Tools
Tools
Metin, Bilgin, Wynn, Martin G 
ORCID: https://orcid.org/0000-0001-7619-6079, Tunali, Aylin and Kepir, Yagmur
(2025)
Business Logic Vulnerabilities in the Digital Era: A Detection Framework Using Artificial Intelligence.
Information, 16 (585).
pp. 1-22.
doi:10.3390/ info16070585
Preview |
Text
15168 Metin, B. et al.(2025) Business Logic Vulnerabilities in the Digital Era.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (1MB) | Preview |
Abstract
Digitalisation can positively impact the efficiency of real-world business processes, but may also introduce new cybersecurity challenges. One area that is particularly vulnerable to cyber-attacks is the business logic embedded in processes in which flaws may exist. This is especially the case when these processes are within web-based applications and services, which are increasingly becoming the norm for many organisations. Business logic vulnerabilities (BLVs) can emerge following the software development process, which may be difficult to detect by vulnerability detection tools. Through a systematic literature review and interviews with industry practitioners, this study identifies key BLV types and the challenges in detecting them. The paper proposes an eight-stage operational framework that leverages Artificial Intelligence (AI) for enhanced BLV detection and mitigation. The research findings contribute to the rapidly evolving theory and practice in this field of study, highlighting the current reliance on manual detection, the contextual nature of BLVs, and the need for a hybrid, multi-layered approach integrating human expertise with AI tools. The study concludes by emphasizing AI’s potential to transform cybersecurity from a reactive to a proactive defense against evolving vulnerabilities and threats.
| Item Type: | Article |
|---|---|
| Article Type: | Article |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences |
| Depositing User: | Martin Wynn |
| Date Deposited: | 11 Jul 2025 10:42 |
| Last Modified: | 11 Jul 2025 11:00 |
| URI: | https://eprints.glos.ac.uk/id/eprint/15168 |
University Staff: Request a correction | Repository Editors: Update this record
