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FORTIFYING AI-IOT FOOD SUPPLY CHAINS: ADDRESSING THIRD-PARTY CYBERSECURITY VULNERABILITIES
Get PDFThe 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
AN ANALYSIS OF SECURITY RISKS POSED BY TEXT-BASED GENERATIVE AI AND CORPORATE SECURITY WEAKNESSES LEADING TO DATA LEAKS
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Generative AI (GenAI) has become a fundamental part of modern life, influencing how we work, learn, and interact with technology. This project focuses specifically on text-based GenAI, which is widely used for tasks such as information gathering, code improvement, and content creation. Despite its benefits, it presents significant security risks that are often underestimated by users. This project investigates these risks and the corporate security gaps that lead to unintentional data leaks. The project also provides a brief overview of Large Language Models (LLMs), which are based on the deep learning technique known as Transformer architecture, used for performing Natural Language Processing (NLP) tasks. Additionally, a brief explanation of the Transformer architecture, including its main components, the encoder and decoder, has been included. The research objectives of this project are: (RO1) to understand how security risks emerge from text-based GenAI and (RO2) to examine how corporate security weaknesses enable unintended data disclosures. Two case studies were selected and analyzed to answer the research objectives. Findings reveal that GenAI safeguards are insufficient, allowing for prompt manipulation and various malicious activities, while inadequate employee training and guidelines contribute to data leaks. Conclusion highlights that, while text-based GenAI offers numerous benefits, it also carries significant security risks. Enhanced corporate training, stronger GenAI security measures, and effective mitigation strategies are essential to address these vulnerabilities. Future research could concentrate on developing these mitigation strategies, establishing standardized guidelines for secure AI usage in corporate environments, and exploring security measures for media-based and audio-based GenAI
THE PRACTICAL ADOPTION AND APPLICATION OF BLOCKCHAIN TECHNOLOGY WITHIN THE BEVERAGE INDUSTRY
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The beverage industry is facing heightened scrutiny as the demand for transparency and accountability reaches new heights. In the age of information technology, companies must prioritize enhanced traceability to ensure product safety, comply with government regulations, maintain customer trust, and protect brand integrity. This thesis explores the potential of blockchain technology as a solution to these challenges, focusing on its ability to decentralize data, improve traceability, and expedite response times during safety recalls. The research provides an overview of the evolution of food safety regulations, beginning with the first establishment by Upland Sinclair, and examines current traceability practices and technological advancements within the beverage sector. Through a case study on the BODYARMOR Sport Water recall, this thesis highlights vulnerabilities in the industry and presents strategies for implementing blockchain to address these issues. The findings underscore the importance of integrating blockchain with existing systems, ensuring accurate data entry, and tackling scalability and privacy concerns across businesses of all sizes. This thesis bridges the gap between academic research and industry practice, offering actionable insights into enhancing supply chain integrity through blockchain technology in the beverage industry
