Legal and cybersecurity challenges of integrating artificial intelligence and the internet of things in financial institutions in the United Arab Emirates and Jordan
Bibliographic record
Abstract
The study looks into the intersection of Artificial Intelligence (AI) with the Internet of Things (IoT), especially the legal, regulatory, and cybersecurity integration challenges within the context of UAE and Jordan's financial sectors. The objective of the study was to assess the relative impact of the cybersecurity challenges, legal infrastructures, and e-governance maturity on the cyber threats and trust of clientele. The study utilized a quantitative research design, gathering data through a survey distributed to employees and managers within a number of financial institutions. With a data sample of 400 employees, the survey data were analyzed through a variety of methods, such as descriptive statistics, reliability, Pearson correlations, and Structural Equation Modelling (SEM). The study established that the risks posed by inadequate cybersecurity infrastructures substantially increase the threats. Also, the risks posed by inadequate legal regulations and low e-governance maturity do not appear to increase the challenges. Legal adequacy positively impacts trust. Exposure to cyber threats with unmitigated risks and poor legal regulations and low e-governance maturity do not appear to increase the challenges. The study relies on the trust of cyber clientele to validate and uphold the proposed theoretical framework suggesting the need for an integrated approach consisting of high-quality legal regulations, comprehensive governance, and secure advanced cybersecurity to ensure the safe merging of AI and IoT. In addition, the study sheds light on the perspectives of policymakers, regulators, and financial institutions aiming to build safe and reliable digital financial systems in the UAE and Jordan.
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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 | 0.005 | 0.006 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".