Totally agree on making it a markdown file in the repo. We do something similar, but we generate it automatically from our Terraform modules that manage the rules.
We have a small script that runs during our CI/CD pipeline. It parses the `main.tf` for any disabled Elastic rule IDs, checks for a linked `control_id` variable in the module, and auto-updates the compliance map markdown. It's not perfect, but it kills the manual update step.
Did your team consider any automation to enforce that 15% tax, or is it all manual PR reviews?
Infrastructure as code is the only way
That "cost savings are eaten by engineering hours" line is the whole story right there.
You aren't alone on the latency. It's the data stream config, not the agent. If you're indexing alerts on the default hot-warm-cold lifecycle, they're in line behind log ingestion. You need a separate, dedicated stream with its own ILM policy, which of course isn't in any sane default template.
The tuning fatigue is real. Those default rules are written for a generic enterprise that doesn't exist. Every shop has its own "benign" tools that look malicious to an algorithm.
your mileage will vary
Yep. The ILM policy trick is critical.
I keep a template for the security data stream. It basically sets the rollover threshold way lower and keeps the data hot longer. Saves a lot of head-scratching when alerts are delayed.
YAML all the things.
The dedicated ILM policy is a non-negotiable baseline, but it's only half the battle for latency. The other half is ensuring your shard count is appropriate for that separate stream. If you're using the default for a low-throughput alert index, you can end up with a single shard that becomes a processing bottleneck, especially during ingest spikes from a widespread detection.
You also need to check the `write.wait_for_active_shards` setting on that index template. It's sometimes overlooked.
infrastructure is code
Shard count makes sense. Are we talking about scaling the count based on total expected alerts per day, or is there a rule of thumb for the initial setting?
Also, good call on `write.wait_for_active_shards`. That default for safety can really back up the queue during a major event, right? We're new to managing our own streams.
Scaling by alerts per day is a good start, but I find IOPS and document size are more critical. A rule of thumb is to aim for shards between 10GB and 50GB. For a security alert stream, you might start with 2-3 primary shards and monitor the `_size` and `_doc_count`.
The `wait_for_active_shards` default is "1", which is safe. The queue backup happens when it's set to "all" or a high number during node issues. For a dedicated, replicated hot tier, I usually set it to "2" if I have at least 3 data nodes. This gives durability without the ingest block if a single node is busy.
You'll want to watch the `write.wait_for_active_shards.timeout` as well. If it's too short, writes will fail instead of waiting.
—chris
>No map change, no merge.
That's the only way it works. We tried the honor system first, and the map was stale within a month. The template field is the right kind of friction.
Our caveat was dealing with bulk changes. When a new framework update deprecated a dozen rules at once, the PR template blocked everything. We had to add a temporary override flag for large-scale, documented cleanups, with a follow-up ticket to reconcile the map. It creates process debt, but it's better than a merge freeze.
sub-100ms or bust
The vendor logo on the header is the entire business model. It shifts liability while selling you the rope.
The real irony is you'll spend those engineering hours building a compliance map, then the auditors will ask for "vendor attestation" anyway. You end up doing the work and still paying the tax, just in a different currency.
null
The "cost savings are eaten by engineering hours" is the inevitable outcome when you trade a product for a platform. You're not buying an EDR, you're buying a construction kit.
The latency and console complaints are symptoms of the same root cause. Elastic Security is a collection of features bolted onto a logging database, not a purpose-built security tool. The unified console is just Kibana with a security dashboard plugin. Of course it's clunky.
You can mitigate the latency with dedicated data streams and aggressive ILM policies, as others said, but that's just more engineering tax. The CPU spikes on macOS are a known agent resource profile issue. You'll need to schedule scans during active hours and throttle them, which again defeats the real-time promise.
The noisy rules are the real trap. Every shop ends up disabling or rewriting half of them. By the time you've built your own rule set and tuned the infrastructure, you've spent enough time to justify just buying CrowdStrike.
keep it simple
Yeah, the "construction kit" analogy really hits home. We chose Elastic for the same reason we picked Kafka over a managed queue - we wanted the control. But you're right, that control comes with a hidden tax.
The Kibana console is the perfect example. For dashboards, it's fine. For real-time threat hunting, it feels like you're constantly fighting the UI's log-search origins. We built a separate front-end just for the SOC team to get around some of the clunkiness, which is... more tax.
But I don't think the alternative is always a commercial EDR. If your team already lives in the Elastic Stack for observability, the context switching cost of another tool can be higher than the engineering tax to tune this one. The break-even point is just really hard to calculate upfront.
Data nerd out
Exactly. That reproducible baseline becomes your team's institutional knowledge. It's the difference between chasing the next UI update and having a predictable core you can version-control alongside your infrastructure.
The part I think gets missed in the "cycles for initial build" calculation is the ongoing support debt. A black-box vendor might change things, but they also absorb the support load for those changes. With your own configs, you own the break-fix cycle for every tweak, forever. That's a different kind of long-term control.
—daniel
Your temporary override flag is a necessary compromise, but it introduces a different kind of risk, the risk of procedural drift. Once that escape hatch exists, the incentive to use it for "just this one quick change" outside of a bulk framework update increases. You're essentially trading merge freeze risk for governance decay.
A way to harden that is to tie the override flag's activation to a pre-approved Change Advisory Board ticket number in your CI/CD pipeline. The process debt isn't eliminated, but it becomes auditable debt, which is far more manageable. The reconciliation ticket is good, but without an automated check linking the override to its cleanup, it's just another item on a backlog that can be ignored.
"No map at all" is a false dichotomy. The other alternative is a tacit, tribal understanding that at least adapts when the vendor changes a rule without telling you. A stale document gives you the confidence of a checklist, but the reality on the ground has already moved on. That mismatch is often worse than admitting you're working from memory.
Your PR template gate is process theatre for the auditors. It ensures a document exists, not that it's accurate or useful. I've seen teams spend more time crafting the map's update commit message to get past the gate than they do considering if the compensating control actually holds water.
You're right to point out that process theatre, where the act of documentation replaces its intent, is a real failure mode. A map becomes a liability when the team views it as a compliance artifact to be updated, rather than a living reference they actually use.
This is often a signal of poor tooling integration. If the map lives in a separate wiki or document repository, it's inherently disconnected from the work. The PR template gate you mentioned reinforces that separation. The goal should be embedding the map's logic into the change workflow itself, so updating it is a side effect of the engineering work, not a separate bureaucratic step.
But I think dismissing all structured mapping as theatre is too far. The tribal understanding you describe is fragile and doesn't scale with team turnover or incident pressure. The problem isn't the map, it's expecting a static document to govern a dynamic system without building mechanisms to keep it relevant.
Let's keep it constructive
The latency issues you're seeing are likely tied to your data retention and streaming configuration. Those "aggressive ILM policies" user216 mentioned are necessary, but they introduce another set of trade-offs. Forcing data streams to roll over faster can help with alerting speed, but then you might be cutting historical data needed for investigations just to get the alerts in time. It's a tough balance.
On the noisy rules, I'd push back a little on the idea that pure-play EDRs don't have this problem. They do. The difference is they often have a more curated set of default exclusions for common business software out of the box. With Elastic, you're building that list from scratch for your specific environment, which is where those engineering hours go. That initial tuning is indeed a heavy lift, but once you've built that baseline rule set, maintaining it becomes part of your normal change workflow, like any other infrastructure-as-code.
—HR