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Microsoft Sentinel Reviews & Insights

Score8.6 out of 10

155 Reviews and Ratings

Who Buys & Uses Microsoft Sentinel

Based on 6,313 HG Insights installations.

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Insights from Microsoft Sentinel Reviewers

Based on 16 verified reviews published in the last 18 months


Synthesised from 16 reviews | Last Published May 27, 2026


Microsoft Sentinel is primarily adopted as a comprehensive Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) solution, providing extensive visibility into security operations. In TrustRadius reviews, organizations leverage its AI-powered threat detection and automated response mechanisms, which 69% of reviewers highlight for increasing accuracy and ensuring timely actions against security incidents. Its seamless integration within the broader Microsoft ecosystem, including Microsoft Defender and Azure, is a frequently cited strength, offering a unified approach to security.

Reviewers also note Sentinel's contribution to operational efficiency through automation and its ability to support business growth by scaling services. However, a recurring drawback is the complexity of its licensing model and the high cost associated with log retention, which 7 out of 16 reviewers specifically mention. Other concerns include challenges with non-Microsoft integrations, a steep learning curve, and initial issues with alert volume and false positives, leading to mixed sentiment regarding its overall return on investment.


  • AI-powered threat detection and automated response capabilities
  • Seamless integration with the broader Microsoft ecosystem (e.g., Defender, Azure)
  • Unified view of security data and strong correlation abilities for investigations
  • Streamlined investigation process, enhancing speed and efficiency
  • Comprehensive log management and data ingestion from diverse sources
  • Complex licensing model and high consumption-based log retention costs
  • Limitations or challenges with robust integration in non-Microsoft environments
  • Steep learning curve, particularly for KQL, and inconsistent documentation
  • User interface and navigation described as cluttered or difficult to traverse
  • Initial high volume of alerts and false positives, with a desire for native alerting
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