Check out this workflow for triaging high-risk session alerts.

I've been working with a few teams lately who are rolling out Exabeam for UEBA, and a common pain point has surfaced: managing the volume of "high-risk session" alerts without getting overwhelmed. It's easy to end up with a noisy dashboard that doesn't effectively prioritize analyst time.

One workflow that's shown promise involves a more staged triage approach, leaning heavily on Looker for visualization. The core idea is to move from a flat list of alerts to a layered review process.

First, we built a Looker explore that groups these session alerts by user and ranks them by a composite risk score (pulling in factors like anomaly count, critical asset access, and sequence rarity). This gives a heatmap view of which users need attention first. The key step we added was a second, drill-down dashboard focused on the *session timeline* for a selected user. This view juxtaposes the flagged session against their normal baseline—logon times, typical applications, peer group activity—all pulled via Exabeam's APIs into a modeled dataset.

This separation allows a senior analyst to quickly assign the highest-risk user clusters for deep dive, while junior analysts can investigate the contextual session data without needing to build the timeline narrative manually in the main Exabeam UI. It's less about replacing Exabeam's interface and more about creating a clearer, repeatable investigation path that reduces cognitive load. Has anyone else tried structuring their triage process in a similar way? I'm curious about how you're handling alert fatigue.

- aw