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.