Hey folks! 👋 Working with GravityZone's event dashboard, I keep hitting the same workflow snag: items flagged as "suspicious" that don't get escalated to "malicious." The system is great at catching the obvious stuff, but the middle-ground alerts can pile up.
What's your process for triaging these? I'm curious about:
* **Review triggers:** Do you act on every "suspicious" alert, or only those from certain sources (like a specific detection type or machine group)?
* **Investigation steps:** What's your go-to checklist? I usually:
* Check the file's prevalence in our environment.
* Look at the originating process and user.
* Pull it from the endpoint for a manual upload to VirusTotal.
* Review the application's behavior logs if they're available.
* **Automation:** Have you set up any custom GravityZone rules or integrations (like with your SIEM or IT ticketing system) to handle these cases semi-automatically?
We're on a lean IT/security team, so I'm trying to find the balance between being thorough and not getting swamped by alerts that are likely low-risk. I'd love to hear how others are structuring this—especially if you've tied it into an agile incident response workflow.
Good question! We've wrestled with the same alert fatigue. Our review trigger is risk-based: we prioritize alerts from high-value assets (finance, build servers) or for certain detection types we've seen cause real problems before. Everything else gets batched for a weekly review.
Your checklist is solid. I'd add checking the file's hash against our own internal "known good" repository first. If it's a developer tool or an obscure installer that's been approved before, we can close it out fast.
For automation, we set up a GravityZone custom rule to auto-close specific, repeated "suspicious" alerts from our dev machines if the file's prevalence is above a certain threshold. It's a bit of a calculated risk, but it cut down our noise by maybe 40%.
Pipeline Pilot
Your risk-based prioritization is the correct foundation. Extending that logic into automation, as you've done with custom rules, is the logical next step. However, I'd caution that the "calculated risk" of auto-closing based on prevalence requires a very stable software base image. A high prevalence threshold could blind you to a lateral movement payload that's rapidly propagating from that initial developer machine.
Instead of, or perhaps in addition to, auto-closing, consider using those same custom rules to trigger a secondary data pipeline. We route auto-close candidates to a low-priority queue that performs an automated VT rescan and checks the hash against a timeline of our software deployment logs. This adds a layer of historical context. If a file flagged as "common on dev machines" suddenly appears on a device that has never run that toolchain, it gets bumped back into the review queue. It's a passive verification step that maintains velocity without sacrificing depth.
—BJ
You've hit on the core operational dilemma. Your checklist is a solid manual process, but it's fundamentally reactive and scales poorly. The goal should be to make the system context-aware so the alert carries more meaning before it hits your dashboard.
Automation shouldn't just be about closing tickets. It should be about enriching the event with data that informs your priority. Instead of just prevalence, build a scoring system in your SIEM. Ingest the GravityZone alert and weigh it against: asset criticality (from your CMDB), user role (from AD/Azure AD), network zone, and recent software deployments. This gives you a calculated risk score that dictates whether it's a P0 review or a batched weekly item.
For the investigation steps themselves, manual VirusTotal checks are a time sink. Use their API via a small serverless function or a playbook in your SOAR platform. The moment a "suspicious" alert fires on a high-value server, that function should pull the hash, query VT, and append the results back to the alert. Now your checklist starts at step two.
The real trick is that "known good" repo user122 mentioned. You need to operationalize it. We built a simple internal service where any file hash cleared by the team gets logged with an expiration date and a reason. A GravityZone custom rule can then query this service via API and auto-close only if there's a valid, unexpired entry. This moves you from guessing based on prevalence to acting on a documented, approved baseline.
infrastructure is code
You're right about moving the context enrichment upstream, but that SIEM integration and scoring system you describe is a significant infrastructure project. It assumes you have a mature CMDB, clean user role data, and the engineering bandwidth to build and maintain those ingest pipelines.
My counterpoint is that you can get 80% of the benefit with a simpler, more tactical approach focused on cost to benefit. Instead of a full SIEM scoring engine, we built a simple lookup table in GravityZone itself using custom tags. Assets are tagged with a criticality level (high, medium, low) based on their function. Our automation rules then use that tag as the primary filter. A "suspicious" alert on a "high" criticality server triggers an immediate, automated VirusTotal API call and pages us. The same alert on a "low" asset gets grouped into a daily digest.
This isn't as nuanced as weighing network zone and recent deployments, but it cost us a week to implement, not six months. The operational leap was moving from reviewing every alert to having the system pre sort them into "urgent" and "batch" based on a single, reliable data point.
Spreadsheets or it didn't happen.