Hey everyone! 👋 I've been diving into Google Chronicle at my new job, and I'm really excited about its potential. Coming from a more traditional BI/Tableau background, the shift to a security data lake is fascinating but a bit overwhelming.
I just completed a project where I had to correlate phishing email alerts (coming from our email gateway) with endpoint data (from CrowdStrike) to see if any suspicious emails actually led to malicious activity on our machines. The goal was to move from just seeing alerts to understanding actual risk.
Could someone walk me through how they'd approach this? I managed to get it working, but I'm sure my process could be optimized. Here's what I did at a high level:
* First, I pulled the phishing alerts using a UDM search on the `entity.user.email` field from the email source.
* Then, I tried to find any matching endpoint events by looking for the same user principal in the CrowdStrike data, focusing on execution events or suspicious file writes.
* I used a JOIN-like approach with the `events()` function, correlating on `principal.user.userid` and a time window (like 1 hour after the email was received).
My main questions are:
* Is this the best way to join these datasets? I'm used to SQL JOINS, so the Chronicle way feels a bit different.
* How do you handle cases where the user field might be formatted differently between the two data sources (e.g., `john.doe@company.com` vs `COMPANYjohndoe`)?
* Any tips on visualizing this correlation in a dashboard for our SOC team? I'm thinking a simple table showing the email subject, recipient, and any related malicious endpoint events.
I'd love to see examples of how more experienced users structure their searches for this kind of cross-signal correlation. Also, any gotchas or performance tips when dealing with large volumes of endpoint data?
Nice approach! Using the `principal.user.userid` for correlation is spot on, that's usually the most reliable field.
One thing I've found helpful is to widen that initial time window beyond just one hour. Sometimes a user will open that phishing email a day or two later when they're clearing their inbox, and the malicious link or attachment gets triggered then. I usually start with a 24-72 hour window and then narrow it down if I get too much noise.
Also, for the endpoint side, don't just look for executions. Check for outbound network connections to suspicious domains right after the email timestamp - that's often a faster signal than a file write. Did you run into any issues with userid format mismatches between your email gateway and CrowdStrike? That always trips me up.
Automate the boring stuff.
Good correlation strategy, but I'd add a cost sanity check on that 72-hour window. You're about to multiply your search volume 72x, which can get expensive if you're not careful with your UDM query filters. Seen too many teams blow their monthly SIEM budget because someone ran a poorly constrained JOIN across two high-cardinality event sources.
Add a low-effort filter for `event.analysis.score` or `security_result.severity` on the phishing events first - no point burning cycles on low-confidence alerts. Also, Chronicle's pricing for that `events()` function isn't always linear, especially with massive time windows. It's weirdly similar to how AWS CloudTrail Lake charges per GB scanned.
What's your usual event-per-hour volume for phishing alerts? That's your multiplier right there.