Anvilogic

Automatically correlates disparate security signals from multiple sources using machine learning to identify genuine threats and reduce false positives. Reduces alert noise by 70-80% compared to rule-based detection systems.

Executes pre-defined or dynamically generated response playbooks to contain and remediate detected threats without manual analyst intervention. Automates containment actions across 200+ integrated security tools.

Provides a visual, low-code interface for creating complex security response playbooks without requiring Python or advanced programming skills. Enables security teams to build automation in hours rather than weeks.

Seamlessly integrates with 200+ security and IT tools including EDR, SIEM, cloud platforms, and ticketing systems to orchestrate coordinated responses across the entire security stack. Reduces tool fragmentation in modern SOCs.

Continuously learns from security events and analyst feedback to improve threat detection accuracy over time. Adapts detection models to organization-specific threat patterns and infrastructure characteristics.

Reduces manual analyst workload by automating alert triage, threat correlation, and response execution. Addresses alert fatigue by filtering noise and prioritizing genuine threats for human review.

Accelerates incident response by automating detection, correlation, and containment workflows. Reduces the time between threat detection and remediation action.

Collects and normalizes security event logs from 200+ disparate sources into a unified format for analysis and correlation. Handles the complexity of heterogeneous security tool outputs.

Provides correlated threat data and historical context to support security analyst investigations. Enables rapid forensic analysis by correlating events across multiple sources.

Provides visibility into security operations metrics including alert volume, detection accuracy, response times, and threat trends. Enables data-driven decision making for security programs.