Hi everyone, I've been lurking for a bit and finally decided to post. I'm helping my team evaluate our security stack, and we're currently running a POC of CrowdStrike Falcon. So far, it's been really eye-opening in terms of visibility.
Our main issue right now is with our CI/CD pipelines. We have a bunch of custom in-house DevOps tools (mostly scripts and small services for deployment and logging) that Falcon keeps flagging. I understand the principle of "better safe than sorry," but we're getting a bit overwhelmed with alerts. For example, our custom deployment orchestrator, which has been running for years, now triggers a "suspicious process invocation" detection almost daily.
I’m curious how others here have handled this. We're a small team and don't have a dedicated SOC, so we need to tune this effectively.
* What's the best practice for teaching Falcon that these internal tools are legitimate? Is it all about creating IOC exceptions, or is there a smarter way using the prevention policies?
* How do you balance suppressing noise without creating a blind spot? I'm worried we might whitelist something that could later be compromised.
* Has anyone built a specific workflow for vetting these alerts from their toolchain? I'd love to hear how you triage them.
We're on a tight budget, so I want to make sure we're using our time efficiently and not just constantly dismissing false positives. Any guidance from your experiences would be super helpful
That's a classic challenge when you start applying a mature EDR to a custom-built toolchain. Your question about blind spots is the right one to ask.
From what I've seen, the most effective approach is a two-step process, especially for something like your deployment orchestrator:
1. First, create a high-fidelity detection suppression rule based on the *hash* of your known-good script, not just the path or name. That's your initial stopgap.
2. Then, dedicate time to analyze *why* it's triggering the "suspicious process invocation" rule. Is it the child processes it spawns, the network connections, or the specific command-line arguments? Often, you can then adjust your prevention policy to be more specific, rather than a blanket exclusion. This way, if the tool is later tampered with (changed hash), the detection will fire again.
For a small team, building a simple review workflow is key. Maybe a weekly meeting where you examine the top false positives and decide if they need a permanent exclusion, or if the underlying tool behavior can be altered slightly to be less "noisy."
Have you looked at the exact detection logic in the Falcon console? Sometimes the description points to a specific module you can tune.
Keep it real
This is exactly what I was hoping to see someone mention. The idea of using a hash for the exception instead of just the path makes a lot of sense. It sounds like it directly addresses that fear of whitelisting something that gets compromised later.
When you say "analyze why it's triggering," what does that process actually look like for someone without a deep security background? Are you mostly looking at the specific alert details Falcon provides, or do you need other logging tools to trace the orchestrator's activity?
Use hash-based exclusions as a stopgap, but don't rely on them long-term. The hash changes every time you rebuild the script. You'll be chasing that hash update forever.
The real fix is policy tuning. In Falcon, go to Prevention Policies > your policy > click the detection rule that's firing. Look at the "Behavior" tab. It will tell you exactly what triggered it - usually a parent-child relationship or a specific command line pattern. For a deployment orchestrator, it's probably spawning shells or invoking curl/wget. Write a custom IOA exclusion based on the parent process path and the specific child process name, not the hash. That way if the orchestrator script gets replaced, it still blocks unknown child processes.
We built a small workflow: when a new false positive appears, we file a ticket to either add a narrow IOA exclusion or refactor the tool to avoid the triggering pattern. The refactor option is usually faster than updating hash lists. Example: our old deploy script used `curl | bash` which is a huge red flag. Changed it to download the script, verify checksum, then execute. No more alerts.
If you don't have a SOC, treat Falcon's alerts as a devops duty. Rotate a person each week to triage the pipeline alert queue. Most agents can be dismissed in 30 seconds once you know the pattern.
slow pipelines make me cranky
Totally agree on moving from hashes to policy tuning. That's the sustainable path.
What I'd add is that from an observability standpoint, this tuning process generates valuable data. When you look at the "Behavior" tab to build an IOA exclusion, log that context somewhere central, like a wiki or even a simple markdown file in your tools repo. Over time, you'll see patterns emerge in *which* behaviors your custom tools trigger. That list becomes a fantastic input for your DevOps team's future development - essentially a security/observability guide for writing internal tools that don't set off alarms.
Also, the "refactor the tool" option is key. It often improves the tool's reliability and makes its behavior easier to monitor in Grafana. For instance, replacing a `curl | bash` pattern with a download-and-verify flow gives you clear success/failure metrics to track.
Sleep is for the weak
This is super helpful to read, I'm in a similar boat with our internal tools. The hash vs. policy tuning discussion is clarifying a big worry I had.
A question from your last point about building a workflow: how do you track these exclusions once you make them? If you're tuning a prevention policy for a specific alert, do you just make a note in the Falcon console, or is there a better way to keep a record so future team members know why something is allowed? I'm worried we'll forget the context in six months.
Oh man, the "suspicious process invocation" on the old deployment orchestrator brings back memories. I had a similar tool that would trigger alerts every time it spun up a `bash` shell to run remote commands. The key for us was following the breadcrumbs in the alert details to see *exactly* which child process was the culprit.
For your question on workflow, we ended up using a simple GitHub Issue template in our internal "security-ops" repo. Every time we had to tune a policy or create an exclusion, we'd file an issue. The template forced us to paste the alert details, the hash or process path we excluded, and most importantly, the "business reason" - something like "Allowed orchestrator to spawn /usr/bin/ssh because it's used for prod deploys."
That issue log became our audit trail. When a new team member wonders why something is allowed, we point them there. It's not fancy, but it beats scribbling notes in a Slack thread that vanishes in two weeks. How are you tracking changes right now?
it worked on my machine
Great points already. The move from hash-based exclusions to policy tuning is key, especially for CI/CD tools that get rebuilt frequently.
For your question about tracking, we do something similar to the GitHub issue approach, but we tie it directly to our tool's version control. When we create an IOA exclusion in Falcon, we also add a comment in the Ansible playbook or Jenkinsfile that uses the tool. Something like:
```
# Note: Excludes cs-falcon detection on child process 'scp'
# See Falcon policy 'CI-CD-Allowlist' - IOA exclusion ID XXXX
```
That way, anyone updating the pipeline sees the security context right there. It's a bit more maintenance, but it prevents that "why is this allowed?" confusion later on.
Also, for the orchestrator specifically, look at the command-line arguments it passes. Sometimes you can refine the exclusion to only allow the exact argument pattern you use, like `/usr/bin/ssh -q -p 22 myhost`, rather than allowing all ssh invocations from that parent. That tightens the blind spot considerably.