Modern Detection, Smarter Triage
Built for Microsoft Sentinel
Deploy high-fidelity KQL --> Microsoft Sentinel, Azure Log Analytics, Data Explorer and beyond. Correlate alerts into attack stories. Triage with AI, not noise.
The World's Best SOC Teams Use Anvilogic
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Unify Detection and Response
Across Your Azure Stack
Deploy Sequence-Based KQL Detections Faster
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Generate powerful KQL detections in minutes using our Low-Code Detection Builder to precisely target behavioral attack patterns for laser-focused precision and impactful coverage without the hassle.
Correlate multi-stage attack sequences with our Custom Detection Builder that simplifies advanced KQL with Detection-as-Code for high-impact, high-fidelity detections.
Access to thousands of out-of-the-box KQL detection use cases that can be easily deployed and customized to suit your threat priorities.
Alleviate detection engineering management with automatic MITRE ATT&CK mapping, version control, custom tagging, integrations, and more so you can focus on high-impact tasks.
Assess Detection Coverage Gaps
Easily import your Sentinel rules and map them to MITRE ATT&CK to visually track coverage with our rule coverage and assessment analysis from a single detection management platform.
Force-multiply you efforts with Anvilogic’s Detection Armory, our library of out-of-the-box rules tested and validated by our purple team, and our AI Copilot that suggests and deploys detections to Sentinel.
Leverage the Anvilogic platform to create threat models customized to your risks, using information such as your industry, infrastructure, Microsoft assets, and regions.
Visualize coverage gaps in real time to prioritize hunting efforts and focus areas while identifying data source gaps that limit detection improvements.
Next-Level KQL Rule Optimization
Constantly evolving data formats and new telemetry can complicate your detection logic. Detections need regular updates and fine-tuning to stay effective, which often takes hours and even dedicated FTEs to execute to completion.
We’ve experienced this pain and tackled it head-on with our machine learning (ML) models, which continuously monitor data feed health, provide hunting and tuning insights, and deliver allowlist recommendations whenever alert volumes go up.
Anvilogic’s Copilot provides how-to guidance on correlating your detection output into threat scenarios that create better triage outcomes, while also continuously tuning, monitoring health, and reviewing detection results to improve accuracy.
Leverage our Detection Engineering Lifecycle Workbench, equipped with advanced version management and control features that easily tag, enrich, and seamlessly track the evolution of your detections.
How an AI SOC for Microsofts Shops Works
Anvilogic plugs into Microsoft Sentinel and tailors threat models to your environment using your Azure setup, critical assets, and industry context. During onboarding, we scan your existing KQL detections, show you what’s missing, and surface gaps in coverage and data sources.
You can pull from thousands of pre-built, validated KQL detections in our Detection Armory or optimize what you already have. Our platform doesn’t just recommend and tune detections, it triages them by filtering out false positives with 98% accuracy, links related alerts into full attack sequences, and flags what’s truly worth investigating. All alerts flow into a single triage view across Sentinel, Defender, and other security vendor alert sources including other data platforms outside the Microsoft ecosystem like Splunk, Snowflake and Databricks.
You can pull from thousands of pre-built, validated KQL detections in our Detection Armory or optimize what you already have. Our platform doesn’t just recommend and tune detections, it triages them by filtering out false positives with 98% accuracy, links related alerts into full attack sequences, and flags what’s truly worth investigating. All alerts flow into a single triage view across Sentinel, Defender, and other security vendor alert sources including other data platforms outside the Microsoft ecosystem like Splunk, Snowflake and Databricks.
See Integrations
Case Study
Leading Food & Beverage Manufacturer
Learn how a U.S. food manufacturer cut SIEM alerts by 99% and increased MITRE ATT&CK coverage by 40% without adding headcount or replacing security tools.
Case Study
International Appliance Company
By providing visibility across their Microsoft Sentinel feeds and enhancing detection engineering efficiency, Anvilogic assisted the SOC team in achieving a 52% increase in their MITRE ATT&CK coverage within two weeks.
Anvilogic + Azure
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