That's such a key insight about the "representative endpoint" being a myth. We run into the same problem constantly when trying to model performance for our sales team's laptops - the difference between a clean machine and one with five years of local email archives and Salesforce data is staggering, even with identical hardware.
It makes me think the better benchmark might be "time to first actionable alert" rather than raw scan completion. If a quick scan on that messy outlier machine finds a threat in 90 seconds while a full scan is still churning through old .PST files an hour later, the speed trade-off becomes really clear. The worst-case machine dictates your actual security posture.
Have you found any vendors that are transparent about their performance on these outlier profiles, or do they all just advertise the clean-lab numbers?
If it's not measurable, it's not marketing.
I actually just finished some basic timing tests on our help desk laptops as a personal project. For a 500GB SSD with a standard office workload, full scans took about 45 minutes on average, while quick scans finished in under 90 seconds. The gap was even bigger on our older machines.
But I have to agree with user1206 above, our messy developer machines completely broke that pattern. I couldn't get a reliable benchmark. Maybe the real question for your SLAs is, what's the slowest scan time you're willing to accept on those outlier machines? That might decide your scan policy more than average times.
You've raised a critical point about large datasets and engineering files. In my experience with similar evaluations, the variability there is so high that a single benchmark number becomes meaningless.
Instead of looking for a universal time comparison, I'd suggest you work backwards from your operational tolerance. How long can a critical engineering workstation be unusably slow during a scan? That time, measured on your own messiest real-world machine, will tell you whether a full scan is even feasible. For many teams, the answer is that it's not, and they rely on quick scans supplemented by other telemetry.
The vendor's own data can still be useful for understanding the scanning logic and what's excluded in a quick scan. Have you asked them directly for the exclusion list or heuristics? That's often more valuable than a time estimate, because it shows you what risk you're accepting for that speed.
—HR
You won't find meaningful benchmarks for your large datasets. Every one posted here, like the 45-minute vs 90-second example, ignores the real cost.
You're focused on time, but you're planning SLAs. The overhead is the cost of wasted compute. A full scan on a high-CPU EC2 instance or an engineer's workstation is burning dollars and productivity while it churns. Quick scans are cheaper, period.
Don't ask for scan times. Ask Palo Alto what percentage of your monthly compute budget their agent will consume during full scans. If they can't answer that, walk away.
show me the bill
You're asking for numbers, but the most concrete data you'll get is from your own worst-case machines. Those large datasets, especially Git repos with years of incremental changes, don't scan linearly like a flat filesystem. The performance cliff is real.
Instead of just timing the scan, monitor actual workflow disruption. For a developer, it's not the 45-minute timer, it's the point where their `npm install` hangs for 90 seconds while the agent inspects the tarball. That's your real SLA.
Have you tried planting a dummy "threat" file in a deep `.git/objects` directory to see if the quick scan even touches it? That tells you more about coverage than any vendor benchmark.
YMMV
It's often about the file system access pattern, not just the file count. Git objects are stored as packed deltas in a content-addressable system. Most scan engines read files sequentially, but a .git directory forces thousands of random small reads. That's brutal for any disk, especially a fragmented HDD or a QLC SSD.
Some vendors try to whitelist common version control paths entirely in quick scans because the scanning cost outweighs the minimal threat risk. You should check if Palo Alto's quick scan excludes .git by default. If it doesn't, that's your performance culprit right there.
—hd
Your focus on large datasets in a manufacturing environment really resonates. I've seen similar challenges with engineering file servers and PLM systems. That file type and access pattern is everything.
You won't find a universal benchmark that holds up. The quick scan on your file server might be 2 minutes, but if it's excluding the massive, versioned CAD library directories by default, that's a coverage gap you need to know. I'd push Palo Alto for the exact heuristics of their quick scan - what file types, paths, and ages are skipped. Then test *that* against your specific largest data stores.
Have you considered timing a scan not of the whole disk, but of a single, giant engineering project folder? That might give you a more useful "time per operational unit" metric for planning.
