AI-Driven Big Data Analytics for Secure, Privacy-Centric Web Applications in SAP Ecosystems
DOI:
https://doi.org/10.15662/IJEETR.2025.0706026Keywords:
Machine Learning, Big Data Analytics, Generative AI, Cybersecurity, Data Privacy, Web Technologies, SAP SystemsAbstract
The convergence of machine learning, big data, web technologies, and generative AI has transformed enterprise data processing while introducing complex cybersecurity and privacy challenges, particularly within SAP-based digital ecosystems. This paper presents an integrated analytical framework that combines machine learning and big data analytics with generative AI to enhance secure data management, intelligent web-based interactions, and privacy preservation in SAP environments. The proposed approach leverages scalable big data pipelines to process heterogeneous web and enterprise data, while machine learning models enable real-time threat detection, anomaly identification, and predictive risk assessment. Generative AI is employed to automate data synthesis, policy enforcement, and adaptive security responses, improving system resilience against evolving cyber threats. Privacy-aware mechanisms, including data anonymization and access control, are incorporated to ensure regulatory compliance and trust. The framework demonstrates improved cybersecurity intelligence, data governance, and operational efficiency, positioning SAP platforms as robust and privacy-centric enterprise solutions in large-scale digital infrastructures.
References
1. Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31.
2. Babiceanu, R. F., & Seker, R. (2006). Tangible benefits and challenges of RFID in supply chains. Computers in Industry, 57(8–9), 900–916.
3. Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business School Press.
4. Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. Journal of Privacy and Confidentiality, 7(3).
5. 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.
6. Kesavan, E. (2023). ML-Based Detection of Credit Card Fraud Using Synthetic Minority Oversampling. International Journal of Innovations in Science, Engineering And Management, 55-62.
7. Pimpale, S. (2025). A Comprehensive Study on Cyber Attack Vectors in EV Traction Power Electronics. arXiv preprint arXiv:2511.16399.
8. Potdar, A., Kodela, V., Srinivasagopalan, L. N., Khan, I., Chandramohan, S., & Gottipalli, D. (2025, July). Next-Generation Autonomous Troubleshooting Using Generative AI in Heterogeneous Cloud Systems. In 2025 International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) (pp. 1-7). IEEE.
9. Kubam, C. S. (2026). Agentic AI Microservice Framework for Deepfake and Document Fraud Detection in KYC Pipelines. arXiv preprint arXiv:2601.06241.
10. Genne, S. (2025). Bridging the Digital Divide: Mobile Web Engineering as a Pathway to Equitable Higher Education Access. Journal of Computer Science and Technology Studies, 7(7), 560-566.
11. Kabade, S., Sharma, A., & Chaudhari, B. B. (2025, June). Tailoring AI and Cloud in Modern Enterprises to Enhance Enterprise Architecture Governance and Compliance. In 2025 5th International Conference on Intelligent Technologies (CONIT) (pp. 1-6). IEEE.
12. 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.
13. Kusumba, S. (2024). Accelerating AI and Data Strategy Transformation: Integrating Systems, Simplifying Financial Operations Integrating Company Systems to Accelerate Data Flow and Facilitate Real-Time Decision-Making. The Eastasouth Journal of Information System and Computer Science, 2(02), 189-208.
14. D. Johnson, L. Ramamoorthy, J. Williams, S. Mohamed Shaffi, X. Yu, A. Eberhard, S. Vengathattil, and O. Kaynak, “Edge ai for emergency communications in university industry innovation zones,” The AI Journal [TAIJ], vol. 3, no. 2, Apr. 2022.
15. Akter Tohfa, N., Alim, M. A., Arif, M. H., Rahman, M. R., Rahman, M., Rasul, I., & Hossen, M. S. (2025). Machine learning–enabled anomaly detection for environmental risk management in banking. World Journal of Advanced Research and Reviews, 28(3), 1674–1682. https://doi.org/10.30574/wjarr.2025.28.3.4259
16. Madabathula, L. (2022). Automotive sales intelligence: Leveraging modern BI for dealer ecosystem optimization. International Journal of Humanities and Information Technology (IJHIT), 4(1–3), 80–93. https://www.ijhit.info
17. Chivukula, V. (2020). IMPACT OF MATCH RATES ON COST BASIS METRICS IN PRIVACY- PRESERVING DIGITAL ADVERTISING. International Journal of Advanced Research in Computer Science & Technology, 3(4), 3400–3405.
18. Singh, A. (2023). Benchmarking Network Performance in Smart Cities. Journal of Artificial Intelligence & Cloud Computing, 2(2), 1-6.
19. Natta, P. K. (2024). Closed-loop AI frameworks for real-time decision intelligence in enterprise environments. International Journal of Humanities and Information Technology, 6(3). https://doi.org/10.21590/ijhit.06.03.05
20. Thumala, S. R., Madathala, H., & Mane, V. M. (2025, February). Azure Versus AWS: A Deep Dive into Cloud Innovation and Strategy. In 2025 International Conference on Electronics and Renewable Systems (ICEARS) (pp. 1047-1054). IEEE.
21. Poornima, G., & Anand, L. (2024, April). Effective strategies and techniques used for pulmonary carcinoma survival analysis. In 2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST) (pp. 1-6). IEEE.
22. Kasireddy, J. R. (2025, April). The Role of AI in Modern Data Engineering: Automating ETL and Beyond. In International Conference of Global Innovations and Solutions (pp. 667-693). Cham: Springer Nature Switzerland.
23. Sugumar, R. (2024). Next-Generation Security Operations Center (SOC) Resilience: Autonomous Detection and Adaptive Incident Response Using Cognitive AI Agents. International Journal of Technology, Management and Humanities, 10(02), 62-76.
24. Panda, M. R., Mani, K., & Muthusamy, P. (2024). Hybrid Graph Neural Networks and Transformer Models for Regulatory Data Lineage in Banking. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 6(1), 619-633.
25. Ramakrishna, S. (2024). Intelligent Healthcare and Banking ERP on SAP HANA with Real-Time ML Fraud Detection. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(Special Issue 1), 1-7.
26. Nagarajan, G. (2023). AI-Integrated Cloud Security and Privacy Framework for Protecting Healthcare Network Information and Cross-Team Collaborative Processes. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6292-6297.
27. Navandar, P. (2022). The Evolution from Physical Protection to Cyber Defense. International Journal of Computer Technology and Electronics Communication, 5(5), 5730-5752.
28. 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.
29. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
30. Ramalingam, S., Mittal, S., Karunakaran, S., Shah, J., Priya, B., & Roy, A. (2025, May). Integrating Tableau for Dynamic Reporting in Large-Scale Data Warehousing. In 2025 International Conference on Networks and Cryptology (NETCRYPT) (pp. 664-669). IEEE.
31. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.
32. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
33. Vasugi, T. (2023). An Intelligent AI-Based Predictive Cybersecurity Architecture for Financial Workflows and Wastewater Analytics. International Journal of Computer Technology and Electronics Communication, 6(5), 7595-7602.
34. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
35. Okpara, K. (2025). Human-Centric Machine Learning Intrusion Detection for Smart Grid SCADA Systems, Grounded in Human-Systems Integration Theory. Available at SSRN 5295278.
36. 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.
37. Kairam, S., Braverman, M., & Cheng, J. (2012). Designing and mining multi facet data streams for real time intelligence. ACM Transactions on Knowledge Discovery from Data, 6(4).
38. Konečný, J., McMahan, H. B., Ramage, D., & Richtárik, P. (2016). Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492.
