AI-Driven Cybersecurity Frameworks for Enterprise Networks: Integrating Machine Learning for Threat Detection and Automated Response

Abstract

Enterprise networks face an ever-evolving threat landscape, characterized by advanced persistent threats, zero-day vulnerabilities, and insider attacks. Traditional cybersecurity mechanisms are increasingly inadequate in addressing these sophisticated challenges due to their reliance on rule-based systems and signature detection. This paper explores the integration of artificial intelligence (AI) and machine learning (ML) into cybersecurity frameworks to enhance real-time threat detection, anomaly prediction, and automated incident response. The proposed AI-driven architecture offers adaptability, scalability, and context-awareness, making it particularly relevant for high-risk domains such as financial systems, healthcare IT infrastructure, and cloud-based enterprise services. The paper presents an in depth review of current AI techniques used in cybersecurity, a proposed intelligent framework, and a comparative analysis of their effectiveness. Original tables and 2D figures are included to illustrate the architecture, datasets, model performances, and use case impacts.

Keywords: Artificial Intelligence, Machine Learning, Cybersecurity, Threat Detection, Anomaly Prediction, Automated Response, Enterprise Networks, Financial Systems, Healthcare IT, Cloud Security