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

TEXT START: In 2024, Saudi Arabia's Personal Data Protection Law (PDPL) came into force.

The Dissection

This paper audits the visible paperwork of 100 Saudi e-commerce websites. Its central finding—only 31% declared all four examined items—demonstrates weak privacy-policy formalization, not necessarily weak or strong underlying data protection. It then positions GPT-5 as a scalable compliance scanner and converts the results into recommendations for regulators, businesses, and developers.

The paper is therefore doing two things: measuring a narrow administrative surface and presenting automation as the mechanism for expanding oversight. It is an implementation-barrier study, not an autopsy of whether the law actually constrains data extraction, retention, profiling, or disposal.

The Core Fallacy

It risks confusing declaration with compliance.

A privacy policy can state a retention period, destruction right, copy request mechanism, and complaint channel while the organization ignores every one of them. Conversely, a deficient policy may coexist with practices that satisfy some obligations. The measured variable is documentary disclosure at the policy level—not operational compliance, enforcement, user power, or realized privacy.

The LLM layer compounds the limitation. GPT-5 can classify text at scale; it cannot make a company honor a deletion request, impose sanctions, repair institutional incentives, or prevent data from being harvested in the first place. It automates the inspection of compliance theater. That is useful surveillance of the paperwork, but it is not governance.

Under the Discontinuity Thesis, this is a lag-defense instrument: law and automated auditing attempt to preserve institutional control while the economic system accelerates toward machine-mediated extraction. It may improve visibility and reduce some friction. It does not reverse the underlying concentration of data, computational power, or decision authority.

Hidden Assumptions

• That the 100-site dataset adequately represents Saudi e-commerce.
• That search ranking and local hosting status are analytically meaningful proxies for organizational behavior rather than confounded characteristics.
• That four declared items are sufficient to characterize meaningful PDPL implementation.
• That a policy’s wording corresponds to actual internal processes.
• That human and LLM judgments can be made consistent enough for automated monitoring to deserve regulatory reliance.
• That better detection will produce better compliance, despite no demonstrated enforcement mechanism in the supplied abstract.
• That policymakers, website owners, and developers possess both the incentive and capacity to act on the recommendations.
• That formal legal coordination can keep pace with AI-enabled data collection and institutional asymmetry.

These assumptions turn an enforcement problem into a measurement problem. Measurement is cheaper, cleaner, and easier to publish. It is also the part least capable of changing the power relationship.

Social Function

Primary classification: partial truth and transition management.

Secondary classification: prestige signaling and ideological anesthetic.

The paper provides a legitimate diagnostic: many websites do not even declare basic rights and practices, and LLMs may help regulators inspect large volumes of text. But its framing risks making the system appear governable through better documentation and automated review. The machine becomes an administrative prosthesis for institutions that still lack demonstrated control over behavior.

The Verdict

This is a useful compliance-paper census with a deliberately narrow field of vision. Its 31% figure exposes documentation failure, not the full condition of data protection. GPT-5 can scale the audit, but scaling audits without credible enforcement merely industrializes the discovery of violations. The paper identifies a bureaucratic symptom and offers a faster thermometer; it does not show that the patient can be made healthy.