About the Lecture:

This presentation examines the growing cybersecurity implications of artificial intelligence (AI) and maritime autonomy within an increasingly digitalised maritime sector. Drawing on research from the Alan Turing Institute and the University of Plymouth’s Centre for Marine Autonomy and Maritime Cyber Security and Technology (CMAST), the talk discusses how AI is being deployed to support sustainability, decarbonisation, situational awareness, and autonomous operations across commercial and defence maritime domains. Particular attention is given to the cyber-physical risks that emerge as vessels become more connected, software-dependent, and autonomous, including vulnerabilities in AI-enabled decision-making systems, adversarial machine learning attacks, and threats to remote and autonomous vessel operations. One case study is shared to highlights approaches to improving cyber resilience. The talk also has some considerations for the evolving regulatory landscape for Maritime Autonomous Surface Ships (MASS), discussing the challenges of governance, accountability, human oversight, and operational responsibility in autonomous systems. It concludes by reflecting on how the maritime community can balance innovation, sustainability, safety, and security as AI becomes increasingly embedded in future maritime operations.

About the Speaker

Professor Kimberly Tam is Professor of Cyber Security at the University of Plymouth and Theme Lead for Marine and Maritime at The Alan Turing Institute. Her research focuses on cybersecurity, cyber risk assessment, maritime cyber resilience, autonomous systems, and the human and organisational factors that influence security outcomes. She has led and contributed to numerous interdisciplinary projects in collaboration with industry, government, and international partners, helping to advance the understanding and management of cyber risks in complex technological environments.

Professor Tam earned a degree in Computer and Systems Engineering from Rensselaer Polytechnic Institute in the United States and completed a PhD in Information Security at Royal Holloway, University of London, where her research explored machine learning techniques for the analysis and classification of smartphone malware.