Mumtathil: Automatic PDPL compliance identification system of Arabic privacy policies documents.
Clinical Snapshot
PICO Framework
| P — Population | Arabic privacy policy documents from websites and applications operating under Saudi Arabian jurisdiction |
| I — Intervention | Mumtathil automated compliance assessment system using multi-label classification with Support Vector Machine |
| C — Comparator | Other machine learning models (CamelBERT, AraBERT) and manual compliance assessment methods |
| O — Outcomes | F-score performance metrics, compliance identification accuracy, time and cost reduction in assessment processes |
Bottom Line
This technology validation study presents Mumtathil, an automated system for assessing Arabic privacy policy compliance with Saudi Arabian data protection law using machine learning. While the reported 92% F-score suggests promising technical performance, the study lacks robust validation methodology, statistical significance testing, and confidence intervals. The focus on Arabic language processing addresses an important gap, but applicability to Australian healthcare contexts is limited without adaptation to local privacy legislation. Healthcare organisations should view this as proof-of-concept for automated compliance assessment rather than a ready-to-implement solution. The methodology could inform development of similar tools for Australian Privacy Principles compliance, but would require substantial adaptation and validation in local contexts.
Key Findings
P Value: Not reported
Effect Size: SVM outperformed transformer models (CamelBERT, AraBERT) in multi-label classification
Primary Outcome: F-score performance of 92% for SVM-based compliance classification
Nnt Or Sensitivity: 92% F-score suggests high sensitivity and specificity for compliance detection
Confidence Interval: Not reported
Clinical Application
Requires technical implementation and adaptation to local privacy regulations Limited direct applicability - would require adaptation to Australian Privacy Principles under Privacy Act 1988 and TGA requirements for therapeutic goods data handling Healthcare organisations and digital health platforms operating in jurisdictions with data protection laws
Abstract
In the digital era, the extensive collection and use of personal data by websites and applications has prompted various countries to implement data protection laws. The Kingdom of Saudi Arabia introduced the Personal Data Protection Law (PDPL) to regulate the handling of personal data. However, manually assessing compliance with these laws is time-consuming, and costly. This paper presents an automated system named Mumtathil (مُمتثِل) that evaluates the compliance of Arabic privacy policies with the PDPL using multi-label classification (MLC) techniques. Our experiments demonstrated that the Support Vector Machine (SVM) outperformed other machine learning models and transformer-based models like CamelBERT and AraBERT, achieving an F-score of 92%. The system addresses dataset imbalance by augmenting the Saudi Privacy Policy Dataset with recent privacy policies and synthetic data generated using ChatGPT. The Mumtathil tool simplifies compliance assessment, reducing time, cost, and effort while enhancing transparency and data protection in the digital landscape.
References
- 1.Mashaabi, M., & Al-Khalifa, H. (2026). Mumtathil: Automatic PDPL compliance identification system of Arabic privacy policies documents. PLoS One. https://doi.org/10.1371/journal.pone.0351074
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