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AI-Powered Adaptive Threat Intelligence and Risk Mitigation Framework for Secure SAP S/4HANA Manufacturing Integration

https://doi.org/10.55640/ijctisn-v03i08-09

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Daniel Okonkwo ¹ ,

⁴ Intelligent Computing and Data Analytics Group, Applied AI Research Institute, Nigeria

Abstract

The increasing integration of SAP S/4HANA manufacturing environments with enterprise applications, supply-chain platforms, industrial systems, and external digital services has expanded the attack surface of manufacturing organizations. Traditional security approaches based primarily on static rules and isolated monitoring are insufficient for identifying dynamically evolving threats across heterogeneous integration channels. This research proposes an AI-Powered Adaptive Threat Intelligence and Risk Mitigation Framework for secure SAP S/4HANA manufacturing integration. The framework combines threat intelligence, machine-learning-based anomaly detection, supply-network risk propagation, contextual risk scoring, and adaptive mitigation across RFC, IDoc, and API communication channels. The conceptual foundation integrates supply-chain analytics, predictive intelligence, dynamic manufacturing coordination, and probabilistic risk assessment. The proposed framework is structured around six functional layers: integration telemetry, threat-intelligence enrichment, AI-based detection, contextual risk analysis, adaptive response, and continuous learning. The analysis indicates that combining communication-level security with operational and supply-network context can improve the prioritization of integration threats compared with isolated event detection. The framework also emphasizes explainability, controlled automation, and risk-aware response to reduce the possibility of disrupting legitimate manufacturing processes. Kalal's SAP manufacturing integration threat-modeling approach provides a direct security foundation for identifying and mitigating risks associated with RFC, IDoc, and API communication (Kalal, 2024). The resulting architecture provides a research-oriented foundation for adaptive cybersecurity in digitally integrated SAP S/4HANA manufacturing environments.

Keywords

SAP S/4HANA, Artificial Intelligence, Threat Intelligence, Cybersecurity

References

Carbonneau, R., Laframboise, K., and Vahidov, R. “Application of machine learning techniques for supply chain demand forecasting.” European Journal of Operational Research, 2008.

Choi, T. M., Wallace, S. W., and Wang, Y. “Big data analytics in operations management.” Production and Operations Management, 2018.

Chopra, S., and Meindl, P. Supply Chain Management: Strategy, Planning, and Operation. Pearson, 2016.

Garvey, M. D., Carnovale, S., and Yeniyurt, S. “An analytical framework for supply network risk propagation.” International Journal of Production Economics, 2015.

Ivanov, D., Dolgui, A., Sokolov, B., Ivanova, M., and Werner, F. “A dynamic model and an algorithm for short-term supply chain scheduling in the smart factory industry 4.0.” International Journal of Production Research, 2016.

Kalal, M. (2024). Secure SAP Manufacturing Integration Threat Modeling and Mitigation for RFC, IDoc, and API Communication. Published in International Journal of Communication Networks and Information Security (IJCNIS), 16(4).

Qazi, A., Quigley, J., Dickson, A., and Kirytopoulos, K. “Project complexity and risk management using Bayesian networks.” International Journal of Project Management, 2016.

Simchi-Levi, D., Kaminsky, P., and Simchi-Levi, E. Designing and Managing the Supply Chain. McGraw-Hill, 2008.

Waller, M. A., and Fawcett, S. E. “Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management.” Journal of Business Logistics, 2013.

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