This paper explores modern approaches to the visualization, transformation, and analysis of data obtained from security alarm and video surveillance systems, using a power substation as an example of a critical infrastructure facility. Special attention is given to the integration of heterogeneous data sources, including video streams, alarm signals, telemetry, and access events. The proposed architecture of the software system enables automated image preprocessing, optical character recognition (OCR) using the Tesseract library in Python, and semantic analysis of the extracted textual data. An analytical event processing model is developed, allowing for classification, prioritization, and incident logging. Furthermore, various methods of visual data representation are presented, including heat maps, time-series charts, and tabular reports, all designed to support efficient real-time decision-making. The solutions discussed aim to enhance the level of automation and situational awareness in the security systems of energy infrastructure, contributing to improved operational reliability and response effectiveness.