Anomaly Detection

Proactively analyze real-time performance data streams to find & identify anomalous infrastructure behavior and take quick action.

Identifying Anomalies with Legacy Tools Doesn’t Work

As modern IT environments continuously evolve and become more complex, it is becoming more difficult for traditional monitoring tools and techniques to detect problems. Static thresholds and correlation rules do not allow organizations to find and detect anomalous behaviors in these dynamic environments. However, these behaviors still need to be addressed as they can be early indicators of performance issues and service outages.

Machine Learning Models to Identify Anomalous Behavior

Grok’s anomaly detection algorithms enable organizations to reduce their reliance on static management tools to better isolate anomalous behavior. This type of behavior can signal potential failures, outages or performance degradation. Grok continuously learns and detects subtle changes in patterns that are not obvious or easily detected by relying on preprogrammed thresholds.

Industry-Leading Neuroscience Approach Delivers Most Effective Solution

Grok utilizes an industry-leading HTM (Hierarchical Temporal Memory) algorithm to detect anomalies. This approach was developed in partnership with Numenta, a world-renowned organization in the fields of neuroscience and machine intelligence. The HTM approach mimics the architecture and processes of the human brain and is considered the most accurate anomaly detection algorithm in the industry. Its ability to provide less noise, fewer false positives and more error-free detections from streaming data are key reasons that this approach sets us apart from other AIOps solutions.

Anomaly Algorithms and IT Experience Provides Results and Agility

Grok’s HTM anomaly detection algorithm learns and models streaming performance telemetry data from your IT infrastructure (servers, network devices, applications, etc.). Grok then automatically builds models of the metric data to assign anomaly scores to differentiate between the levels of unusual behavior. This approach eliminates any static thresholds and accounts for seasonality, allowing businesses to focus on identifying and resolving the issues that are most impactful. New detections are raised to alert operations teams of unusual behavior enabling quick analysis and remediation of underlying issues.