Data sharing across digital services introduces significant privacy and security risks, especially when sensitive information can be correlated across multiple contexts. Traditional techniques of threat modeling approaches often remain manual, fragmented, and lack automated mechanisms to detect such risks in real time. To address these limitations, this thesis applies the LINDDUN Privacy Threat Modeling framework as a methodological foundation for systematically identifying and categorizing privacy threats throughout the system architecture. Building on this systematic order model, this thesis proposes a framework for data sharing that preserves privacy and automated threat analysis, through the integration of Natural Language Processing techniques with risk evaluation based on rules.