Machine learning anomalies in context with custom URLs in Kibana

Machine learning within the Elastic Stack offers a powerful method for anomaly detection in extensive datasets, though understanding the root cause of anomalies often requires further analysis beyond initial detection. By utilizing custom URLs, users can extend their investigative capabilities in Kibana by linking anomaly records to additional dashboards or external sites, providing essential context for thorough analysis. This approach is demonstrated with a security use case, using Auditbeat data to detect unusual user activity and processes, and is facilitated by the machine learning job wizard in Kibana. Custom URLs allow seamless navigation between Kibana views and external tools such as Grafana, enabling users to filter data and time ranges based on specific anomaly records, thus enhancing the efficiency and depth of anomaly investigation workflows. Elastic Stack's capabilities, including prebuilt jobs and the Security solution, further support this comprehensive analysis approach.