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Manage detection rules at scale

Teams that run large sets of detection rules (prebuilt, custom, or both) have recurring operational work. Rules need to be audited for health, errors need to be surfaced quickly, coverage needs to be tracked against a framework, and status often needs to flow back to an external tracker.

Use workflows to automate these rule-operations tasks. Workflows can query detection engine APIs on a schedule, post summaries to a chat channel, index results for dashboarding, or open a ticket when something is wrong, all using existing workflow building blocks.

The following patterns combine scheduled triggers with Kibana request actions to drive rule-operations work:

  • Audit rule health on a schedule. A scheduled workflow queries the detection engine API for rule status, filters for rules in an error or disabled state, and publishes a daily summary.
  • Surface rule errors. Use if steps to branch on rule status and send a targeted notification when the failing rule is business-critical.
  • Report on coverage. Use foreach steps to iterate over rules, group by tag or framework mapping, and index the result to an Elasticsearch index for dashboard visualization.
  • Sync rule status to external systems. Use HTTP actions to mirror rule status into an external tracker, or post to Slack or PagerDuty when a rule crosses a threshold.

A daily rule-health report workflow has the shape:

  1. Scheduled trigger fires every morning.
  2. Kibana request step calls the detection engine API to list rules and their status.
  3. Foreach step iterates over the returned rules.
  4. If step identifies rules in an error state.
  5. Elasticsearch step indexes the summary to a rules-health index, or a connector step posts the summary to Slack.