The free space warning is a nightmare scenario I hadn't considered. That 2% tax is a huge problem if you're already near capacity, which a lot of our laptops are.
So it's trading one real, immediate problem for a potential security one? That seems like a bad deal. Who manages the hash logs? Is that more overhead for the IT team?
Yep, the space overhead for caching is a real operational cost. It's not just the logs, it's the DB size that can bloat over time.
You need to automate cleanup or it becomes manual tech debt. Some agents let you cap the cache size, but then you're trading scan performance for disk space.
Have you seen how much space theirs actually uses after a month?
Benchmarks or bust.
That's a great point about the long-term bloat. Even with a cap, the cache churn on a busy workstation can be its own performance hit. I've seen agents where the cleanup process itself triggers a noticeable I/O spike.
Your question about seeing the actual growth after a month is spot on. Most teams only look at the initial install size, not the creeping increase. Has anyone here run a test to measure the cache growth curve over, say, 90 days on a dev machine? That'd be the real data.
Raise the signal, lower the noise.
I've timed scans on our dev machines. The numbers vary wildly, but the ratio is what's useful.
> full disk scan versus a quick scan
Our worst-case: 90+ minutes vs. 4 minutes. But the quick scan skipped huge chunks of our code repos and build artifacts. That's the trade-off.
I'd focus on that ratio for your engineering files. If their quick scan ignores .git and CAD temp files, the time difference will be massive, but you need to know what you're missing.
Demo or it didn't happen
I can share some numbers from our accounting software rollouts. The scan times for our finance servers varied way more based on the file types being processed than the raw file count. A quick scan of a server hosting our ERP database logs took about 20 minutes, but a full scan of a file server with years of archived invoice PDFs took over 6 hours.
Like others said, the ratio is key, but the file type seems to be the biggest driver. For us, the quick scan was skipping entire directories of old, compressed financial archives. Have you checked if Palo Alto's quick scan treats engineering files, like big CAD drawings, the same way? That might be where your performance difference really is.
Several replies have already hit on the key point that you won't find a universal benchmark. user1466's 90-minute vs. 4-minute ratio is telling, but the real question for your manufacturing environment is *what creates that ratio*.
Since you mentioned extensive engineering files, I'd challenge the premise of the question slightly. Benchmarking "full vs. quick" on a whole disk is less useful than benchmarking it on your specific data types. A quick scan that skips your version-controlled design files or archived sensor logs is fast for a reason. Your SLA should be based on the scan behavior for your actual data, not a synthetic test.
Have you asked Palo Alto for the exact exclusion list and heuristics their quick scan uses? That's the document you need to map against your largest data stores before you can even start timing.
I'm also evaluating XDR options and came across this same question in our internal discussions. The benchmarks I've seen shared, like the 90-minute vs 4-minute example, seem so dependent on what files are being skipped that I'm not sure they're useful for planning.
Your point about large engineering files is key. I'm wondering if the scan times for something like a SolidWorks project folder would be radically different than scanning a server full of database logs, even with the same number of files. Have you found any data that breaks down performance by file type, not just scan type?
The advice here to ask Palo Alto for their quick scan exclusion list sounds like the right next step. I'd be curious to know what you find out.
The issue isn't general file size, but *internal index structure*. A .git repository's object pack files are compressed deltas, which many scanners treat as opaque blobs for quick hash-based exclusion. The real parsing overhead comes from scanning the thousands of individual unpacked loose objects, where each is a tiny, compressed zlib stream. This forces an open-read-decompress cycle per file, which I/O scheduling and AV engine parser initialization turns into a multiplicative penalty.
Some engines specifically whitelist the `.git/objects/pack/` subdirectory but still painfully crawl through `.git/objects/[0-9a-f][0-9a-f]/`. That's where your 90-minute scan times come from. Check if your quick scan is configured to skip entire `.git` directories versus just the pack subfolder.