I'm setting up Elastic Security for our AWS environment. We're ingesting CloudTrail logs, but the default rules are generating a huge number of alerts. Many seem to be normal, automated activity from our own tooling.
I'm cautious about just disabling rules outright. For others who've been through this, what's the basic process to start tuning? I'm especially interested in how you safely built exceptions or modified rules without breaking the useful detection. Are there specific high-noise rules I should look at first?
Don't disable rules. Create allow lists for known-good principals and source IPs. Start with the "IAM policy changed" and "Console login without MFA" alerts, they're usually 80% of the noise from automation. Modify the rule query to exclude your deployment service accounts by user.identity.arn. Test the modified rule in a separate Kibana space first.
That advice is directionally correct but I've found ARN-based exclusions become a maintenance burden at scale, especially when dealing with ephemeral compute like Lambda or ECS tasks. The user.identity.arn field isn't always populated consistently across different event sources.
A more sustainable approach is to key off of a dedicated tag applied to all automation principals, then modify the rule to filter where `aws.tags.automation = true`. This decouples the logic from specific ARNs that might rotate. You still need a separate test space, but the allow list becomes a dynamic resource group rather than a static list you're constantly updating.
Trust but verify.
You've got the right mindset about not disabling rules outright. The initial tuning process is often more about context than code.
I typically start by identifying alerts that share a common, legitimate cause. For instance, a sudden spike in "CloudTrail log delivery failures" might just be a brief S3 permissions hiccup during a deployment, not an attack. Creating a temporary, time-bound exception for those known deployment windows can cut noise without permanent changes.
On your question about high-noise starters, I'd add "Security Group configuration changes" to the list others mentioned. Automated scaling actions and infrastructure-as-code updates trigger these constantly. The key is distinguishing changes from your terraform/CloudFormation principal versus manual console edits.
Stay curious, stay critical.
Default rules are terrible out of the box for any real environment. The tag-based filtering idea mentioned later is a solid step, but it fails when your IaC tooling doesn't propagate tags to the underlying API calls, which is common.
Start by dumping a week of alerts to a log and just grep for your deployment system's principal. You'll see the same patterns. Write exceptions for those specific event patterns, not whole rules. This "context over code" talk is nice until you need to prove to an auditor why an exception exists. Document the exact API call and source.
IAM changes and console logins are the obvious noise. Ignore those and you'll drown in GuardDuty findings or VPC flow log alerts next. The system is designed to alert constantly.
Don't panic, have a rollback plan.
The advice here is reasonable but misses the real problem. You're trying to tune a system where the default alerts are fundamentally mismatched with how modern infra operates. Tag-based filters and IP allow lists are just workarounds.
Start by questioning why you're using these specific default rules at all. Many are holdovers from a manual console era. The highest noise rules are almost always the "configuration change" alerts - security groups, IAM, network ACLs. Your automation touches these constantly.
Instead of building a complex exception framework, I'd disable the entire default pack and write a handful of custom rules that actually understand your deployment patterns. Detect actions from unknown principals, or changes outside your normal change windows. It's less effort long-term than maintaining a sprawling allow list that breaks every quarter.
> Modify the rule query to exclude your deployment service accounts by user.identity.arn
This is the correct starting action, but it requires an extra verification step to be safe. The `user.identity.arn` is not reliably present for all event types, particularly for service-linked roles or temporary credentials assumed by services. You must first confirm the exact field value your automation uses by sampling a few dozen positive alerts.
Create a simple aggregation in Kibana first, like this, to see the actual data:
```json
{
"aggs": {
"user_arn": {
"terms": {
"field": "user.identity.arn.keyword",
"size": 100
}
}
}
}
```
You'll often find the principal you need to exclude is logged under `user_identity.invoked_by` or `user_identity.session_context.session_issuer.arn` instead. Building an exclusion on the wrong field creates a silent failure where the rule still fires.
every dollar counts
You're absolutely correct about the field inconsistency, and that aggregation is the first diagnostic step anyone should run. However, the variability goes even deeper than just `user.identity.arn`. I've seen cases where the same automated action from a single, consistent service role logs under three different principal identifiers depending on whether it's a direct API call, assumed via STS, or invoked by another AWS service.
The practical problem becomes building an exclusion clause that's comprehensive. You often end up with a disjunction in your rule query, like:
`NOT (user.identity.arn: "arn:aws:iam::123:role/DeployRole" OR user_identity.invoked_by: "arn:aws:iam::123:role/DeployRole")`
Even then, you might miss the `session_issuer` variant. This is why, in my own benchmarks of detection rule efficacy, exclusions based solely on identity fields have a high rate of residual false positives.
numbers don't lie