Efficient compliance with GDPR through automating privacy policy captions in web and mobile application

Abstract

Ensuring compliance with the General Data Protection Regulation (GDPR) presents significant challenges for organizations, especially those developing web and mobile applications. This study investigates the use of automation to enhance GDPR compliance by generating privacy policy captions through static code analysis and deep learning models. Privacy policy captions offer concise, user-friendly summaries of data processing practices, improving transparency and user trust. The research combines qualitative and quantitative methodologies, including static code analysis of application source codes and the application of neural machine translation models to generate privacy policy captions. Findings indicate that automation can effectively produce accurate, consistent, and comprehensible privacy policy captions that align with GDPR requirements. However, limitations such as tool capabilities, dataset diversity, and user testing scale highlight areas for future research. This study provides practical guidelines for implementing automated privacy policy captions, emphasizing the importance of continuous monitoring and updates to maintain compliance. By leveraging automation, organizations can enhance their data protection practices, build user trust, and achieve efficient GDPR compliance.