Automating the SOC: How AI Is Transforming Threat Detection and Response
Keywords:
Artificial Intelligence, Security Operations Center, Machine Learning, Threat Detection, Incident Response, Security Automation, SIEM, SOAR, Deep Learning, Cybersecurity Analytics, Automated Threat Hunting, Advanced Data Analytics.Abstract
Security Operations Centers (SOCs) play a critical role in protecting modern digital infrastructures against increasingly sophisticated cyber threats. However, the growing volume of security alerts, complex attack patterns, shortage of skilled cybersecurity professionals, and limitations of conventional detection mechanisms have created significant operational challenges. Artificial Intelligence (AI) and Machine Learning (ML) are transforming SOC operations by enabling intelligent threat detection, automated alert prioritization, behavioral analytics, incident correlation, and rapid security response. This research examines the integration of AI-driven automation into SOC environments, focusing on the application of supervised learning, unsupervised learning, deep learning, natural language processing, and large language models for advanced cybersecurity analytics. The study proposes an AI-enabled SOC architecture that combines Security Information and Event Management (SIEM), Security Orchestration, Automation, and Response (SOAR), endpoint telemetry, threat intelligence, and automated investigation mechanisms. An experimental evaluation framework is developed to compare conventional rule-based monitoring with an AI-assisted detection and response approach using representative security event scenarios. Evaluation parameters include detection accuracy, precision, recall, F1-score, false-positive rate, alert processing time, and incident response latency. The proposed framework demonstrates how intelligent correlation and automated triage can improve operational efficiency while maintaining human oversight over high-impact security decisions. The research further investigates challenges associated with adversarial attacks, model drift, data privacy, explainability, and automated containment. The findings establish that AI-driven SOC automation represents an important direction for advanced cybersecurity operations, particularly when machine intelligence is integrated with reliable security policies, continuous monitoring, and analyst validation.
