I've been quietly reading posts here about Exabeam workflows, and I finally have something to share. We've been using Exabeam for user behavior analytics for a few months, and our SOC wanted a quicker way to see high-risk user alerts without having to be in the UI all the time.
I put together a Python script that pulls the latest high-risk users from the Exabeam API and sends a formatted summary to a dedicated Slack channel. It runs on a schedule via a simple cron job. The main goal was to get the username, risk score, and the top contributing rule for each user flagged in the last 24 hours.
It uses the `exabeam` Python client library for the authentication and data pulling, and the Slack SDK for posting. The trickiest part was figuring out the exact API endpoint for the user risk data and parsing the nested JSON for the rule information. I also added a filter to only show users with a risk score above 80 to avoid noise.
It's been running for a couple of weeks now and the team finds it helpful. I'm wondering if others have built similar integrations, and if there are better ways to structure the data or perhaps use webhooks instead of polling the API on a schedule.
Polling the API every few minutes seems like a reliable way to hit rate limits or unexpected charges when your user base grows. Have you actually looked at the API costs or the load you're generating, or are you just hoping it stays under the radar?
The webhook suggestion is a good one, but I'd push further. Why is the data going to Slack at all? You're just recreating a poor version of the Exabeam UI in a channel that will get muted in a month. If the SOC needs real-time alerts, the tool should email them or page them. If they need a dashboard, build a proper one that doesn't get lost in the chat scroll.
And filtering at 80? That's an arbitrary threshold that will either flood you with false positives or make you miss the clever, low-and-slow attacks that accumulate risk just below your line. You've basically built a cron job that gives a false sense of vigilance.
monoliths are not evil
Oh, I agree on the arbitrary threshold. Picking 80 because it feels high is classic "let's just ship it" logic. But the flood of false positives is the least of your problems.
The bigger issue is you're now training your SOC to ignore a stream of semi-relevant data in Slack. They'll miss the one real alert buried in the noise, and when asked, they'll say "Oh, that channel's a mess, we mute it."
Email or paging for critical alerts, a real dashboard for trends. Don't mix the two into a chat graveyard.
Trust but verify.
Thanks for sharing the specifics of your script, especially mentioning the risk score filter and the libraries you used. I'm glad your team's finding it helpful.
You're right to wonder about webhooks vs polling. The schedule-based approach makes sense for a daily summary, but if the goal is immediate alerts for truly critical risks, you might want to look into setting up an Exabeam alert rule to trigger an outgoing webhook. That way you're only pinging Slack when a specific, high-fidelity threshold is crossed, which can help with the rate limit concern.
On the data structure, have you considered including a direct link back to the user's timeline in the Exabeam UI? That small addition in the Slack message could save the SOC a few clicks when they need to investigate.
Reviews build trust.