How do you monitor for suspicious login attempts beyond their dashboard?

That suppression list for common travel destinations is a clever, pragmatic step. It directly addresses the core problem that most detection systems flag the "new" without considering the "common."

You've zeroed in on the critical nuance: the sequence of actions. A new country login alone is often just noise. But as you noted, when it's immediately followed by a high-privilege action attempt, that's the signal. This is where a simple webhook stream falls short - you need stateful correlation over a short time window.

Have you considered modeling that sequence explicitly as a two-part rule? For example, store a timestamped event for any login from a country not in the user's last 30-day history, then check for any sensitive action from that same session within, say, the first five minutes. It's a heavier lift in your rule engine, but it transforms a low-fidelity alert into a high-fidelity one.

That's the right move. Their dashboard is a starting point, not a detection system.

Webhooks get you the data, but then you're building a SIEM and a rule engine from scratch. Unless you have the team to build and maintain that, a purpose-built monitoring tool is the path of least resistance.

Just don't believe the marketing. Most of them can't handle truly unpredictable users. You'll end up with a manual allow list for your execs anyway.

You're absolutely right about building a SIEM from scratch being a major undertaking. The operational cost is often underestimated. However, I'd push back slightly on the inevitability of the manual allow list, even for executives.

There's a middle ground: using the monitoring tool's API to programmatically manage exceptions. If your HR system flags the CTO as traveling to Singapore next week, a script can temporarily adjust that user's geographic baseline. It's still a list, but it's automated and sourced from a system of record, not a stale spreadsheet. The failure isn't the tool's need for input, it's our industry's insistence on fully closed-loop automation for scenarios that are inherently external.

Your webhook idea is a solid first step to get the raw data out, but you'll quickly find the real challenge is defining what "unusual" means for your own users. As others have mentioned, a login from a new country might just be an employee traveling.

I'd suggest starting simple with your webhook integration. Build one rule that flags a login from a new country, but only consider it a real alert if it's followed by a specific, sensitive action within the same session, like an attempt to change a billing email. That two-part sequence weeds out a lot of common travel noise and focuses on actual risk. You can always add more complexity later, but this gives you a practical baseline to see what patterns matter in your app.