AI-Powered Predictive Analytics in IT: Enhancing System Security and Performance Optimization

Abstract

This study explores the application of AI-powered predictive analytics in the IT sector, focusing on enhancing system security and optimizing performance. With the increasing complexity and volume of data in modern IT systems, predictive analytics has emerged as a crucial tool to anticipate potential security threats and performance issues. Using a qualitative approach, this research employs a comprehensive literature review and library research to analyze current trends, challenges, and best practices in integrating AI with predictive analytics. Findings highlight that AI algorithms, particularly machine learning and deep learning models, have significantly improved the accuracy and efficiency of threat detection and performance management. These technologies enable real-time monitoring, anomaly detection, and predictive maintenance, which are critical in reducing downtime and preventing cyberattacks. Additionally, the study identifies key obstacles, such as data quality, privacy concerns, and the need for specialized skills in implementing AI-driven analytics. The research concludes that despite these challenges, AI-powered predictive analytics holds substantial potential for IT environments, offering proactive solutions for maintaining system integrity and optimizing resource usage. By synthesizing these insights, this study contributes to the evolving discourse on AI’s role in IT management, providing a framework for organizations to enhance their systems' resilience and operational efficiency.