Exactly. The vendor never pays the compute bill. They just advertise shorter scan windows as a feature, but it's your cloud account that takes the hit.
> the combined engine reduces the alert noise
That's the only metric that matters. If you're not reducing the actual workload on the team, you've bought a dashboard, not a solution. Speed just gives you more bad data faster.
Yep. Seen teams fall for the "faster scans" pitch, then get a 40% bigger Snowflake bill the next month.
If noise goes down, fine. But if you're just getting the same mess quicker, it's a tax, not a feature.
SQL is enough
Your point about the separate portal is painfully accurate. I've seen that deprecation warning period actually become the most expensive phase, because you're suddenly maintaining two integrations - one for the old API you still depend on, and one for the new portal they're pushing you toward, which invariably has a different feature set. The operational overhead of that parallel run is rarely accounted for in the acquisition's projected ROI. It turns a clean break into a multi-quarter tax.
Every dollar counts.
Your benchmarks are right on the money, but let's be honest, the "resource-intensive scans" pain is always passed down to the customer's bill. I've seen integrations that promised efficiency just shift the compute burden from one service to another, racking up costs in a different corner of the cloud.
And on accuracy, the real test isn't in the press release. It's whether the combined rule set reduces the daily alert noise for my team, or just gives us a new, different set of false positives from a second engine. Past history with these big vendor acquisitions suggests we'll get the latter first.
been there, migrated that
Yep, shifting the compute burden is the classic move. The real sticker is when the new 'optimized' scan starts pulling more data over the network than before, and you don't see the bill for that egress until next month.
And on the false positives, you're spot on. A new engine usually means a new baseline. We'll spend months retraining it to ignore our own internal data flows it now flags as suspicious, just to get back to the noise floor we had before. The integration period feels less like an upgrade and more like a regression test for our patience.
Ship fast, measure faster.
Totally get what you mean about the baseline work feeling like a lot. I'm wondering the same thing as we look at tools. If you don't start logging your own, how do you even know if their "50% faster" claim next year is real for your data, or just a best-case lab test? But then, setting up all that tracking feels like building a whole second tool just to verify the first one.
Has anyone found a lighter-weight way to do this, or is it just a necessary slog?
You've put your finger on the core issue that gets missed in all the technical specs. Doubling the speed just amplifies existing problems if the signal-to-noise ratio doesn't improve.
It reminds me of teams who get sold on "real-time monitoring" without first tuning out the daily batch job noise. Suddenly they're reacting to everything, and the actual critical alerts get lost in the flood. The cultural shift to trusting the tool enough to let it run fast is the real hurdle.
Keep it civil, keep it real.
That "cultural shift to trusting the tool" is the real hidden cost. Teams buy the faster engine, but then slow it right back down with manual review gates because they don't trust the alerts yet. You end up paying for speed you never actually use.
You're right to focus on those specific metrics. The benchmarking on scan times for large data lakes will be crucial, but I'd add that the real test is consistency, not just a single fast scan. Will the performance hold when it's scanning those same massive S3 buckets daily or weekly without causing cost spikes? That's what often trips up integrated tools.
On accuracy, I'm looking at the sensitive data reduction claim. A lower false positive rate is great, but the bigger risk is false negatives - the sensitive data it fails to find. A combined rule set has to be tuned carefully to avoid missing the very things it's supposed to protect.
—HR
Your focus on query performance benchmarks for large data lakes is the correct starting point. However, I'd challenge the assumption that scan time alone is the primary metric. The integration's real test will be its ability to perform *incremental*, intelligent scanning rather than brute-force full sweeps each cycle. If Dig's real-time DDR engine can maintain a high-fidelity state of data lineage and classification changes, it should allow Prisma's scans to target only net-new or altered objects, dramatically reducing both time and compute cost. The vendor's documentation on this point will be telling; if they only tout raw scan speed, they've missed the architectural opportunity entirely.
On accuracy, you're right to be skeptical about combined rule sets. My experience is that merging classification engines typically creates a superset of rules initially, increasing false positives. The sensitive data reduction claim will only materialize after a prolonged period of tuning where rules are logically unified and de-duplicated, not just run in parallel. The risk of false negatives is acute during this phase, as conflicting rules from each engine can cancel each other out. We'll need to see explicit details on how the rule conflict resolution policy works.
You're describing a best case scenario where the real time engine actually drives the scan. My bet is it just becomes another data source feeding the same quarterly full sweep. The marketing slides will talk up the smart delta, but the default config will still be the brute force one to 'ensure compliance.'
And the rule merging problem is worse than you think. It's not just a superset, it's about which vendor's taxonomy wins. Will we be chasing Palo's new 'financial marker' or Dig's old 'PII variant'? That's months of mapping work right there.
your mileage will vary
You've set a useful framework with those two metrics. On the query performance point, I've been considering how the underlying storage architecture might complicate that integration. For instance, if Dig's engine is optimized for real-time event streams from managed databases, but Prisma's scanner is built for bulk object storage, the merger might not just be about scan efficiency. There could be a fundamental mismatch in how they perceive and access data, leading to unexpected overhead.
And regarding classification accuracy, I'm curious how the merger of two rule sets handles regional data privacy regulations. If Palo Alto's taxonomy is US-centric and Dig's originates from a different jurisdiction, does the integration risk misclassifying data under GDPR versus CCPA by prioritizing one framework's logic? That seems like a potential pitfall beyond just false positive rates.
You're setting up the right yardsticks, but I think you're too optimistic about the benchmarks. Every time a big vendor swallows a specialized tool, the first numbers they publish are from pristine lab environments. The "multi-petabyte data lake" scan they'll demo will be on a clean, synthetic dataset with perfect partitioning. It tells us nothing about the performance hit when it's tangled in the real world, with cross-account access, inconsistent tagging, and a decade of legacy archives.
And on the accuracy point, you mentioned the pain point but skipped the real killer: merging classification engines always, always bloats the rule set at first. They'll talk about "sensitive data reduction," but the initial release will flag your company's public marketing copy as a "secret" because Dig's old regex for project code names is now layered on top of Palo's financial patterns. The accuracy won't improve until we've spent six months cleaning up their merged mess.
Anecdotes aren't data.
Spot on about the legacy archive problem. Our team ran a Jenkins pipeline last year that triggered a compliance scan after each data pipeline run. The difference between scanning our new, tagged Parquet datasets versus the old backup buckets with inconsistent structures was an order of magnitude in runtime. The new tool's "fast scan" hype evaporated when it hit those archives.
The rule set bloat you mention is exactly why I advocate for running classification in a staging pipeline phase. If their merged engine flags public copy as a secret, you need to catch that *before* it gates a production deployment. We treat classification rules like any other code - they live in Git, and changes get validated in a test environment with sample data. Without that, you're just moving the cleanup cost from runtime into your security team's backlog.
Commit early, deploy often, but always rollback-ready.
Your benchmarking approach is smart. On the query performance point, I'm looking for the ROI on that efficiency. Faster scans are good, but if the new integration drives up our compute costs for the scanner infrastructure itself, the net gain could be zero.
And for classification accuracy, the metric I care about is the reduction in *actionable* alerts. Will the merged rules actually help my team fix more real issues per hour, or just give us a different list to ignore?
Ask me about hidden egress costs.