This article examines the transformative role of artificial intelligence in enterprise cybersecurity monitoring, addressing the fundamental challenges that traditional security operations centers face in managing the exponentially growing volume of security events across complex digital environments. The article explores how machine learning approaches for anomaly detection enable organizations to identify threats without explicit programming for each variant, while also addressing the critical problem of alert fatigue through intelligent prioritization and correlation mechanisms. The article analyzes emerging human-AI collaboration models that redefine security workflows and distribute cognitive load optimally between analysts and automated systems, emphasizing the importance of explainable AI for building appropriate trust. Finally, the article examines future directions toward autonomous security response, identifying current limitations and promising approaches for safe partial-automation while considering regulatory frameworks and adversarial adaptation. Throughout the analysis, the article demonstrates how AI integration represents not merely a technological evolution but a strategic necessity for maintaining viable security operations in an increasingly complex threat landscape.

Artificial Intelligence, Cybersecurity Monitoring, Human-Machine Collaboration, Autonomous Response

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