Architectural Approaches to Real-Time AI-Based Risk Monitoring Systems for Critical Infrastructure

Ilia Serebriakov

Citation: Ilia Serebriakov, "Architectural Approaches to Real-Time AI-Based Risk Monitoring Systems for Critical Infrastructure", Universal Library of Engineering Technology, Volume 02, Issue 03.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This article analyzes architectural approaches to building real-time risk monitoring systems in the context of the development of intelligent technologies in critical infrastructure. The study is conducted in the format of a systematic review and analytical synthesis of academic publications focusing on the application of artificial intelligence in monitoring systems, the specifics of stream data processing, distributed computing, and mechanisms of explainability and control. The primary focus is on the relationship between the characteristics of the digital environment, the logic of architectural organization of information processing, and the formation of risk signals. Key factors determining monitoring effectiveness are examined, including data quality, algorithm performance, distribution of computation, and control mechanisms. It is established that the influence of intelligent technologies is indirect and is realized through changes in the structure of information formation across system levels. It is shown that risk monitoring ceases to be a procedure of deviation detection and acquires a systemic character, being formed within the architecturally coordinated interaction of data, algorithms, and governance. An original model is developed, reflecting the sequential interaction of data streams, analytical models, mechanisms of explainability, and control in the process of risk signal formation. The results obtained make it possible to consider risk monitoring as an architecturally determined process that defines the stability and reliability of system functioning. The article will be useful for researchers in intelligent systems, specialists in the design of distributed architectures, and practitioners involved in the development and implementation of monitoring systems in critical infrastructure.


Keywords: Risk Monitoring, Critical Infrastructure, Artificial Intelligence, System Architecture, Stream Data, Distributed Computing, Explainability.

Download doi https://doi.org/10.70315/uloap.ulete.2025.0203022