An NLP-Based Method for Assessment of GDPR Compliance in Data Privacy Agreements
Wuqiang Shen, Tao Dai, Lei Cui, Chuanmao Xu · 2024
In the digital era, the massive utilization of personal data has raised significant privacy and compliance concerns, underscored by frequent data breaches and privacy violations. The introduction of the General Data Protection Regulation (GDPR) by the European Union marks a stringent global standard for data protection, emphasizing the need for rigorous compliance mechanisms. Traditional methods for assessing GDPR compliance within Data Processing Agreements (DPAs) are often manual, time-consuming, and prone to human error. This paper introduces a novel NLP-based methodology that leverages the BERT text classification model to automate the assessment of GDPR compliance in DPAs. Our method not only expedites the process but also enhances accuracy and reduces the potential for oversight. Empirical results demonstrate the method's efficacy, with high precision and recall in detecting compliance issues, indicating a significant step forward in automating legal document analysis. This advancement holds substantial promise for streamlining GDPR compliance checks, providing legal professionals and data protection officers with a powerful tool that augments the accuracy and efficiency of their work.