Detect Multi-Stage Attacks with Microsoft Sentinel Fusion Rules

Modern enterprise environments generate thousands of security alerts daily across identity providers, endpoints, network appliances, and cloud workloads. Individually, most are low-fidelity signals that rarely justify incident response on their own. The real threat emerges when an attacker chains these discrete activities into a multi-stage campaign spanning initial access, credential theft, lateral movement, and data exfiltration. Microsoft Sentinel's Fusion engine addresses this by applying ML correlation across disparate alert sources to automatically detect advanced persistent threats buried in alert noise.

In this lab, you will work with a Sentinel workspace pre-loaded with multi-source security log data simulating a coordinated attack campaign. You will configure Fusion source signals, create scheduled analytics rules with entity mapping and MITRE ATT&CK tactic assignments, investigate the resulting multi-stage incident through the investigation graph, and map the full attack chain to the MITRE ATT&CK framework.

Objectives

Upon completion of this advanced level lab, you will be able to:

• Configure Fusion rule source signals and severity filters to tune multi-stage attack detection
• Create scheduled analytics rules with entity mapping (Account, IP, Host) and MITRE ATT&CK tactic assignments qualifying for Fusion correlation
• Analyze Fusion-generated incidents to understand how low-fidelity alerts were correlated into high-fidelity detections
• Trace the attack chain through the investigation graph, pivoting across entities and timelines
• Map each attack stage to MITRE ATT&CK tactics using Sentinel's coverage view

Who is this lab for?

This lab is designed for:

• Security analysts and SOC engineers preparing for SC-200 certification
• Detection engineers strengthening Sentinel detection engineering skills
• Security operations professionals learning advanced threat hunting and correlation techniques

Familiarity with the Azure Portal, basic KQL, and the MITRE ATT&CK framework is recommended.