Using Artificial Intelligence to Automate Information Systems Management

Abstract

The relevance of this research lies in the need to apply artificial intelligence (AI) to automate information systems management, which has become essential for effective organizational performance. The growth in data volume and complexity introduces new challenges, making the management of these systems increasingly resource-intensive. AI integration in such processes allows for optimized operations, providing flexibility, adaptability, and system stability. The study aims to analyze AI methods for enhancing the stability, productivity, and security of information systems, as well as to develop recommendations for their implementation. A systematic review of scientific sources and a comparative analysis of algorithm effectiveness, including deep neural networks, machine learning methods, and natural language processing, were conducted. Findings indicate that deep neural networks are effective for analyzing unstructured data, though they require significant computational resources. For classification tasks, algorithms such as support vector machines have shown efficiency. Based on the analysis, hybrid algorithms and cloud platforms are recommended to reduce the load on local infrastructure. The study’s conclusions highlight the utility of cognitive technologies to improve user interactions, blockchain for enhanced security, and automated anomaly detection for rapid threat response. Future research will focus on optimizing algorithms for heterogeneous data, developing adaptive security systems, and refining models for user behavior prediction