Data Protection by Design Tool for Automated GDPR Compliance Verification Based on Semantically Modeled Informed...

Area of Science:

• Computer Science
• Information Science
• Law

Background:

• The General Data Protection Regulation (GDPR) enforcement in May 2018 introduced significant data protection challenges for organizations.
• Complexities in consent management and the need for large-scale compliance verification pose hurdles.
• GDPR's emphasis on data protection by design and interoperability necessitates system redesign.

Purpose of the Study:

• To present a scalable tool for automated compliance verification and auditability under GDPR.
• To address the technical challenges of implementing data protection by design principles.
• To facilitate GDPR compliance through informed consent modeling.

Main Methods:

• Development of a data protection by design tool utilizing a knowledge graph for informed consent modeling.
• Implementation of a regulation-to-code process to translate GDPR requirements into software code.
• Demonstration of the tool's effectiveness in the insurance and smart cities sectors.

Main Results:

• The tool enables automated compliance verification and enhances auditability for GDPR.
• The knowledge graph model effectively represents complex consent scenarios.
• The regulation-to-code approach streamlines the translation of legal requirements into technical measures.

Conclusions:

• The presented tool offers a scalable solution for automated GDPR compliance.
• The approach supports data protection by design and can be adapted to various domains.
• Automated verification and auditability are crucial for modern data protection frameworks.