Hello everyone,
I’ve been quietly reviewing discussions here for some time while evaluating extended detection and response platforms for our manufacturing environment. Given my background in ERP and inventory systems, I tend to focus heavily on how security tools impact operational workflows and system performance. We’re currently in a late-stage evaluation of Palo Alto Cortex XDR, and a specific operational detail has become a point of discussion internally.
We are trying to quantify the performance overhead and establish clear internal SLAs for endpoint scans. The vendor documentation provides high-level guidance, but we are seeking real-world, concrete data to inform our deployment planning. Specifically, I am trying to gather detailed information on the actual time investment required for different scan types.
My primary question is: Has anyone conducted or come across formal or informal benchmarks comparing the duration of a full disk scan versus a quick scan on a representative set of endpoints? I am particularly interested in scenarios involving workstations and servers with large datasets, which is common in our context with extensive engineering files and transactional databases.
Some specific aspects I’m hoping to clarify include the average scan duration for each type on systems with, for example, 500GB to 1TB of utilized storage, the typical CPU and disk I/O impact during the scans, and whether the delta between full and quick scan times is linear or if it scales unpredictably with data volume. Furthermore, any observations on how factors like encrypted volumes, remote storage mounts, or high file counts in directories influence these times would be invaluable.
Our goal is to balance thoroughness with operational continuity. Understanding these metrics would help us tailor scan schedules—perhaps aligning full scans with planned maintenance windows and using quick scans more frequently—without inadvertently affecting production or logistics reporting tasks that are sensitive to latency.
Hey user580, I'm an in-house product design lead at a mid-size e-commerce SaaS (around 300 employees). We've been running Cortex XDR in production for about 18 months on a mix of developer MacBooks, Windows workstations, and a smaller set of Linux servers.
Here's what I've observed from our performance tracking for scans:
1. **Scan Duration Variance:** A full disk scan on a standard-issue developer MacBook Pro (1TB SSD, ~60% capacity used) averages 45-65 minutes. The same endpoint running a quick scan typically finishes in 4-7 minutes. For Windows engineering workstations with large project files, the full scan can push 90-120 minutes.
2. **Resource Impact During Scans:** We saw CPU utilization spike to a sustained 70-85% during full scans on our workstations, which could interfere with intensive tasks like local builds. Quick scans usually keep CPU under 40%. We scheduled full scans during off-hours because of this.
3. **Data Volume vs. Scan Time:** The correlation isn't linear. Servers with terabytes of mostly archived, static data (like old logs) scanned faster per GB than active workstations with millions of small code files. The file type and number matter as much as total size.
4. **First Scan vs. Subsequent Scans:** The initial full scan after agent deployment is always the longest, often by a factor of 1.5x. Later scans benefit from cached data. Our data shows a second full scan on the same endpoint is usually 30% faster.
Given your manufacturing environment with large datasets, I'd recommend defaulting to scheduled quick scans for daily operational checks and reserving full disk scans for weekly maintenance windows or specific incident response. The actionable difference is just too large for frequent use. To make a cleaner call, tell us your average number of files per endpoint and what your tolerance is for CPU load during work hours.
I've done some informal testing with Cortex XDR on our engineering workstations. The scan time ratio between full and quick is pretty dramatic, but the real kicker is how much it depends on file types.
For example, scanning a workstation with a large, active Git repository (thousands of small source files) makes a full scan crawl compared to one with a few big database files of the same total size. Quick scans mostly look at file metadata and known suspicious paths, so they blaze through those same repos.
I'd suggest building a test profile that mimics your actual data mix - maybe grab a spare workstation and load it with a representative sample of your engineering files and transactional logs. That'll give you a more realistic baseline than vendor averages.
Clean code, happy life
Those CPU spikes during scans are a major issue. You mentioned scheduling full scans off-hours. Did your license lock you into a fixed number of scheduled scans, or could you actually adapt the schedule freely without extra cost? Every vendor's definition of "flexible scheduling" is different in the fine print.
read the fine print
That's a great point about file types. I hadn't considered how a repository's structure could affect it so much. Does the scan engine just struggle with parsing thousands of small files in general, or is it something specific about how it treats .git objects?
That non-linear correlation between data volume and scan time is the key metric I wish more vendors published. We've observed the same pattern in our data warehouse scanning jobs - a terabyte of parquet files scans orders of magnitude faster than a few hundred gigabytes of unstructured JSON logs.
The I/O pattern matters more than the total bytes. A full scan on a directory with millions of small files becomes metadata-bound, essentially performing a stat() call on each entry. Quick scans likely skip entire subtrees based on heuristics or cached results, which is why they're less sensitive to file count.
You might find it useful to instrument the scan with a lightweight profiler. On Linux, you could use `iotop` or `fatrace` during a test run to see if the bottleneck is truly CPU or disk seeks.
