--- sourceDocument: Zurich IT Operations Management sourceDocumentLink: https://www.servicenow.com/docs/r/zurich/it-operations-management Release : - zurich ft:locale : - en-US ft:publication_title : - Zurich 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: Zurich * * Updated July 31, 2025 * * ![](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) uses AI-driven pattern recognition to identify anomalies in log data and generate alerts. These alerts are sent to the ServiceNow Event Management application, helping operators proactively address emerging IT issues before they affect users. Show full answer Show less ## Alert Generation Mechanisms * **Log Anomaly Detection:** HLA monitors log activity baselines, such as event rates per minute, and detects deviations indicating abnormal behavior. For example, an unusual spike in events from a normally quiet log pattern triggers an alert. * **Alert Metrics:** Metrics are tracked per unique source (service instance and component). When a metric behaves anomalously, HLA generates an alert. Operators can provide feedback to mark alerts as significant or mute irrelevant alerts, which helps tune alert noise and priority. * **Lexical Keywords:** HLA scans logs for critical keywords like "crashed" or "failed." If occurrences exceed a learned threshold, an alert is generated. Keywords can be globally managed or customized per source type. * **Correlations:** Log correlators identify related alerts by detecting shared keys or values across different alerts, such as a common device ID appearing simultaneously in multiple warnings. * **Advanced Alert Filtering:** Operators can create filters to specify conditions for alerts, reducing noise by excluding non-significant alerts. Filters can be tested and updated dynamically. * **Custom Alert Rules:** Users can define alert rules for specific metrics and thresholds, setting alert properties tailored to their environment and needs. ## Practical Benefits for ServiceNow Customers By leveraging HLA's alert generation capabilities, ServiceNow customers can: * Detect IT issues early through AI-powered anomaly detection and keyword monitoring, improving incident response times. * Customize alert relevance by marking significant alerts or muting noise, enhancing operational efficiency. * Identify related issues quickly through log correlators, aiding in root cause analysis. * Use advanced filtering and custom rules to tailor alert behavior to their unique environment. * Integrate alerting seamlessly with Event Management for streamlined issue tracking and resolution. 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/Q2dHeKw~Ml~GTNo4R86S8A "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/EPD_UfmW6CGRQKRleSCVNQ "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/u1LS4Evzc6wbCEjHbEC3ow "If you no longer want a muted alert or an alert that was marked as significant to be treated specially, you can restore normal importance to the metric involved in generating it."). {#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/mNy22ZoaVqUrIMT9BHLWhw "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/S~ivLvqB5HRcdOPcdRs39w "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/uAODbYjv64LQVQYjySFT5A "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/QUQ6ExcoAwCfdyY52dZdhg "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/Ob8gzzrDdYfu46_HLELKHQ "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/vAvhvaLLrb_ONXlBCDwRdg "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.").