Cyberattack Detection and Automated Response Using Microsoft Azure Sentinel

The growing sophistication and volume of cyberattacks have outpaced the capacity of manual security operations to detect and contain threats within an acceptable time window. Cloud-native Security Information and Event Management (SIEM) platforms combined with Security Orchestration, Automation, and Response (SOAR) capabilities have emerged as a response to this gap, enabling organizations to correlate telemetry across large, distributed environments and to trigger automated containment actions without waiting for manual analyst intervention. Microsoft Azure Sentinel (now marketed as Microsoft Sentinel) is one such platform, combining a cloud-scale data lake, machine-learning-based analytics, and low-code automation playbooks within a single service. This article examines how Azure Sentinel can be configured to detect a range of common cyberattack scenarios and to trigger automated response actions that contain threats before they escalate, spanning intrusion detection research, SIEM and SOAR architecture studies, threat intelligence frameworks including MITRE ATT&CK, and automated incident response systems, the article proposes an end-to-end detection-and-response pipeline architecture for Sentinel, compares its core components against their underlying detection and response functions, and maps common attack scenarios to specific Sentinel-based detection logic and automated mitigations. The findings indicate that Sentinel's integration of data aggregation, behavioral analytics, and orchestrated response substantially reduces mean time to detection and containment relative to manually operated security operations, provided that organizations invest in tuning analytics rules, mapping detection logic to a recognized threat taxonomy, and maintaining human oversight of automated response actions. The article concludes with practical recommendations for security operations teams adopting Azure Sentinel.