u/Past-Ad6606

we pushed some policy changes to tighten Azure security and alert volume jumped from ~20 high priority alerts a day to 200+.

a lot of it is low signal. things like log access, policy mismatches on short-lived or already removed resources, repeated findings that don’t change state.

tried the usual tuning. adjusted thresholds, added exclusions, set up suppression rules. some of that helped briefly, then broke when underlying resource IDs or context changed.
compliance is fine with the volume, but it’s making it harder to spot anything real.

we’re running Defender for Cloud + Sentinel across multiple tenants with Entra and a mix of VMs and app services.

what’s actually working for you to reduce Azure security alert noise without losing real signal?

we’ve got a strict policy that all AWS resources go through Terraform. that broke this week.

a junior dev needed temporary storage for a data export and created an S3 bucket directly in the console. uploaded ~500GB of customer data from a prod RDS replica. bucket ended up public.

we found it when GuardDuty flagged activity on a bucket we didn’t recognize. public access was open for several hours before we caught it. we’ve locked it down now, but there’s no clear way to know who accessed the data during that window.

on top of that, an IAM role from prod with broad read permissions was attached for the export script. so now we’re also dealing with potential exposure through that path.

we’re digging through CloudTrail and access logs to understand scope, but it’s messy.

this wasn’t a tooling gap, it was someone bypassing IaC under time pressure.

for those dealing with AWS security at scale, what actually works to prevent this? not policies on paper, but controls that stop or catch console-created resources fast.

Been rolling out agentless scanning across our multi-cloud setup and honestly the gaps are starting to show now that were past the initial POC phase. Coverage is solid on public cloud but the blind spots for private infrastructure and hybrid workloads are real. Were basically seeing limited visibility on Windows boxes and the reporting feels incomplete when you actually need to track something specific.

False positives are killing us too. The noise makes it hard to prioritize what actually matters. We tried tightening thresholds but then real issues slip through. Scaling it across hundreds of workloads without impacting performance is harder than expected.

Has anyone been through this with agentless tools at scale. about what coverage gaps you ran into that werent obvious in demos, how you handle the false positive problem in production, and whether you actually found it scales well or if you hit a wall somewhere. What did you end up doing differently after deployment started.

also if anyone hit limits with agentless and had to rethink their setup

Been rolling out agentless scanning across our multi-cloud setup and honestly the gaps are starting to show now that were past the initial POC phase. Coverage is solid on public cloud but the blind spots for private infrastructure and hybrid workloads are real. Were basically seeing limited visibility on Windows boxes and the reporting feels incomplete when you actually need to track something specific.

False positives are killing us too. The noise makes it hard to prioritize what actually matters. We tried tightening thresholds but then real issues slip through. Scaling it across hundreds of workloads without impacting performance is harder than expected.

Has anyone been through this with agentless tools at scale. about what coverage gaps you ran into that werent obvious in demos, how you handle the false positive problem in production, and whether you actually found it scales well or if you hit a wall somewhere. What did you end up doing differently after deployment started.

also if anyone hit limits with agentless and had to rethink their setup