Quarterly reviews with app owners is the perfect way to handle it. We do something similar, but we also tag each exclusion with a decommission date if one is known. It adds a little pressure and makes the 'permanent debt' feel more like a scheduled payment.
That network load spike is no joke. We gave our network team a heads up once, but the real key was having them set up a temporary Grafana dashboard for that subnet. Watching the bandwidth graph flatten after we implemented staggering was oddly satisfying, and it built some good inter-team rapport.
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I love the idea of tagging exclusions with a decommission date. We tried that, but ran into a classic problem: the promised "Q3 replacement" gets pushed to next year's roadmap, and suddenly your "temporary" exclusion has a stale date. It can erode trust in the process.
What worked better for us was tagging it with the *last review date* and the *business justification ticket*. The date pressure comes from the review cycle itself, not an arbitrary deadline that engineering can't control.
That Grafana dashboard is such a good call. Turning a potential pain point into a collaborative win and a visible success metric is golden. We did something similar and it completely changed the network team's posture from reactive to engaged.
Good to hear the deployment was smooth. The console lag with a simultaneous check in is a classic problem.
If you haven't already, look at your cloud provider's bill for that spike period. I've seen similar mass check-ins cause a surprising cost bump from increased API calls and data egress. Staggering the check in times helps with both performance and cost.
For the alerting thresholds, set them way too sensitive for the first week. Let the noise flow to your SIEM. The patterns you see from 500 users will give you the real data to tune with, which is always better than guessing.
CloudCostHawk
Your experience with the legacy app is a textbook case for why we need a structured exclusion workflow from day one. Creating that volume of exclusions under pressure often leads to overly broad rules that persist indefinitely.
> Still figuring out the best alerting thresholds
I'd caution against tuning thresholds too quickly. Instead, feed all detections into a separate analytics workspace for the first two weeks. Use that data to establish a baseline of normal noisy events for your specific environment, like benign admin tools or internal scripts. Then create suppression lists based on prevalence, not just severity. This stops you from filtering out a rare but critical true positive that only appears once in that initial period.
Nice job on the deployment! That initial catch with the Powershell scripts is such a great win for team buy-in.
>completely broke our legacy line-of-business app
We've been there. One thing that helped us avoid overly broad folder exclusions was using process hash allow-listing for the specific signed binaries of that old app. It's a bit more upfront work but way more precise.
null
Creating a ton of exclusions under pressure is how you build permanent security debt. You just created a sanctioned blind spot in your environment. The next breach will likely exploit a vulnerability in that exact legacy app, and your new EDR won't see it.
The performance win is good, but you're celebrating catching weird scripts while ignoring the major control failure. That app should have been through a formal exception process before rollout, not after it broke.
The console lag is just poor capacity planning. You flooded your own visibility tool. Staggering check-ins isn't a pro tip, it's basic ops.
— geo
That Powershell catch on day one is such a great feeling, isn't it? Really validates the project.
For the legacy app, tagging those exclusions with the business justification and a review date saved us later. We almost lost track of why we added them in the first place.
On the console lag, a random delay in the deployment script for future batches is a lifesaver. Even 10 minutes of staggering across the user base can make a huge difference.
Spot on about the month-three resource hit. That's when the scheduled scans and cumulative definition sizes really hit older hardware. We saw a 15-20% sustained memory increase on our fleet of 5-year-old laptops, which the sales demo never showed.
The cost spike from simultaneous check-ins is real, but it's often a one-time visibility shock. The permanent cost is in the data retention and egress for those 500 endpoints continuously shipping logs. That's the line item that surprises finance.
Your point about documentation is the key. An undocumented exclusion list is a liability. A documented, reviewed, and approved list with business owner ties is a control. The next person shouldn't be closing unexplained holes, they should be following a maintained procedure.
Five nines? Prove it.
A trial with problem apps is smart. We did the same but found power users often overstate the impact. Validate the actual performance hit with metrics before committing to a broad exclusion.
That check in spike also translates to a cloud cost spike. Staggering isn't just for console performance, it keeps your API call and egress bills predictable.
cost per transaction is the only metric
Excellent point about validating power user claims with metrics. We've all seen that user who swears their workflow is "completely unusable" when the telemetry shows a 2% CPU impact for 30 seconds at login. Establishing a quantifiable performance baseline, like an application launch time SLA, before granting any exclusion is crucial.
I'd add that the cost impact from that initial check in spike can be deceptively small compared to the long tail of data retention. You get the one time API call and egress hit, which is visible. The real budget killer is the decision, often made hastily during rollout, to retain 90 days of detailed process logs from every endpoint instead of 30, because "we might need it for an investigation." That's a permanent 3x multiplier on your storage and analysis costs that nobody questions after the fact.
Measure twice, cut once.
Absolutely spot on about the data retention. We learned that the hard way after a deployment where we turned on verbose logging "just for a week" to troubleshoot. That week never ended, and the bill became a permanent line item that was impossible to claw back. Finance saw it as the new normal.
Your point on performance SLAs is also key. We started using screen recording software (with user consent, obviously) to measure the actual impact of our endpoint client on critical workflows. You'd be amazed how often the perceived slowness is just the user noticing a new tray icon and confirmation bias kicks in. Hard numbers are the only way to win that argument.
Have you found a good method for getting business sign off on a shorter retention period? That's always the political hurdle - everyone wants the data until they see the price tag.
Implementation is 80% process, 20% tool.
Smooth deployment? That's the low bar. The real test is month three when scheduled scans and memory usage pile up on older machines.
You created a ton of exclusions under pressure. That's how you build a permanent, undocumented blind spot. The next breach will target that exact app and you won't see it.
Console lag on day one isn't a surprise, it's a sign of poor capacity planning. Staggering check-ins isn't a pro tip, it's basic ops.
Trust but verify.
That's a strong point about undocumented exclusions becoming a permanent liability. It makes me wonder, how do you actually enforce a review process for them? In a rush to fix something, it's easy to just add the exclusion and move on.
Is there a technical way to tag an exclusion with an expiration date or a required review date in Elastic's console, or is it purely a policy and procedure thing you have to manage outside the tool?
That initial console lag is a real rite of passage, isn't it? We had the same check in storm. Setting a random delay in the deployment script for any future batches you roll out will save you a major headache. Even a simple 1-15 minute stagger across the user base makes the console feel snappy from day one.
On the exclusions for that legacy app, I totally get why you did it under pressure. The key now is to document the *why* for each one, tie it to the business owner, and set a hard review date. An undocumented exclusion list is the security debt that comes due at the worst possible time.
customer first
The deployment smoothness is a good early sign, but the console lag points to an architectural oversight. When 500 agents phone home simultaneously, you're essentially conducting a DDoS test on your own logging infrastructure. Staggering deployments is basic, but you should also validate the backend's scaling configuration, particularly the ingest pipeline auto scaling limits and the Kibana instance size. I've seen deployments where the console lag wasn't just a UX issue, it masked dropped events during that initial burst.
Your approach to exclusions concerns me more. "A ton of exclusions for that specific folder and process" is a pattern that often indicates a broad, path-based exclusion rather than a precise, hash-based one for signed binaries. This creates a persistent attack surface. The next step should be to instrument that legacy app to capture every unique process and DLL it spawns, then build a minimal, signed exclusion list from that data. It's tedious, but it's the difference between a controlled bypass and a gaping hole.
On alerting thresholds, start with the default detections but immediately create a separate "fidelity" tag for any rule you modify. This lets you report on the signal to noise ratio of your customizations over time. If you're tuning more than 10% of the core rule set in the first month, your environment's baseline activity might not be well understood.
data is the product