AI and ML-Driven SAP Supply Chain Security: Enhancing Cyber Resilience with CNNs, Cloud-Based Architecture, and Secured Access
DOI:
https://doi.org/10.15662/IJRAI.2024.0706005Keywords:
Artificial Intelligence, Machine Learning, SAP, Cybersecurity, Supply chain resilience, Convolutional Neural Networks (CNNs), Cloud architecture, Secured access, Anomaly detection, Intrusion prevention, Blockchain, Digital transformation, Predictive analytics, Data protectionAbstract
The increasing digitization of enterprise ecosystems has heightened the need for robust and intelligent cybersecurity frameworks within SAP-driven supply chains. This paper presents an AI and ML-driven security architecture that leverages Convolutional Neural Networks (CNNs), cloud-based infrastructure, and secured access protocols to strengthen cyber resilience and operational continuity. The proposed framework employs CNNs for real-time intrusion detection and anomaly recognition across network layers, SAP modules, and IoT-connected assets. Machine learning models analyze behavioral and transactional patterns to identify threats such as data tampering, unauthorized access, and advanced persistent attacks before they impact business operations. Cloud-native deployment ensures scalability, fault tolerance, and rapid security updates, while blockchain-enabled access management guarantees transparency and immutability in identity verification. Experimental validation reveals that the system enhances detection accuracy, reduces latency in threat response, and improves overall SAP supply chain robustness. The study concludes that integrating AI and ML with CNN-powered analytics in a cloud-secured SAP environment is critical to achieving proactive, adaptive, and sustainable cyber resilience in modern digital supply chains.
References
1. Kosasih, E. E., & Brintrup, A. (2021). Reinforcement learning provides a flexible approach for realistic supply chain safety stock optimisation. arXiv. https://doi.org/10.48550/arXiv.2107.00913
2. Dave, B. L. (2024). An Integrated Cloud-Based Financial Wellness Platform for Workplace Benefits and Retirement Management. International Journal of Technology, Management and Humanities, 10(01), 42-52.
3. Arul Raj .A.M and Sugumar R.,” Monitoring of the social Distance between Passengers in Real-time through video Analytics and Deep learning in Railway stations for Developing highest Efficiency” , March 2023 International Conference on Data Science, Agents and Artificial Intelligence, ICDSAAI 2022, ISBN 979- 835033384-8, March 2023, Chennai , India ., DOI 10.1109/ICDSAAI55433.2022.10028930.
4. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2020). Explain ability and interpretability in machine learning models. Journal of Computer Science Applications and Information Technology, 5(1), 1-7.
5. 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.
6. Sahaj Gandhi, Behrooz Mansouri, Ricardo Campos, and Adam Jatowt. 2020. Event-related query classification with deep neural networks. In Companion Proceedings of the 29th International Conference on the World Wide Web. 324–330.
7. Wang, H., Sagbansua, L., & Alidaee, B. (2023). Enhancing supply chain security with automated machine learning. arXiv. https://doi.org/10.21203/rs.3.rs-3317886/v1
8. S. Devaraju, HR Information Systems Integration Patterns, Independently Published, ISBN: 979-8330637850, DOI: 10.5281/ZENODO.14295926, 2021.
9. Vadlamani, S., Kankanampati, P. K., Goel, O., Jain, A., & Khan, S. (2022). Integrating AI and machine learning for optimized supply chain and procurement systems. Universal Research Reports, 9(4), 540–560. https://urr.shodhsagar.com/index.php/j/article/view/1391
10. AKTER, S., ISLAM, M., FERDOUS, J., HASSAN, M. M., & JABED, M. M. I. (2023). Synergizing Theoretical Foundations and Intelligent Systems: A Unified Approach Through Machine Learning and Artificial Intelligence.
11. Sugumar, R. (2023). Enhancing COVID-19 Diagnosis with Automated Reporting Using Preprocessed Chest X-Ray Image Analysis based on CNN (2nd edition). International Conference on Applied Artificial Intelligence and Computing 2 (2):35-40.
12. Chellu, R. (2022). Design and Implementation of a Secure Password Management System for Multi-Platform Credential Handling.
13. Kalusivalingam, A. K., Sharma, A., Patel, N., & Singh, V. (2022). Enhancing supply chain resilience through AI: Leveraging deep reinforcement learning and predictive analytics. International Journal of AI and ML, 3(9). https://doi.org/10.32628/CSEIT241061232
14. Bangar Raju Cherukuri, "AI-powered personalization: How machine learning is shaping the future of user experience," ResearchGate, June 2024. [Online]. Available: https://www.researchgate.net/publication/384826886_AIpowered_personalization_How_machine_learning_is_shaping_the_future_of_user_experience
15. Devaraju, S., Katta, S., Donuru, A., & Devulapalli, H. Comparative Analysis of Enterprise HR Information System (HRIS) Platforms: Integration Architecture, Data Governance, and Digital Transformation Effectiveness in Workday, SAP SuccessFactors, Oracle HCM Cloud, and ADP Workforce Now.
16. Poovaiah, S. A. D. (2022). Benchmarking provable resilience in convolutional neural networks: A study with Beta-CROWN and ERAN.
17. SAP India. (2021, September 9). Build supply chain resilience and agility in 2022. SAP News. https://news.sap.com/india/2021/09/build-supply-chain-resiliency/
18. Yeboah-Ofori, A., Swart, C., Opoku-Boateng, F. A., & Islam, S. (2022). Cyber resilience in supply chain system security using machine learning for threat predictions. Continuity & Resilience Review, 4(1), 1–36. https://doi.org/10.1108/CRR-10-2021-0034
19. Shekhar, P. C. (2023). From Traditional to Transformational: Leveraging Digital Twins for Advanced Testing in Life Insurance.
20. GUPTA, A. B., et al. (2023). "Smart Defense: AI-Powered Adaptive IDs for Real-Time Zero-Day Threat Mitigation."
21. Sugumar, R. (2023). A Deep Learning Framework for COVID-19 Detection in X-Ray Images with Global Thresholding. IEEE 1 (2):1-6.
22. Devaraju, S., & Boyd, T. (2021). AI-augmented workforce scheduling in cloud-enabled environments. World Journal of Advanced Research and Reviews, 12(3), 674-680.
23. Gosangi, S. R. (2024). Scalable Single Sign-On Architecture: Securing Access in Large Enterprise Systems. International Journal of Technology, Management and Humanities, 10(02), 27-33.
24. Schmitt, M. (2023). Securing the digital world: Protecting smart infrastructures and digital industries with artificial intelligence (AI)-enabled malware and intrusion detection. arXiv. https://doi.org/10.48550/arXiv.2401.01342
