Minimizing financial risks of cloud cost volatility through automated resource control

Abstract

In the context of enterprises’ digital transformation, the use of cloud technologies is accompanied by growing financial uncertainty due to volatility in resource consumption, the complexity of pricing models, and a high dependence on global digital infrastructure providers. Limited transparency into costs and fragmented financial controls in cloud environments increase the risk of budget overruns, unproductive expenditures, and reduced overall economic efficiency. In this regard, the need to develop integrated mechanisms for managing digital resources to ensure financial stability and the predictability of cash flows becomes increasingly relevant. The purpose of this article is to substantiate a methodological approach to minimizing financial risks associated with the use of cloud services through the implementation of an automated cost control system and its integration with enterprise financial management tools. The methodological framework of the study is based on systemic and structural-functional analysis, comparative generalization, economic and statistical analysis, managerial process modeling, and logical synthesis. The research employs analytical reports from international consulting companies, statistical data on the development of the cloud services market, and generalized practices of FinOps and digital control instruments. The article systematizes the main factors contributing to financial instability in cloud environments, substantiates the impact of market concentration on costs, and identifies key risks to enterprise digital infrastructure. An integrated model of automated resource control is proposed that combines monitoring mechanisms, budget regulation, analytical data processing, and financial load forecasting. An operational algorithm for cloud cost management systems is developed, and the relationship between automation tools, types of financial risks, and economic outcomes of their application is generalized. The practical value of the study lies in the applicability of the proposed automated control model to enhance financial resilience, reduce unproductive expenditures, and establish an effective cloud resource management system in the digital economy.