Rui Chen, Iowa State University

Juan Feng, Tsinghua University

Miguel Godinho de Matos, Católica Lisbon School of Business & Economics

Carol Hsu, University of Sydney

H. Raghav Rao, University of Texas at San Antonio

Digital threats continue to impede information assurance. Many issues in information assurance have arisen in the last decade or two, including risk management, information quality, intellectual property, privacy protection, compliance with regulations, and continuity of operations. As a result, protecting information has become a global priority, and collaborative efforts are being made to prevent, detect, and react to threats to information quality, authenticity, integrity, confidentiality, and availability.

As society steps into the age of generative AI (GenAI), fresh challenges and opportunities are arising in the realms of information security, privacy, and quality. Questions have emerged regarding the role and intended/unintended consequences of GenAI in information assurance. GenAI is believed to pose a paradox, serving as a dual-edged sword in the realm of information assurance. GenAI creates new content, whereas traditional AI mostly makes predictions and classifications based on existing datasets. GenAI is designed to reason and operate independently across various domains, whereas traditional AI focuses on narrow tasks.

The emergence of GenAI is poised to exert a profound impact on assurance. On the one hand, GenAI has been recognized for its ability to bolster information assurance, with the potential to strengthen business defenses, accelerate security processes, and identify emerging threats as they arise. On the other hand, GenAI heightens the potency of existing threats, allows the fabrication of false information, fuels intellectual property theft, and poses challenges to governance and compliance.

This special issue seeks research that goes beyond simple applications of existing theories and methods from the cybersecurity literature in IS. We invite studies that explore the unique information assurance challenges in the realm of GenAI, calling for the development and application of new theories or methods.

Potential topics

• What factors influence individuals' security and privacy behavior in the presence of GenAI tools?
• How can we predict, analyze, and counteract the emerging threats to GenAI models?
• How can economic analysis contribute to combating information assurance threats in GenAI?
• What are the managerial strategies and their effectiveness in addressing GenAI-induced issues on data security and privacy?
• What are the key principles in attributing accountability and responsibilities for the risks in GenAI model output?
• Individual behaviors - User security and privacy behaviors with GenAI
• Organizational practices - Enterprise AI governance and risk management
• Societal impacts - Broader implications of AI-enabled threats
• Risk management - Assessment and mitigation strategies
• Investments in assurance - Economic aspects of AI security
• Market effects - Industry-wide implications and responses
• Attacker analysis - Understanding adversarial AI behaviors

Timeline

• November 15, 2024: Optional introductory workshops (virtual)
• January 20, 2025: Two-page abstracts due
• March 31, 2025: Feedback on abstracts
• October 31, 2025: First-round paper submission
• February 28, 2026: First-round decisions
• June 30, 2026: Second-round revisions due
• September 30, 2026: Second-round decisions
• December 31, 2026: Final revisions due
• March 31, 2027: Final revisions
• Paper development workshop (virtual)
• Workshop for authors with R&R

Associate editors

• Panagiotis Adamopoulos, Emory University
• Jingjing Li, University of Virginia
• Rodrigo Belo, Nova School of Business and Economics
• Huigang Liang, University of Memphis
• Indranil Bose, NEOMA
• Alexander Maedche, Karlsruhe Institute of Technology
• Lemuria Carter, University of Sydney
• Ning Nan, University of British Columbia
• Christy Cheung, Hong Kong Baptist University
• Jella Pfeiffer, University of Stuttgart
• Rahul De', Indian Institute of Management Bangalore
• Dandan Qiao, National University of Singapore
• Amany Elbanna, University of Sussex
• Sagar Samtani, Indiana University
• Uri Gal, University of Sydney
• Anastasia Sergeeva, Vrije Universiteit Amsterdam
• Weiyin Hong, Hong Kong University of Science and Technology
• Maha Shaikh, ESADE Business School
• Nina Huang, University of Miami
• Paolo Spagnoletti, Luiss Business School
• Hartmut Höhle, University of Mannheim
• Rohit Valecha, University of Texas at San Antonio
• Allen Johnston, University of Alabama
• Jing Wang, Hong Kong University of Science and Technology
• Arpan Kar, Indian Institute of Technology
• Jingguo Wang, University of Texas at Arlington
• Juhee Kwon, City University of Hong Kong
• Hong Xu, Hong Kong University of Science and Technology
• Atanu Lahiri, University of Texas at Dallas
• Alvin Leung, City University of Hong Kong
• Ting Li, Erasmus University
• Heng Xu, University of Florida
• Niam Yaraghi, University of Miami
• Cathy Liu Yang, HEC Paris
• Yingjie Zhang, Peking University