Microsoft Sentinel (SIEM)

Microsoft Sentinel is a cloud-native SIEM (Security Information and Event Management) and SOAR (Security Orchestration, Automation, and Response) solution. It helps organizations detect, investigate, and respond to security threats across their entire digital estate—including on-premises, Azure, and other clouds. The core business problem it solves is the overwhelming volume of security alerts and log data that security teams face. Without a centralized SIEM, analysts would have to manually check logs from firewalls, servers, endpoints, and applications, which is slow and error-prone. Sentinel ingests all that data into one place, uses analytics to filter out noise, and surfaces only the most critical threats.

Pricing is consumption-based: you pay for data ingestion (about $2.50/GB for the first 10 GB, then lower tiers) plus separate charges for Logic Apps (playbooks) and extended retention. There is no upfront cost.

Traditional on-premises SIEM solutions (like Splunk on-prem or IBM QRadar) require you to provision and manage servers, storage, and networking. Scaling involves buying more hardware. They also require manual updates and maintenance. In contrast, Sentinel is fully managed by Microsoft—no infrastructure to maintain. Scaling is automatic: you just send more data and pay for it. Sentinel also integrates natively with Azure and Microsoft 365, reducing complexity. However, on-premises SIEMs may offer more customization in certain areas (like custom parsers) and may be preferred for air-gapped environments with no cloud connectivity.

# Create a Log Analytics workspace
az monitor log-analytics workspace create --resource-group myRG --workspace-name sentinel-workspace --location eastus

# Enable Sentinel on the workspace (this is actually done via the portal, but you can use ARM templates)
az deployment group create --resource-group myRG --template-file sentinel-deploy.json

Scenario 1: Retail chain with hybrid infrastructure A retail company with hundreds of stores uses a mix of on-premises servers, Azure VMs, and Office 365. They need to detect point-of-sale (POS) malware and insider data theft. They deploy Sentinel and connect Azure AD, Office 365 audit logs, and on-premises Windows Event logs via the Log Analytics agent. They enable the built-in 'Suspicious process execution' rule. One day, Sentinel detects a process on a store server that is communicating with an unknown external IP. An incident is created, and the playbook automatically isolates the server from the network and sends an alert to the store manager. The security team investigates and finds a POS malware variant. The incident is resolved in minutes instead of hours.

Scenario 2: Financial services compliance A bank must comply with PCI DSS and SOX by monitoring all access to financial data. They use Sentinel to ingest logs from Azure SQL Database, Azure Key Vault, and on-premises file servers. They create custom analytics rules to detect unusual query patterns (e.g., a user downloading thousands of records). Sentinel also integrates with their SIEM tool for legacy systems. The bank sets up a playbook that triggers when a 'high severity' incident is created, automatically opening a ticket in their ITSM system and sending a message to the compliance team. They also use workbooks to generate monthly compliance reports. When an employee accidentally accesses a restricted database, Sentinel detects it and the playbook revokes their access temporarily, preventing a potential breach.

Scenario 3: SaaS provider defending against DDoS A SaaS company hosts its application on Azure and is frequently targeted by DDoS attacks. They use Azure DDoS Protection Standard and Sentinel. They connect Azure DDoS Protection logs and Azure Firewall logs to Sentinel. They create an analytics rule that correlates a spike in incoming traffic with a sudden increase in failed connections. When a DDoS attack is detected, a playbook automatically scales out the application instances and notifies the operations team via Teams. Sentinel also provides a workbook that visualizes attack patterns over time. One misconfiguration: if the analytics rule is too sensitive, it might trigger false positives during legitimate traffic spikes (e.g., product launch). The team must tune the rule threshold to avoid alert fatigue.

Common pitfalls: (1) Not enabling enough data connectors—you miss threats. (2) Using default retention (31 days) for compliance requiring longer retention. (3) Underestimating costs due to high log volume—set budgets and cost alerts. (4) Not tuning analytics rules, leading to too many false positives or missed detections.