Securing Supply Chain Data Integrity

Résumé fourni par la source

The digitization of global supply chains has enhanced efficiency but simultaneously expanded the attack surface for cyber-sabotage and insider manipulation. Ensuring data integrity across interconnected entities has become a strategic necessity for operational resilience. This chapter presents an AI-driven multi-layered framework integrating machine learning, deep learning, federated learning, and explainable AI to strengthen data authenticity and transparency. The framework detects behavioral and contextual anomalies, preserves privacy through decentralized intelligence, and supports auditability via interpretable decision outputs. Analytical case studies from pharmaceutical and semiconductor domains demonstrate adaptability across heterogeneous supply environments. The study further outlines future research directions involving quantum-safe architectures, sustainable AI, and ethical governance, positioning artificial intelligence as a cornerstone for secure, trustworthy, and resilient supply chain ecosystems.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.