--- sourceDocument: Australia IT Operations Management sourceDocumentLink: https://www.servicenow.com/docs/r/australia/it-operations-management Release : - australia ft:locale : - en-US ft:publication_title : - Australia IT Operations Management ft:clusterId : - itom bundleId : - itom workflow : - Technology --- # How Health Log Analytics generates alerts # How Health Log Analytics generates alerts {#ariaid-title1} Release version: Australia Updated March 12, 2026 ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 3 minutes to read Summarize ![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete. ## Summary of How Health Log Analytics generates alerts Health Log Analytics (HLA) utilizes AI-driven pattern recognition to analyze your log data, identifying anomalies that deviate from established baselines. When such anomalous behaviors are detected, HLA sends events to the ServiceNow Event Management application, enabling operators to proactively address potential IT issues before they impact users. Show full answer Show less ## Key Features * **Log anomaly detection:** HLA tracks baseline behaviors, such as average event rates for specific log patterns, and detects deviations like unexpected spikes to generate alerts. * **Alert metrics:** Multiple metrics tied to unique service instance and component sources are monitored for anomalies. Operators can provide feedback on alerts, marking them as significant to raise priority or muting them to reduce noise. Muted alerts stop generating notifications but can be restored as needed. * **Lexical keywords:** The system scans logs for specific words (e.g., "crashed," "failed") that indicate issues. Alerts are triggered when occurrences exceed a set threshold based on normal patterns. Customers can view, manage, and configure these keywords globally or per source type. * **Correlations:** Log correlators detect related alerts by identifying common keys or values in log data, such as repeated device IDs appearing in multiple warnings, helping to pinpoint related incidents. * **Advanced alert filtering:** Customers can create and manage filters that specify conditions to drop insignificant alerts, reducing noise and focusing on critical issues. Filters can be tested and updated dynamically. * **Custom alert rules:** Operators can define alert rules for specific metrics with custom thresholds and alert properties, tailoring alert generation to unique operational needs. ## Practical Impact for ServiceNow Customers By leveraging these capabilities, ServiceNow customers can expect improved visibility into log-based anomalies and more precise alerting. This proactive detection and alert customization reduce alert noise, enhance prioritization, and facilitate timely resolution of emerging IT issues, thereby minimizing user impact and optimizing operational efficiency. Health Log Analytics identifies patterns in your log data and learns pattern behavior. When HLA's AI engine detects anomalous behavior, it sends an event to the ServiceNow Event Management application. As an operator, you can use these predictive alerts to handle emerging IT issues before they impact users. ## Log anomaly detection {#hla-op-anomalies-detecting__section_vlx_gtf_qmb} Anomalies are abnormal or unexpected behavior that occur when activities deviate from established baselines. There are many kinds of anomalies. In this example, the system tracks the baseline rate (the average number of events per minute) for a specific log pattern. When this typically inactive log generates a spike in events, the system detects the deviation from the baseline and generates an alert. Figure 1. Anomalous behavior Health Log Analytics uses various methods to detect anomalies and generate alerts. ## Alert metrics {#hla-op-anomalies-detecting__section_mh2_1q2_ymb} Health Log Analytics monitors multiple metrics in the log stream to detect anomalous behavior. Each metric is associated with a unique source: the combination of service instance and component. When the system identifies an anomalous pattern for a metric, it generates an alert. As an operator, you can provide feedback about the generated alerts. Your feedback "teaches" Health Log Analytics that a specific alert is significant or irrelevant to you. The application then either raises the priority of the alert metric or mutes it to reduce noise. * A significant alert is more likely to be included in a Log Analytics group when the associated metric behaves anomalously. For more information, see [Mark an alert as significant in Health Log Analytics](https://www.servicenow.com/docs/4MkdFtFauJYVxLOCD~ESDA "Make an alert more likely to be included in a Log Analytics group when the associated metric behaves anomalously by labeling the alert as meaningful.") * Mute an alert for a specified source to eliminate distracting new alerts for unimportant issues. When a metric is muted, Health Log Analytics removes the current alert and any other alerts based on that metric from the feed. It also stops generating new alerts from that metric. For more information, see [Mute an unimportant alert in Health Log Analytics](https://www.servicenow.com/docs/sVTB2z~4O~zt3NH0UJEANA "Eliminate distracting new alerts for insignificant issues by muting them."). * When the situation changes, you can return a significant metric to its default significance. You can also reactivate a muted metric to cause the system to start generating alerts again. For more information, see [Restore normal importance to an alert metric in Health Log Analytics](https://www.servicenow.com/docs/b3rcuUWLg3hotsNKsAxprg "Return normal significance to the metric involved in generating a Log Analytics alert. Use this option if you no longer want an alert to be treated specially that was muted, marked as significant, or made less sensitive to similar anomalies."). {#hla-op-anomalies-detecting__ul_a1n_stg_f4b} ## Lexical keywords {#hla-op-anomalies-detecting__section_gdn_bbn_3nb} Health Log Analytics scans your logs for words that can indicate important issues. Lexical keywords such as "crashed" or "failed" signal a condition that can merit attention. The system sets a threshold for each lexical keyword that is based on what it considers the normal occurrence pattern and frequency of that keyword in your logs. When it scans your logs, it finds all occurrences of the keyword. If the number exceeds the threshold, it generates an alert. For more information, see [View the lexical keywords that generate alerts in Health Log Analytics](https://www.servicenow.com/docs/P0ta3UKN4uY4dHMibpwS5A "View the list of lexical keywords that can indicate important issues in log entries."). For information about managing global keywords, see [Add, edit, or delete lexical keywords in Health Log Analytics](https://www.servicenow.com/docs/aCyFzOE6pviK8p6VfnCFMw "Manage the keywords that Health Log Analytics looks for in your log data."). To create or delete keywords for a specific source type, see [Configure source type capabilities in Health Log Analytics](https://www.servicenow.com/docs/Tt_eXKybYE7~W7T2EMmV0g "Health Log Analytics extracts source types automatically in the mapping process. You can add timestamp formats and specify, delete, or exclude keywords for individual source types."). ## Correlations {#hla-op-anomalies-detecting__section_jxp_dq2_ymb} Log correlators are keys or values in log data that detect correlations between alerts. For example, a log correlator could detect when the ID of a particular network device occurs simultaneously in multiple warnings across different service instances. For more information, see [Log correlators for identifying related alerts](https://www.servicenow.com/docs/4cgoFJ1d9WVr7r7Qy34mkg "In Health Log Analytics, log correlators are keys or values in log data that detect correlations between alerts. These correlations help you determine whether an alert is part of a larger issue."). ## Advanced alert filtering {#hla-op-anomalies-detecting__section_drm_y1n_3nb} Add advanced log alert filters to scan alerts for conditions that you specify. The filters reduce noise by dropping alerts that do not indicate a significant issue. While developing a filter, you can test, update, publish, or activate the filter at any time. For more information, see [Create advanced log alert filters](https://www.servicenow.com/docs/1LAy8ahH4mxt2jJEdJ3IfQ "Add advanced log alert filters to scan alerts for conditions that you specify. The filters reduce noise by dropping alerts that do not indicate a significant issue. While developing a filter, you can test, update, publish, or activate the filter at any time."). ## Custom alert rules {#hla-op-anomalies-detecting__section_vxf_lj2_knb} Define a Log Analytics alert rule when you encounter log data that should generate an alert. The alert rule generates an alert for a specified metric with a threshold that you specify and sets the properties of the generated alert. For more information, see [Alert rules in Health Log Analytics](https://www.servicenow.com/docs/anOZdlsK0AFmDFDAmoU0OA "Health Log Analytics (HLA) detects anomalies automatically by learning from your log data. However, some log types require a custom alert rule to generate alerts reliably.").