Custom Microsoft Sentinel SIEM Enabled Non-Standard Data Collection and Cut False Positives by 20%

Summary

A US-headquartered B2B IT services provider wanted to enhance its existing Microsoft Sentinel SIEM deployment across a hybrid infrastructure to improve visibility and streamline SOC operations. INNERLUXES onboarded previously unsupported, business-critical data sources and fine-tuned Sentinel for high-performance threat detection — cutting false positives by 20%, expanding coverage, and lowering SIEM operating costs.

About the Client

The Client is a B2B IT services provider headquartered in the US, delivering technology implementation and advisory services to businesses worldwide.

The Challenge

As part of its Information Security Management System (ISMS), the Client sought to enhance its existing Microsoft Sentinel SIEM deployment to improve visibility and streamline SOC operations. Operating a hybrid infrastructure, the Client faced two key challenges:

• Limited data collection and visibility. Key security data sources (firewalls, domain controllers, and identity systems) were not integrated into the SIEM, resulting in blind spots that affected threat detection and SOC effectiveness.
• Integration and processing of non-standard systems. The Client's on-premises systems lacked native support for audit log processing and threat-hunting queries and rules, which affected meaningful security monitoring.

The Client commissioned INNERLUXES to fine-tune its Microsoft Sentinel deployment and resolve the integration issues, trusting our expertise in SIEM deployment and customization.

The Solution

Data Onboarding and Integration

As part of the SIEM customization process, INNERLUXES's experts:

• Integrated critical data sources — including firewalls, domain controllers, and identity systems — by configuring data collection rules and mappings.
• Created custom data connectors and tables using Data Collection Rules (DCRs), transformation rules, and Azure Resource Manager (ARM) templates to onboard non-standard data sources. Following Advanced Security Information Model (ASIM) standards, the engineers developed new universal correlation rules that supported diverse data sources, simplifying rule management while expanding detection coverage.
• Designed and implemented custom parsers for accurate log interpretation across custom data sources.
• Integrated Microsoft Sentinel with both Azure cloud services and on-premises systems for unified security monitoring.

With all data sources integrated and mapped, the foundation was in place for the next stage — fine-tuning Microsoft Sentinel's configuration and optimizing it for high-performance threat detection and response.

Sentinel Configuration and Optimization

Building on the newly onboarded data, INNERLUXES's team refined Microsoft Sentinel's configuration and performance to ensure accurate alerting and efficient threat and incident investigations:

• Developing Kusto Query Language (KQL) queries for log analysis and advanced threat hunting.
• Creating analytics rules, workbooks, and playbooks to enable proactive threat detection and automated incident response.
• Designing dashboards and visualizations to provide actionable security insights.
• Fine-tuning data retention and archival settings to balance compliance, cost, and investigative needs.
• Developing suppression rules to filter known benign patterns and approved activities.
• Reviewing and improving entity mappings to better correlate alerts with users, hosts, and IPs.
• Optimizing log processing performance for faster analysis and efficient threat hunting.

These enhancements improved Microsoft Sentinel's efficiency, ensured precise alerts, and streamlined threat investigations.

The Results

• A 20% reduction in false positives due to fine-tuned correlation and suppression rules.
• Expanded infrastructure coverage, enabling detection of previously unnoticed security incidents.
• Regulatory compliance with ISO/IEC 27001 and SOC 2 requirements for security monitoring of critical systems.
• Faster incident response and elimination of human error through automated playbooks (e.g., malicious IP blocking, incident escalation).
• Higher SOC analyst productivity due to alert-noise reduction, automated workflows, and tailored dashboards.
• Lower SIEM operation costs through optimized alerting, data retention, and archival settings.

Technologies and Tools

Microsoft Sentinel, Microsoft Azure, Data Collection Rules (DCRs), Azure Resource Manager (ARM) templates, Advanced Security Information Model (ASIM), Kusto Query Language (KQL), analytics rules, workbooks, playbooks.