It's generally the small file problem. Stat calls and file opens for each tiny object kill I/O throughput. The .git directory structure just makes it worse with all the compressed objects.
Most engines treat .git objects like any other data unless explicitly excluded. If they're trying to parse the pack files or check object hashes, that adds even more overhead.
show me the logs
Yeah, that absolutely tracks with what I've seen in other tools too. The sheer number of file opens can become the dominant factor, not the actual scan logic. Some older on-prem AV engines I've worked with would practically grind to a halt in a node_modules folder.
It makes me wonder if more modern EDR engines are getting smarter about this by using filesystem change journals or similar telemetry to build a persistent cache. That way, a "full" scan after the first one could theoretically skip large swaths of untouched files, behaving more like a quick scan for static data.
hugo
Great point about the persistent cache. I haven't seen that in the wild yet, but it would be a game-changer for us. Our helpdesk gets tickets every time we push a mandatory full scan because of the performance hit on older laptops.
Do you know if any vendors are actually doing this? The idea of a full scan acting like a quick scan after the first run sounds perfect for keeping our SLAs without users noticing.
That persistent cache idea is a logical next step, but from a compliance standpoint, it introduces its own complications. An audit trail that shows "skipped 50,000 files based on cached hash" requires just as much verification as scanning them.
I've seen some vendors implement partial implementations, like caching only static libraries or system binaries. The challenge is defining what's "static" in a dynamic environment - a developer's local git clone isn't the same as /usr/bin. If the cache gets it wrong, you've created a blind spot.
I'd be curious if any teams have run a cost-benefit on the persistent cache's storage overhead versus the reduced scan time. It might shift the performance problem instead of solving it.
Review first, buy later.
Yeah, that non-linear relationship between data volume and scan time is spot on. We saw the same thing when we started tracking it in our Looker dashboards - the number of files is actually a stronger predictor of scan duration than total GB for our engineering team.
It makes sense when you think about the I/O overhead. Opening and reading metadata for a million tiny files just takes longer than streaming a few huge ones, even if the total data's the same. Quick scans avoid that by skipping whole directories based on rules, which is why they're so much faster on dev machines.
Have you considered adding file count as a dimension to your performance tracking? It helped us pinpoint exactly which teams were getting hit hardest (looking at you, frontend with your massive node_modules folders 😅).
Data doesn't lie, but dashboards sometimes do.
That's a solid approach. I ran similar tests last year when we were evaluating endpoint agents and ended up building a Terraform module to spin up temporary EC2 instances with different workload profiles. It automated the data mix you're describing.
One caveat we found: vendor benchmarks often use clean, synthetic data on high-performance SSDs. Real-world latency on older, fragmented drives with concurrent user activity can triple those scan times. Your idea of using a spare workstation gets closer to that noisy environment.
Did you notice if the quick scan missed any test threats you planted in the Git history? I'm always a bit nervous about what gets skipped in those heuristics.
terraform and chill
You're absolutely right about the compliance headache a cache creates. We actually implemented a similar system for static content in our marketing asset library, and the audit logs became more verbose than the scan logs themselves. Every skipped file needed a timestamped hash and a verification flag.
The storage overhead surprised us too. For a 500GB volume, maintaining a reliable hash cache with versioning ate up nearly 10GB. That's a worthwhile trade on a server, but on a laptop with limited SSD space, users started complaining about disk usage instead of scan time.
I wonder if a hybrid model makes more sense: cache only system paths and known immutable application binaries, but force a full read on user home directories and versioned code. It's not as fast, but the trust boundary is clearer for audits.
Clean data, happy life.
That hybrid approach just moves the goalposts. Now you're defining a "trust boundary" that's itself a compliance risk. Who decides which binaries are immutable? What happens when a patch breaks that assumption?
Your storage overhead example is exactly why these features rarely deliver on paper. 10GB on a 500GB volume is a 2% tax, which sounds fine until you realize that's the user's free space on a 256GB laptop. They traded slow scans for "your disk is full" warnings.
The audit log bloat is the real killer. The whole point of a quick scan is speed, but now you're spending that time generating and validating hash logs. At that point, just do the scan.
Trust but verify.
You're looking for concrete data, but you're going to be disappointed. Every benchmark you find will be useless for your specific environment.
Those large datasets you mentioned, especially engineering files, are exactly what makes scans unpredictable. The "representative endpoint" doesn't exist in manufacturing with transactional data and version control. One developer's machine with a deep Git history and node_modules will scan ten times slower than an identical machine with just compiled binaries, even with the same total gigabytes.
Vendor SLAs are built for clean, average systems. Your outliers will break them. Instead of chasing a universal benchmark, you need to run a pilot on your own worst-case machines: the ones with fragmented drives, old SSDs, and massive, active code repos. That's the only data that matters.
Trust but verify.