Year: 2026
Type: article
Abstract: As digital technologies increasingly shape organizational infrastructures, privacy has become a critical policy concern. Although the EU General Data Protection Regulation mandates a Privacy by Design approach, organizations often struggle to translate legal requirements into actionable system designs. Privacy Patterns offer reusable design guidance, yet their adoption remains limited due to fragmented sources and reliance on expert interpretation. This paper reviews existing approaches to Privacy Pattern selection and identifies key limitations from a practitioner's perspective. We propose a Design Science Research artifact in the form of a Retrieval-Augmented Generation framework that enables natural-language interaction with a language model to deliver context-specific Privacy Pattern recommendations grounded in curated knowledge bases. An initial feasibility assessment confirms retrieval correctness (P@3 = 1.0) and on-premises deployability, providing early evidence for the architecture's practical potential.
Institution: Pforzheim University of Applied Sciences
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7164719910