As part of the RETURN project, a model for assessing landslide susceptibility through logistic regression within a GIS environment has been developed, aimed at supporting public authorities and professionals in managing ground instability risks. The model utilizes freely accessible national-scale datasets to ensure high transferability and transparency of results. The analysis is implemented in Python and integrated into GRASS GIS, with the objective of automating the workflow and making the procedure accessible even to non-expert users. The methodology was tested in the Province of Savona (Liguria, Italy), using eight predisposing factors and landslide data from the IFFI inventory. The results demonstrated reliability exceeding 75% in most cases. The resulting susceptibility maps are reclassified into three qualitative categories—low, medium, and high susceptibility—to improve interpretability. The case study highlighted both the strengths and limitations of the approach, notably the need to standardize data and procedures to ensure applicability at broader scales. Ongoing development efforts are focused on enhancing the identification of relevant factors and minimizing subjectivity in data preparation. The automation of the model paves the way for extensive testing across different areas and geomorphological settings, contributing to the development of a robust tool for landslide risk management.