Guest Editors:
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Dr. Joseph Bamidele Awotunde Department of Computer Science, Faculty of Information and Communication Sciences, University of Ilorin, Ilorin 240003, Kwara State, Nigeria |
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Dr. Akash Kumar Bhoi Directorate of Research, Sikkim Manipal University, Gangtok, Sikkim, 737102, India |
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Dr. Paolo Barsocchi Institute of Information Science and Technologies, National Research Council, 56124, Pisa, Italy |
The IJMS (Interdisciplinary Journal of Management Studies) is a peer-reviewed open access journal published by the University of Tehran, Iran since the year 2007. The journal publishes scientific papers reporting original research and/or applications in the field of Management Sciences.
Perspective
The rapid evolution of Artificial Intelligence (AI) has the enormous potential to deliver powerful insights for making intelligent business decisions through analyzing vast quantities of business data. Organizations are embracing the confluence of Business Intelligence (BI) and AI that aims to streamline the process of collecting, storing, and analyzing a large amount of data, both structured and unstructured, to draw valuable insights for forecasting marketing trends and user behavior which helps to improve the productivity as well as the performance of enterprises. However, the increased adoption of AI in business has also brought some serious threats and risks regarding the privacy of security. By employing AI, conventional cybersecurity mechanisms move from proactive to predictive.
AI can act as a double-edged sword so that it generates insights related to customer prediction. At the same time, it also comes up with a range of security vulnerabilities, significantly affecting trust and brand loyalty. AI can outperform well in automated threat detection and monitoring and risk-related decision-making. As organizations are at the forefront of this AI adoption, AI systems also become vulnerable to various cybersecurity threats such as evasion, data poisoning, data replication, and data manipulation attacks. Meanwhile, attackers are also leveraging AI to create malware and automated bots that humans could not easily detect and prevent. Once the threats attack the BI tools and applications, it becomes harder to control the cyberattacks as they learn to gain access quickly. Moreover, hackers can design new attacks that will create new mutations according to the recently launched defense mechanism during the previous attack. Most importantly, AI can mimic trusted participants using automated bots to copy their language and actions. As a consequence, AI systems without appropriate security features will lead to exposing the sensitive information and intellectual data to hackers, and as such, preventing and avoiding data leakage is extremely crucial for the business environment while updating AI features for existing products or platforms since it may not properly be investigated whether encrypted or not. In another scenario, it is of utmost importance to ensure the reliability of conclusions drawn by AI since mistakes will lead to dangerous consequences.
Organizations have a standard framework for managing and preventing AI-based reputational risks and data breaches to building more robust and resilient AI systems for modern business applications. This special issue intends to bring together the impact of AI-based threats and risks in business intelligence applications from a future perspective. We welcome researchers and practitioners to present their novel contributions in this regard. Topics of interest include, which are not limited to:
Manuscript Preparation and Submission
All Manuscripts submitting in the SI should conform to the standard editorial and publication policies as mentioned in the journal. The authors should submit the manuscript via online system at https://ijms.ut.ac.ir/. Please select the article type “SI: AIBI-2025” when submitting the manuscript.
Important Dates