AI and Blockchain in Critical Food Supply Infrastructure: Cybersecurity Threats and Solutions

Abstract

The escalating digitization of food supply chains (FSCs) has exposed them to significant cybersecurity threats, endangering food safety, integrity, and operational efficiency. This study presents a robust framework integrating blockchain and artificial intelligence (AI) to counter these challenges. Blockchain technology, with its decentralized and immutable ledger, ensures transparent and tamper-proof traceability, while AI’s capability for real-time data analysis enables anomaly detection, predictive maintenance, and fraud prevention. Edge computing complements this framework by enabling localized data processing, thereby reducing latency and enhancing cyber-resilience.

Methodologically, the study combines a systematic literature review with simulated experiments to evaluate the efficacy of the integrated framework. The results are compelling: the framework achieves 92% accuracy in anomaly detection, reduces food fraud incidents by 40%, and boosts operational efficiency by 25%. Additionally, the fusion of edge-AI with blockchain slashes data processing latency by 30% in comparison to traditional cloud-based systems. These improvements effectively mitigate data integrity attacks, insider threats, and network-based vulnerabilities while preserving end-to-end traceability.

The study’s findings highlight the potential of AI and blockchain to revolutionize FSCs, making them more secure, transparent, and efficient. However, challenges such as scalability, cost-effectiveness, and regulatory compliance need further exploration. This research not only offers actionable insights for developing resilient food supply infrastructures but also outlines priorities for future research, including the creation of advanced insider threat detection models and the implementation of robust security protocols. Overall, the proposed framework represents a significant step toward securing FSCs in an increasingly digital world.