User Vulnerabilities in AI-Driven Systems: Current Cybersecurity Threat Dynamics and Malicious Exploits in Supply Chain Management and Project Management
Joseph Squillace, Justice Cappella, Andrew Sepp · 2024
In today's interconnected digital ecosystem, Artificial Intelligence (AI) and Machine Learning (ML) stand at the forefront of innovation, efficiency, and resource management. However, the rapid ascension of AI and ML technologies that provide a foundation of groundbreaking capabilities need to be protected. Moreover, techniques that make AI and ML effective, efficient, and a force multiplier within the computing domain, and the central tenets of modern AI and ML technological advancement positioning them as the future of tomorrow, need to be safeguarded against exploitation and abuse by threat actors. Based on their ubiquity, embedded use of AI and ML within industry faces a major paradox; as AI and ML technologies empower and streamline operations while providing transformative benefits to users, they simultaneously introduce formidable threats through security vulnerabilities that can cause massive damage if not defended against. This discourse delves into cybersecurity vulnerabilities inherent to AI and ML implementations within critical infrastructure domains of supply chain management and project management to explore how these weaknesses may be exploited. Further, we discuss ways AI and ML can be exploited by cybersecurity vulnerabilities within industry to compromise data and resources. Through a focused, multiple case study, we identify a serious flaw within AL and ML needing further investigation, identify data needing further examination, and isolate specific cybersecurity threats associated with AI and ML in supply chain management and project management, including exploitation of user vulnerabilities, use of AI and ML to bypass security measures, and use of AI and ML to automate attacks.