Artificial Intelligence-Information Assurance Nexus: The Future of Information Systems Security, Privacy, and Quality

  • October 16, 2024
    Call for papers published


  • January 20, 2025
    Abstract Proposal Deadline


  • October 31, 2025
    Stage 1 Submission Deadline

Editors

  • 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

Description

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 (e.g., playing chess and translating languages by following specific rules). In addition, GenAI works with multiple data modalities (e.g., text, images, and videos), whereas traditional AI primarily functions in a single mode of data. These new capabilities of GenAI open new possibilities for its applications in a wide range of areas. GenAI models can range from generalized models to domain-specific models that automate tasks and generate content adhering to industry-specific terminologies, context-specialized knowledge, and tailored experiences. Its power has sparked discussions on ethics and societal questions regarding the potential impact on employment, bias, privacy, and human-AI relationships.

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. Studies have noted that GenAI may be able to address information management challenges, including quality. 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.

Another source of threats to information assurance stems from attacks that are designed to target the way GenAI systems are trained and expected to be used. Many of these attacks can be mitigated by explicitly integrating information assurance considerations when designing GenAI systems. For example, GenAI tools may be subject to unreliable training data, data poisoning, security leaks, inference attacks, and knowledge phishing.

Cisco found that 92% of organizations “see GenAI as fundamentally different, requiring new techniques to manage data and risks.” The 2023 U.S. Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence calls for actions on refining GenAI by mitigating information assurance issues. Worldwide efforts are being made on these fronts to protect LLMs against threats of information fabrication, system misuse, privacy breaches, etc. However, there are growing concerns that excessive focus and regulation on data security and privacy may stifle and slow the advancement of GenAI, especially in terms of the European Union’s AI Act.

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. By focusing on important research questions, this special issue will generate answers to address various national and international research agendas. This special issue also connects with IS research streams, such as the Bright Internet.

Potential topics

  • Influencing factors on security and privacy behavior in the presence of GenAI tools
  • Predicting, analyzing, and counteracting emerging threats to GenAI models
  • Economic analysis in combating information assurance threats in GenAI
  • Managerial strategies for addressing GenAI-induced data security and privacy issues
  • Principles for attributing accountability in GenAI model output risks
  • Individual behaviors regarding GenAI
  • Organizational practices surrounding GenAI
  • Societal impacts of GenAI
  • Risk management in the context of GenAI
  • Investments in assurance related to GenAI
  • Market effects of GenAI
  • Attacker analysis in relation to GenAI

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
Heng Xu, University of Florida
Alvin Leung, City University of Hong Kong
Niam Yaraghi, University of Miami
Ting Li, Erasmus University
Cathy Liu Yang, HEC Paris
Yingjie Zhang, Peking University