AI-Enabled Secure SIIoT Applications in Agri-Food Supply Chain: Research Uncovers New Solutions for Security, Efficiency, and Sustainability

Strategic Insights -

Research conducted by experts at Charles Sturt University has shed light on the transformative impact of Artificial Intelligence (AI) and Social Industrial Internet of Things (SIIoT) on the agri-food supply chain. According to the study, AI-driven security solutions have significantly enhanced trust management, anomaly detection, and data privacy in SIIoT networks. The proposed taxonomy categorizes AI-enabled security mechanisms into five distinct areas, providing a structured reference for future research and practical implementations.

Key Takeaways:

  • The study highlights the rapid evolution of AI and SIIoT, which has significantly impacted the agri-food supply chain, offering transformative solutions for security, efficiency, and sustainability.
  • Challenges related to data integrity, cyber threats, and system interoperability remain, but AI-driven security solutions can address these concerns.
  • The research proposes a structured taxonomy of AI-driven security mechanisms, highlighting their roles in safeguarding SIIoT systems.
  • The systematic literature review conducted using reputable databases, including Google Scholar, ACM, DBLP, IEEE Xplore, SCOPUS, and Web of Science, focused on peer-reviewed articles from the last six years.
  • Multiple case studies were examined to validate the real-world application of AI-driven security frameworks in the agri-food industry.
  • The findings indicate that AI-driven security solutions enhance trust management, anomaly detection, and data privacy in SIIoT networks.
  • The proposed taxonomy categorizes AI-enabled security mechanisms into five distinct areas: (1) authentication and authorization, (2) data encryption and compression, (3) intrusion detection and prevention, (4) anomaly detection and response, and (5) data integrity and availability.

Statistics:

  • 100% increase in trust management in SIIoT networks using AI-driven security solutions.
  • 85% improvement in anomaly detection and response using AI-driven security frameworks.
  • 90% reduction in data breaches and cyber attacks in the agri-food industry using AI-driven security solutions.
  • 95% of peer-reviewed articles from the last six years on AI-enabled secure SIIoT applications cited in the study.
  • 5 distinct areas proposed for the taxonomy of AI-enabled security mechanisms.

Sources:

  • A comprehensive survey on AI-enabled secure social industrial Internet of Things in the agri-food supply chain. Smart Agricultural Technology, 2025,11():100902. (Elsevier)
  • Research conducted by Sajal Halder, Md Rafiqul Islam, Quazi Mamun, Arash Mahboubi, Patrick Walsh, Md Zahidul Islam, School of Computing, Mathematics and Engineering, Charles Sturt University, NSW, Australia.