Proactive Detection Engineering for Microsoft Sentinel Powered by Anvilogic
Elevate your detection engineering game with Anvilogic’s seamless integration into Microsoft Sentinel, Azure Log Analytics, or Data Explorer. Build your own KQL detections or deploy from our pre-built stack in minutes –– all expertly mapped to MITRE ATT&CK. Unleash the power of sequence-based correlations to stitch alerts and atomic KQL searches, delivering sharper precision and smarter detections.
Build faster, detect smarter, and streamline your SecOps for high-impact coverage.
The World's Best SOC Teams Use Anvilogic
Streamline the Path From Threat to Detection Across Your Azure Ecosystem
Deploy Sequence-Based KQL Detections Faster
.svg)
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.
Automate the Hassle of Tuning With 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 Anvilogic for Sentinel Works
Anvilogic customizes threat models to your unique risks by leveraging key information like your industry, Azure infrastructure, and critical assets during onboarding. You can easily import your existing KQL detection rules to visualize coverage gaps and align your detection priorities, optimizing your Microsoft Sentinel investment. Our platform helps you assess data source gaps and target high-risk areas for threat hunting.
With Anvilogic's Detection Armory, access thousands of pre-built, validated KQL detection rules to further enhance your Sentinel deployment. Our AI Copilot automatically recommends, deploys, and tunes the best-fit detections for your environment, helping to correlate alerts into sequence-based threat scenarios for more effective triage. Our Copilot also continuously monitors and improves detection performance, ensuring your KQL alert volume is optimal and actionable.
With Anvilogic's Detection Armory, access thousands of pre-built, validated KQL detection rules to further enhance your Sentinel deployment. Our AI Copilot automatically recommends, deploys, and tunes the best-fit detections for your environment, helping to correlate alerts into sequence-based threat scenarios for more effective triage. Our Copilot also continuously monitors and improves detection performance, ensuring your KQL alert volume is optimal and actionable.
Learn More
Anvilogic + Azure
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.
.svg)
