The use of artificial intelligence for ensuring the security of data centers

Abstract

Background. In today's world, cyber threats to data centers (DCs) have become a significant concern due to their growing complexity and adaptability. Artificial Intelligence (AI) can greatly enhance monitoring and security processes, ensuring real-time threat detection and response. The aim of this study was to evaluate the effectiveness of AI methods for improving DC security and to demonstrate their practical applications.
Methods. In today's world, cyber threats to data centers (DCs) have become a significant concern due to their growing complexity and adaptability. Artificial Intelligence (AI) can greatly enhance monitoring and security processes, ensuring real-time threat detection and response. The aim of this study was to evaluate the effectiveness of AI methods for improving DC security and to demonstrate their practical applications.
Results. The use of the behavioral anomaly analysis method achieved an accuracy of 89% in detecting suspicious activities, while deep neural networks demonstrated up to 92% accuracy in predicting new threats. The average response time to potential attacks was reduced from 25 to 8 seconds, enabling timely blocking of suspicious actions. Practical applications include integrating these models into monitoring systems, allowing automatic threat detection and mitigation, reducing reliance on human intervention, and minimizing false positives.
Conclusions. The study confirmed the effectiveness of AI as a tool for ensuring high levels of DC cybersecurity. AI enables quick and precise threat detection, preventing their realization and minimizing potential damage. However, to fully harness AI's potential, it is essential to consider the need for high-quality training data, computational resources, and algorithm transparency. Future research should focus on refining models to enhance their resistance to manipulation and adaptability to new types of threats.