USE OF ARTIFICIAL INTELLIGENCE IN CYBER THREAT DETECTION AND GEOSPATIAL DATA INTEGRITY PROTECTION IN GIS ENVIRONMENTS

Abstract

The development of Geographic Information Systems (GIS), cloud services, Internet of Things technologies and artificial intelligence has increased the operational importance of geospatial data. At the same time, enterprises using GIS face cyber threats that may disrupt not only IT infrastructure, but also the integrity of spatial data used in decision-making. This article evaluates the effectiveness of artificial intelligence in cyber threat detection and geospatial data integrity protection. The study is based on a single case study of a medium-sized enterprise using GIS, GPS, IoT and SIEM infrastructure. A traditional rule-based system was compared with an AI-supported architecture using quantitative indicators: classification accuracy, precision, Recall, F1-score, false positive rate, mean detection time, integrity violation detection rate and the proposed GIS Integrity Index. The empirical description was supplemented with aggregated operational values adopted as expert-based validation assumptions within the case-study protocol. These values were used to increase methodological transparency, clarify the comparative procedure, and ensure consistent interpretation of the results obtained for the traditional and AI-supported systems. The results indicate improved threat detection and data integrity monitoring following the implementation of AI mechanisms. In the validation set with controlled label consistency, classification accuracy increased from 83.4% to 95.8%, Recall from 80.1% to 95.5%, IDR from 68% to 95%, and GII from 0.91 to 0.98. The findings should be interpreted in the context of a single case study and require further validation in other organizational environments.