Multiple open data portals offer data that may not appear to violate data privacy or confidentiality laws at first glance. However, a thorough study of these datasets and their relationships with others reveals that confidential or private information may be obtained in certain cases. To address these issues, this article proposes a solution that involves implementing a series of AI-powered modules. The goal of these modules is to analyze the quality of the data and its potential combinations with linked data that could lead to legal non-compliance or data quality issues. Due to the lack of standardization across different open data portals, this model facilitates the improvement of these portals for information extraction and decision-making purposes while ensuring compliance with data privacy and confidentiality laws.