Custom Detection Builder
Build Detections Without the Complexity
Go from threat to detection effortlessly with a low-code use case builder that allows your SOC to construct their custom threat detection scenarios without the coding & deployment complexity.
AI-Powered Use Case Development
Simplified DIY Detections
Enterprise SOC teams have moved beyond relying solely on vendor-provided black box detections to prioritizing evidence-based security. You need the ability to customize threat detection content based on your unique environment and threat priorities, but the manual detection engineering lifecycle to achieve this often takes days or months.
With our Custom Detection Builder, you can develop and deploy high-fidelity, behavioral-based detections for your team's custom use cases in minutes across the data platforms choose. The low-code builder reduces the burden on your team to be experts in SPL, SQL, and KQL.
Automate detection-as-code to simplify your detection engineering lifecycle and easily manage code changes with version control. When you edit a use case rule, we create a new version for that rule, and you can revert and deploy, update and deploy or do rapid testing with ease while maintaining full version history.
Easily convert your existing detection content rules from Splunk or Azure to Snowflake. We also let you provide custom tags and use AI algorithms to map your pre-existing content to the MITRE ATT&CK framework.
AI-Assisted Building and Tuning
Building new detections begins with leveraging accessible telemetry, often from a variety of sources including endpoint data, network traffic and cloud services.
With our Custom Detection Builder you can efficiently collect what you need to start building your rule from Splunk, Snowflake or Azure while bypassing the intricacies of data parsing & normalization. We handle that for you.
Monte Copilot is our AI assistant embedded into the query builder experience and was designed to enhance SOC efficiency and breakthrough through skills gap limitations by automating SPL & SQL generation through complete understanding of the schema & data models connected into the platform.
Our Tuning Insights feature leverages a series of AI algorithms based on common false positive strings and patterns to determine which rules have specific terms that repeat often and are likely to be unnecessary noise. We apply these algorithms on a per-use case basis, so you always know the impact upstream and downstream; meaning what rule’s behavior is modified, and how many fewer alerts your triage team can expect.
Multi-Stage Detection Building
The Custom Detection Builder delivers an intelligent threat-pattern capability that automates the deployment of use cases aligned to not only one specific threat behavior but also across multi-stage behavior scenarios.
You can even implement cross-platform correlations to hunt more efficiently across disjointed logging repositories, multi-clouds and data lakes.
Your SOC can design detection strategies across all the kill chain phases to close visibility and threat detection gaps in a more timely matter, reducing your teams’ MTTD/R.
With Anvilogic you can finally have a correlated narrative for detection & resolution without needing to centralize your data or rip-and-replace existing investments.
ESG: Trends in Modern Security Operations
77% of security pros desire new ways to engineer detection engineering rules and 60% feel the time spent on detection engineering is more valuable than nearly any other activity.
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