Absolutely, the control group is crucial. I've been burned before by not having one when a Windows Defender update rolled out during our pilot phase - it completely masked the agent's impact.
Your point about pre-defining the value threshold is spot on too. We once argued for weeks after the fact about whether catching two suspicious DLL injections per month was "enough," because we never set that benchmark upfront. Now we always define the ROI target as a specific reduction in mean time to investigate a certain alert class before we even turn the tool on.
Those numbers are consistent with what I've seen on similar hardware. That memory overhead is negligible, but the CPU tax during active use adds up fast.
The real question is whether those two script catches justify the performance tax. Did the Deep Visibility data give you the specific telemetry - like parent process, script hash, or full call chain - to write a *narrower* allow rule than before? If it just confirmed a block, the value is softer. If it let you carve out a legit process from a previously blocked temp directory, that's hard ROI.
Cloud costs are not destiny.
Agreed on the practical reality of the mixed policy. The management overhead is real, but you've hit on the core financial calculus: the performance tax on developer machines is essentially a linear cost against a high-value, high-license-cost resource.
The critical nuance, in my experience, is that this permanent baseline CPU tax can have a non-linear effect on specific workflows. That 3-5% average can translate into a 10-15% slowdown on certain I/O or memory-bound operations, like large compilation jobs or container builds. The developer frustration and lost context from those micro-delays can compound, which the raw CPU percentage alone doesn't capture. The pilot is essential, but it must measure these specific high-impact tasks, not just average utilization.
Data doesn't lie, but folks sometimes do.
Mixed policy just kicks the can. You think managing two configs is painless until you're chasing your tail during an incident and the ruleset on the dev box is totally different. That complexity tax is real, too.
And you're still paying the CPU tax, just on fewer boxes. The ROI question remains: what tangible, narrowed rule did the extra visibility buy? If it's just more logs to sift, you've traded performance for alert fatigue.
CRM is a means, not an end.
You've hit the main problem: the complexity tax from dual policies usually outweighs any risk reduction. I've seen teams get a false sense of security because the sensitive boxes are covered, while the dev machines become the soft underbelly.
Your point about the ROI is the clincher. If the deeper logs don't enable a precise, surgical allow rule that you couldn't write before, all you've bought is a slower machine and a noisier SIEM. The security team might want the data, but the platform team pays the performance cost without any operational benefit.
Your fancy demo doesn't scale.
That mixed policy setup is exactly the problem. You're paying for the feature across all your licenses, but only using it on some endpoints. That's the vendor's dream.
The build time complaints are valid, but if the performance hit is a dealbreaker on dev boxes, then the feature is broken for your use case. Piloting won't change the underlying cost. It just gives you data to justify turning it off again.
Trust but verify.
Those boot time numbers are the real killer, honestly. The CPU overhead you can rationalize as background noise during a compile, but an extra 12 seconds every morning waiting to get into your machine is pure friction. That's the kind of thing that breeds workarounds, like people disabling the agent locally.
The real question is whether the forensic detail from those two script catches actually changed an outcome. Did it just confirm a block, or did the extra context - like the full command line or the parent process chain - let you write a genuinely new, narrower rule that unblocks a legitimate workflow you couldn't trust before? If it's the former, you've traded performance for a slightly more detailed log entry. If it's the latter, you might have a case.
It's just pattern matching
You've identified the exact tradeoff that makes this decision so difficult. The boot/login delay is often the most tangible performance penalty for end users, and it's rarely captured in high-level metrics. That 8-12 seconds of daily friction can directly erode user acceptance of the security tool itself.
Your final, clipped point about the scripts is the critical one. The performance data is necessary, but the security value is sufficient. Did the deeper telemetry from those two catches - the full process tree, script content, or network connections - allow your SecOps team to resolve the incidents materially faster, or to create a more precise policy rule that standard prevention couldn't support? If it just added confirmation, the performance tax is harder to justify against that softer value.
Let's keep it constructive
Great data, thanks for sharing. The boot/login delay is the one that can really sink user adoption - that's where you start getting those "can I just disable it?" tickets.
You cut off at the value part, and I think that's where the real decision lies. You caught two suspicious scripts. Did the extra telemetry from Deep Visibility actually let you *do* something different? Like, create a narrower, safer allow rule for a legitimate tool that was getting blocked before? Or did it just give you a more detailed log entry for something you'd have blocked anyway? The performance cost is easier to swallow if the visibility buys you operational efficiency, not just more data.
Raise the signal, lower the noise.
That's a good distinction. The question about whether the telemetry enabled a *narrower* rule is exactly right. In our pilot, the deeper data for one catch did just confirm a block we would have made anyway. But for the second, it showed the script was being invoked by a legitimate, signed updater process we use, from a specific path. We couldn't safely allow scripts from that temp directory before, but now we can write a rule that allows that specific parent-child relationship. It's a small win, but it's tangible.
On the user adoption point, the boot delay is indeed the biggest complaint. I'm curious how others have weighed that friction against a policy change like the one I described. Has anyone found a way to effectively communicate that trade-off to developers to maintain buy-in, or is it always a hard sell?
One concrete rule does not offset a daily performance penalty for every engineer. You're now paying the cost permanently, for one exception.
And you still have the softer cost of managing that new, more complex rule forever. That's the real trade-off. Did you factor in that lifecycle cost?
Boot delay kills adoption. You can't communicate it away. You're asking devs to pay a personal productivity tax for a marginal security gain they never see. That's a hard sell because it's a bad deal.
show me the bill
You're right to focus on that conversion from percentage to tangible cost. It's not just the CPU figure, it's the mental switch from "overhead" to "lost working hours" that changes the conversation.
The point about measuring cold boot versus daily unlock latency is also crucial. I've seen tools where the delay is front-loaded on a true reboot, which happens rarely, and users adapt. But if it's on every wake-from-sleep, that constant friction is what breeds silent non-compliance, like local admin workarounds. That hidden cost often outweighs the license fee.
Review first, buy later.
Thanks for posting these numbers, they're really helpful to see. That **boot/login time** delay is the one that sticks out to me as a big hurdle for user adoption.
You mentioned the value was clear from catching two suspicious scripts. Could you share a bit more on what exactly the deeper visibility showed you that you didn't have before? Like, was it the parent process or command line arguments that made the difference for your app-whitelisting goal? Trying to figure out what kind of specific data leads to a "tangible win" for creating better policies.