Cybersecurity in Digital Accounting Systems: Challenges and Solutions in the Arab Gulf Region
Bibliographic record
Abstract
The region of the Arab Gulf is marching ahead very fast toward digitalization in ways prompted by initiatives, such as Saudi Vision 2030 and the UAE’s strategy for Smart Government. Thus, both underscore the boundless movement toward the inclusion of advanced technologies into accounting practices, such as Business Intelligence and Enterprise Resource Planning systems. While these technologies enhance efficiency and facilitate informed decision-making, they also render financial data vulnerable to cybersecurity threats, such as phishing, ransomware, and insider attacks. This paper investigates the impact of cybersecurity practices, ethical accountability, regulatory frameworks, and emerging technologies on the adoption of and trust in digital accounting systems in the GCC region. A quantitative research approach was followed, wherein the responses from a randomly selected sample of 324 professionals representing the GCC nations were collected. The empirical analysis was completed using Partial Least Squares Structural Equation Modeling. Strong cybersecurity measures, AI-driven threat detection mechanisms, and custom-fit employee training programs facilitate the adoption of and faith in digital accounting information systems considerably. Ethical accountability acts as the partial mediator of those effects, and supportive regulatory frameworks enhance cybersecurity strategy effectiveness. This study examines the development of integrated cybersecurity strategies with respect to technology, ethics, and regulations. It makes several major recommendations, calling for bringing the GCC countries’ regulatory frameworks into line with international standards; encouraging workforce training programs; and utilizing AI-powered technologies for proactive threat detection and management. These findings can arm stakeholders with a holistic pathway toward developing secure, resilient, and future-oriented digital accounting infrastructures across the region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".