Embracing Azure Sentinel: Our Journey to a Smarter SIEM Solution

Security information and event management — SIEM — is the operational backbone of any mature security programme. But legacy SIEM platforms carry a significant burden: extensive on-premises infrastructure, constant tuning and maintenance overhead, and scalability constraints that make handling modern threat volumes increasingly difficult. When we evaluated our SIEM posture at AW InfraSec, these constraints were impossible to ignore.

Our journey to Microsoft Sentinel (formerly Azure Sentinel) was not without initial hesitation. Cloud-native SIEM was a significant architectural shift. What we found exceeded our expectations in every meaningful dimension.

Traditional SIEM Challenges

Legacy SIEM platforms were designed for a world where infrastructure was predominantly on-premises and threat volumes were manageable with correlation rules written by human analysts. Today's environment looks nothing like that:

• Cloud workloads generate telemetry volumes that on-premises SIEM hardware struggles to ingest cost-effectively
• Hybrid environments spanning Azure, AWS, on-premises, and SaaS applications require connectors and normalization logic that legacy SIEM vendors have not kept pace with
• Analyst talent is scarce, making the manual rule-writing and tuning cycle increasingly unsustainable
• Infrastructure refresh cycles for on-premises SIEM hardware create periodic capital expenditure spikes

Why Microsoft Sentinel

Microsoft Sentinel is a cloud-native SIEM and SOAR (security orchestration, automation, and response) platform built on Azure Log Analytics. Its architectural advantages over legacy platforms are significant:

• Seamless integration with the Microsoft ecosystem — Native connectors for Microsoft Defender, Microsoft 365, Azure AD, and hundreds of third-party data sources require minimal configuration and maintenance
• Elastic scaling — Ingest as much or as little data as your environment generates; cost scales with actual usage rather than hardware capacity
• AI and machine learning threat detection — Built-in UEBA (user and entity behavior analytics) and fusion detection identify complex multi-stage attacks that rule-based detection misses
• Kusto Query Language (KQL) — A powerful, expressive query language for complex log analysis that security analysts can learn quickly

Data Collection and Enrichment

One of Sentinel's most significant advantages is the breadth and quality of its data connector library. We connected the following sources with minimal engineering effort:

• Azure Activity and Diagnostic logs
• Microsoft 365 audit logs including Entra ID sign-in and audit events
• Microsoft Defender for Endpoint, Identity, Office 365, and Cloud Apps
• On-premises Windows Security Events via Azure Monitor Agent
• Third-party network and firewall vendors via Syslog and CEF connectors

The unified log schema and built-in normalization meant our analysts could write queries that span all data sources without managing per-source schema differences — a significant productivity gain over our legacy platform.

Custom Dashboards and Workbooks

Sentinel's workbook framework enables customizable interactive dashboards built directly on top of your log data. We built workbooks for identity risk monitoring, privileged account activity, external sharing events from SharePoint, and geographic sign-in anomaly detection. These dashboards are updated in real-time and accessible to stakeholders without Sentinel portal access through Azure Monitor Workbooks sharing.

Microsoft 365 E5 Integration

For organizations already on Microsoft 365 E5 or E5 Security licensing, the economics of Sentinel become particularly compelling. The E5 license includes Microsoft Defender for Endpoint, Azure AD Premium P2, and Microsoft Cloud App Security — all of which integrate natively with Sentinel. The combined telemetry from these sources, ingested into Sentinel at no additional data ingestion cost for Microsoft data sources, provides exceptional security coverage relative to licensing investment.

A Financial Services Case Study

One of our financial services clients was operating a legacy on-premises SIEM handling approximately 50GB of log data per day. Alert volumes were high, false positive rates were approximately 65%, and analysts were spending the majority of their time investigating alerts that resolved as benign.

After migrating to Microsoft Sentinel:

• False positive rates dropped by over 40% through UEBA-based alert enrichment and Fusion correlation
• Mean time to investigate dropped from hours to minutes for the most common alert types through automated enrichment playbooks
• Infrastructure costs reduced significantly through elimination of SIEM hardware refresh and vendor licensing
• Compliance reporting time reduced through Sentinel's built-in compliance dashboards and automated evidence collection

AW InfraSec Sentinel Services

Our Sentinel practice delivers end-to-end implementation and managed services:

• Data source connectivity and log normalization across hybrid environments
• Custom analytics rule development and UEBA tuning
• Workbook and dashboard development for operational and executive audiences
• SOAR playbook development for automated incident response
• Ongoing managed detection and response, with analyst-staffed alert triage and incident response
• Team training and knowledge transfer for in-house SOC teams

Conclusion

Microsoft Sentinel represents a genuine step-change in SIEM capability for organizations of any size. The combination of elastic scale, deep Microsoft ecosystem integration, AI-powered detection, and the KQL query environment addresses the core limitations of legacy SIEM platforms at a competitive total cost of ownership.

Our initial hesitation about cloud-native SIEM gave way quickly once the implementation was underway. If you are evaluating a SIEM modernization and have a significant Microsoft technology footprint, Sentinel deserves serious consideration.