FORTIFYING AI-IOT FOOD SUPPLY CHAINS: ADDRESSING THIRD-PARTY CYBERSECURITY VULNERABILITIES

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) technologies within food supply chains has led to significant operational efficiencies but has also introduced cybersecurity vulnerabilities, especially from third-party vendors supplying critical software and hardware. This study explores these vulnerabilities and evaluates multi-layered defense strategies to mitigate the cybersecurity risks they introduce. The research questions guiding this study are: (Q1) How do third-party vendors contribute to cybersecurity vulnerabilities within AI-IoT systems in the food industry? (Q2) How can multi-layered defense strategies effectively mitigate the cybersecurity risks introduced by these vendors? Using a systematic literature review (SLR) approach based on adapted PRISMA guidelines, this study synthesizes recent research on AI-IoT vulnerabilities and defense mechanisms in food industry applications. Findings highlight that third-party vendors often fail to implement robust security measures, leading to vulnerabilities like data integrity compromises and unauthorized access. Defense strategies, including blockchain-enhanced transparency and AI-driven threat intelligence, demonstrate promise in strengthening the cybersecurity of AI-IoT food systems. The study concludes with recommendations for cross-industry standardization and enhanced vendor compliance to secure food supply chains from emerging cyber threats. Future research should further explore blockchain scalability and advanced AI models for real-time threat detection to enhance resilience in this sector.

Publication Details

Journal
CSUSB ScholarWorks (California State University, San Bernardino)
Published
2024-12-01
Primary Topic
Solar and Space Plasma Dynamics
Type
article
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article

FORTIFYING AI-IOT FOOD SUPPLY CHAINS: ADDRESSING THIRD-PARTY CYBERSECURITY VULNERABILITIES

CSUSB ScholarWorks (California State University, San Bernardino)
Solar and Space Plasma Dynamics
article

FORTIFYING AI-IOT FOOD SUPPLY CHAINS: ADDRESSING THIRD-PARTY CYBERSECURITY VULNERABILITIES

article en

Abstract

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) technologies within food supply chains has led to significant operational efficiencies but has also introduced cybersecurity vulnerabilities, especially from third-party vendors supplying critical software and hardware. This study explores these vulnerabilities and evaluates multi-layered defense strategies to mitigate the cybersecurity risks they introduce. The research questions guiding this study are: (Q1) How do third-party vendors contribute to cybersecurity vulnerabilities within AI-IoT systems in the food industry? (Q2) How can multi-layered defense strategies effectively mitigate the cybersecurity risks introduced by these vendors? Using a systematic literature review (SLR) approach based on adapted PRISMA guidelines, this study synthesizes recent research on AI-IoT vulnerabilities and defense mechanisms in food industry applications. Findings highlight that third-party vendors often fail to implement robust security measures, leading to vulnerabilities like data integrity compromises and unauthorized access. Defense strategies, including blockchain-enhanced transparency and AI-driven threat intelligence, demonstrate promise in strengthening the cybersecurity of AI-IoT food systems. The study concludes with recommendations for cross-industry standardization and enhanced vendor compliance to secure food supply chains from emerging cyber threats. Future research should further explore blockchain scalability and advanced AI models for real-time threat detection to enhance resilience in this sector.

CSUSB ScholarWorks (California State University, San Bernardino)
Zero hunger
Openalex Percentile: Top 55%
Solar and Space Plasma Dynamics
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FORTIFYING AI-IOT FOOD SUPPLY CHAINS: ADDRESSING THIRD-PARTY CYBERSECURITY VULNERABILITIES · CSUSB ScholarWorks (California State University, San Bernardino) (2024) | TGRS Research Map | TGRS