4.3.2. Threat Detection and Mitigation in Sentinel
💡 First Principle: Sentinel ingests log and signal data from a wide range of sources — Microsoft 365, Azure resources, on-premises systems, and third-party security tools — then applies analytics rules to correlate that raw data into meaningful incidents, which analysts investigate using built-in workbooks and threat-hunting queries, and can resolve automatically via playbooks.
The workflow in practice: raw logs and alerts flow in from connected data sources, analytics rules (some built-in, some custom, increasingly assisted by AI-driven detections) group related signals into a single incident rather than dozens of disconnected alerts, an analyst investigates using Sentinel's hunting and visualization tools, and a playbook can execute the response automatically or with analyst approval.
⚠️ Exam Trap: An "incident" in Sentinel is a correlated grouping of related alerts, not a single raw alert — the correlation step is what turns scattered signals into something an analyst can act on efficiently.
Reflection Question: Why does grouping dozens of related alerts into one correlated incident make an analyst's investigation faster than reviewing each alert individually?
