AI-driven cyber threat detection for global logistics in United States

PRECIOUS, OJO TITILAYO (2025) AI-driven cyber threat detection for global logistics in United States. International Journal of Science and Research Archive, 14 (1). pp. 1360-1367. ISSN 2582-8185

The integration of AI-driven cybersecurity systems is increasingly essential to the security and efficiency of global logistics operations in the United States. This thematic analysis investigates the major themes, challenges, and opportunities of AI in logistics cybersecurity, focusing on its effectiveness in threat detection and prevention, diverse applications, and implementation challenges. AI has been shown to significantly enhance threat detection, reducing response times by up to 65%, and proactively preventing cyber threats through predictive analytics. Key applications include AI-driven network monitoring, endpoint security, and supply chain risk management, each contributing to the overall resilience of logistics networks. However, several challenges hinder the widespread adoption of AI, including high initial costs, integration issues with legacy systems, and a shortage of skilled professionals. Additionally, regulatory compliance and ethical considerations, such as data privacy and transparency, must be addressed to ensure responsible deployment. Despite these barriers, empirical evidence highlights AI’s potential to revolutionize logistics cybersecurity, offering faster threat detection, reduced vulnerabilities, and more secure operations. Overcoming adoption challenges requires a concerted effort from industry stakeholders, with a focus on workforce development, regulatory adherence, and ethical AI practices. Future research should explore strategies to address these challenges, ensuring the full potential of AI in enhancing the security and efficiency of global logistics operations.