Evaluating PDPL Compliance in E-Commerce Websites: Insights and Lessons Learned from Human and LLM Analyses

Abstract

In 2024, Saudi Arabia's Personal Data Protection Law (PDPL) came into force. However, little work has been done to assess its implementation. In this paper, we analyzed 100 e-commerce websites operating in Saudi Arabia against the PDPL, examining the presence of a privacy policy and, if present, the policy's declarations of four items pertaining to personal data rights and practices: 1) personal data retention period, 2) the right to request the destruction of personal data, 3) the right to request a copy of personal data, and 4) a mechanism for filing complaints. Our results show that, despite national awareness and support efforts, a significant fraction of e-commerce websites in our dataset are not fully compliant with the examined PDPL requirements at the privacy policy level: only 31% of websites in our dataset declared all four examined items in their privacy policies. Second, the majority of top-ranked e-commerce websites in our dataset (based on search results order) and those hosted on local e-commerce hosting platforms exhibited significantly higher non-compliance rates than mid- to low-ranked websites and those not hosted on local e-commerce platforms. Third, we explored the use of Large Language Models (LLMs) as an automated tool for privacy policy analysis to measure compliance with the PDPL, using the GPT-5 model. We highlight the potential of LLMs and suggest considerations that can inform future benchmarks aimed at improving LLM-based automated analysis for privacy policies. Our results provide a step forward in understanding implementation barriers to data protection laws, especially in non-Western contexts. We provide recommendations for policymakers, regulators, website owners, and developers seeking to improve data protection practices and automate compliance monitoring.