AI AND MACHINE LEARNING IN NETWORK SECURITY: CURRENT LANDSCAPE AND FUTURE HORIZONS

Abstract

The revolutionary effects of machine learning and artificial intelligence technologies on network security are examined in this thorough article. The article looks at how threat detection, prevention, and response processes across organizational cybersecurity frameworks are being revolutionized by these cutting-edge technologies. It examines existing implementations and demonstrates how AI-powered security solutions significantly increase threat detection accuracy, reaction times, and cost-effectiveness. Important issues including false positive rates, data quality standards, and the technical skills gap are also covered in the article. Along with examining the development of unified security ecosystems, it also looks into new developments in predictive analytics, adaptive learning systems, and natural language processing integration. In an increasingly complex threat landscape, this article offers insightful information about how enterprises can use AI and ML to improve their security posture.