Analysts had no way to tune alert grouping themselves. Every noisy rule required an engineering change rather than a self-serve setting.

I designed a field-based configuration surface where analysts define suppression logic across up to five field values (source IP, process, user), see exactly what will be grouped, and adjust it without engineering involvement.

Earlier grouping prototypes hid underlying events entirely, risking a real signal being suppressed along with the noise.

The alert detail view surfaces the suppression count, the representative event, and a one-click drill-down to every underlying event. Nothing is deleted, only consolidated.

ML detection rules identify anomalous patterns across thousands of events, where suppression is far less intuitive than for threshold rules, and ML alerts don't carry the same underlying event fields.

Rather than presenting ML and standard rules as if they worked identically, I designed the suppression UI to communicate ML-specific constraints clearly: suppression on anomaly-result fields only, a maximum of three fields, and the anomaly score always visible.

When hundreds of alerts collapse into a single row, analysts need to know at a glance that a row represents a grouped set. Otherwise the queue itself becomes misleading.

I integrated suppression indicators directly into the main Alerts view, showing the suppressed count on every representative alert row so grouped and individual alerts are never confused.

Reducing alert volume is worthless if it breaks the investigation workflow analysts rely on to escalate or dismiss with confidence.

One-click expansion from any suppressed alert to the full event list, keeping the complete investigation pathway (flyout, Timeline, drill-down) intact even when hundreds of alerts collapse into one row.