley, D. (2010). Continuous delivery: Reliable software releases through build, test, and deployment automation. Addison-Wesley.
11. Kavuru, L. T. (2025). Invisible Hands: The Rise of Unseen AI Partners in Remote Project Decision Loops. International Journal of Research and Applied Innovations, 8(5), 13006-13014.
12. Kim, G., Debois, P., Willis, J., & Humble, J. (2016). The DevOps handbook: How to create world-class agility, reliability, and security in technology organizations. IT Revolution Press.
13. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
14. SAP SE. (2023). SAP business technology platform security and compliance overview. SAP Press.
15. Al Rafi, M. (2022). Intelligent Customer Segmentation A Data-Driven Framework for Targeted Advertising and Digital Marketing Analytics. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7417-7428.
16. Vasugi, T. (2022). AI-Enabled Cloud Architecture for Banking ERP Systems with Intelligent Data Storage and Automation using SAP. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4319-4325.
17. 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.
18. Parameshwarappa, N. (2025). Deconstructing Government-Grade Access Management Systems in the Cloud. Journal Of Engineering And Computer Sciences, 4(7), 719-727.
19. Paul, D., Soundarapandiyan, R., & Sivathapandi, P. (2021). Optimization of CI/CD Pipelines in Cloud-Native Enterprise Environments: A Comparative Analysis of Deployment Strategies. Journal of Science & Technology, 2(1), 228-275.
20. 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.
21. Sharma, A., Borovica-Gajic, R., Lee, S., & Banerjee, A. (2021). Machine learning for cloud operations: A survey. ACM Computing Surveys, 53(6), 1–37. https://doi.org/10.1145/3459992
22. Thambireddy, S. (2022). SAP PO Cloud Migration: Architecture, Business Value, and Impact on Connected Systems. International Journal of Humanities and Information Technology, 4(01-03), 53-66.
23. Kasaram, C. R. (2020). Platform Engineering at Scale: Building Self-Service Dev Environments with Observability. ISCSITR-INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND ENGINEERING (ISCSITR-IJCSE)-ISSN: 3067-7394, 1(1), 5-14.
24. Tamizharasi, S., Rubini, P., Saravana Kumar, S., & Arockiam, D. Adapting federated learning-based AI models to dynamic cyberthreats in pervasive IoT environments.
25. Kabade, S., Sharma, A., & Kagalkar, A. (2024). Securing Pension Systems with AI-Driven Risk Analytics and Cloud-Native Machine Learning Architectures. International Journal of Emerging Research in Engineering and Technology, 5(2), 52-64.
26. 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.
27. Joyce, S., Pasumarthi, A., & Anbalagan, B. (2025). SECURITY OF SAP SYSTEMS IN AZURE: ENHANCING SECURITY POSTURE OF SAP WORKLOADS ON AZURE–A COMPREHENSIVE REVIEW OF AZURENATIVE TOOLS AND PRACTICES.||.
28. Hasan, S., Zerine, I., Islam, M. M., Hossain, A., Rahman, K. A., & Doha, Z. (2023). Predictive Modeling of US Stock Market Trends Using Hybrid Deep Learning and Economic Indicators to Strengthen National Financial Resilience. Journal of Economics, Finance and Accounting Studies, 5(3), 223-235.
29. Kumar, S. N. P. (2022). Machine Learning Regression Techniques for Modeling Complex Industrial Systems: A Comprehensive Summary. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 67–79. https://ijhit.info/index.php/ijhit/article/view/140/136
30. Nagarajan, G. (2024). A Cybersecurity-First Deep Learning Architecture for Healthcare Cost Optimization and Real-Time Predictive Analytics in SAP-Based Digital Banking Systems. International Journal of Humanities and Information Technology, 6(01), 36-43.
31. Sugumar, R. (2025). Separating Technology and Trust: A Survey Analysis of Patients’ Attitudes toward AI-Assisted Healthcare Decision-Making. International Journal of Humanities and Information Technology, 7(01), 72-79.
32. Kumar, S. S. (2024). Cybersecure Cloud AI Banking Platform for Financial Forecasting and Analytics in Healthcare Systems. International Journal of Humanities and Information Technology, 6(04), 54-59.
33. Zhang, Q., Chen, M., Li, L., & Li, H. (2020). Predictive analytics for DevOps and cloud reliability engineering. IEEE Software, 37(4), 55–62. https://doi.org/10.1109/MS.2020.2986785
34. Kusumba, S. (2025). Integrated Order And Invoice Tracking: Optimizing Supply Chain Visibility And Financial Operations. Journal of International Crisis & Risk Communication Research (JICRCR), 8.
