Description
In the digital age, compliance with data protection regulations, particularly the General
Data Protection Regulation (GDPR), is crucial for organizations to safeguard personal data
and mitigate legal risks. This thesis delves into the critical link between reference models
and GDPR compliance, addressing key questions to explore current practices and
challenges organizations face.
The study investigates the effectiveness of existing reference models in navigating GDPR
requirements and identifying gaps and persistent challenges in achieving complete
alignment. It explores how organizations integrate reference models to meet GDPR
requirements and the primary challenges encountered.
This study emphasizes the importance of understanding how organizations employ
reference models for GDPR compliance to enhance data protection practices. It aims to
provide valuable insights into organizational challenges, contributing to the development
of more effective and tailored reference models. Challenges lie in interpreting GDPR
requirements, aligning reference models with regulatory intent, and customizing them to
unique data processing activities. Integration with existing systems poses challenges,
necessitating expertise from various domains. Moreover, keeping pace with regulatory
changes and technological advancements, such as Artificial Intelligence (AI), presents
ongoing challenges.
By addressing these challenges, the research contributes to a robust understanding of the
effective utilization of reference models for GDPR compliance, ultimately aiding
organizations in maintaining compliance, minimizing legal consequences, and
safeguarding personal data in the digital landscape.
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