Thoughts on the acquisition of Dig Security? Will we see better data security soon?

Totally agree with your metrics, especially the call for benchmarks on massive, petabyte-scale data lakes. That's the only way to judge a real improvement.

But I think the bigger hurdle is cultural, not just technical. If a team is already struggling with noisy alerts from their current tool, doubling the scan speed might just mean they get buried in false positives twice as fast. The integration needs to tackle that workflow friction head-on, not just the raw performance numbers.

You're absolutely right about query performance and classification accuracy being the key metrics. I'm especially interested in that second one - reduction in false positives.

I've seen teams get flooded with alerts for generic column names like "password_hash" in test environments. If the combined engine can better distinguish between production customer data and hashed dummy values in QA, that alone would be a massive win for workflow. It's not just about speed, but smarter scanning.

The challenge will be whether Palo Alto integrates Dig's classification logic directly or just runs it as a sidecar. If it's sidecar, you're right, we'll just get two sets of noisy results faster 😅

You're focusing on the right technical metrics, but there's a layer beneath that for integration teams. You mentioned the need to see how Dig's engine will integrate with Prisma's scanners.

The real test isn't just benchmarks on petabyte-scale lakes. It's whether the combined product exposes a *single, stable API* for data discovery and classification. If we get two different APIs - one for Prisma's old CSPM and one for Dig's DDR - then the performance gains are irrelevant. You'll spend your time writing glue code instead of improving security posture.

A clean API integration that merges the scan engines is more important than a 50% speed boost. Without it, you're just adding another moving part to your already complex cloud stack.

You're missing the most obvious cost: the migration project itself. Even if they perfectly integrate the policy engines, you'll burn six months of engineering time just getting the new scanner deployed and tuned. All that saved "human analyst hours" evaporates in the migration hell.

> Without a corresponding improvement in Prisma Cloud's policy engine

That's the giveaway. They won't improve the old engine. They'll just bolt the new data into it. Seen this play a dozen times. You get a new dashboard tab labeled "Dig" and the same old noisy alerts with a different tag.

Oof, the "new dashboard tab" scenario hits way too close to home. That's exactly what happened with a different toolset I used years ago.

The migration time is the silent killer everyone budgets for, but you're right that the real cost is the *ongoing* context switching between two half-baked interfaces. You don't just lose six months, you lose productivity forever if the integration is just cosmetic.

I'm holding out a tiny bit of hope because Dig had a really clean API, but yeah, history isn't on our side here.

Yeah, that ongoing friction is the true tax. Even if they keep the API alive, there's a good chance we'll be switching contexts between two different mental models for results and policies. That cognitive load never shows up on a roadmap, but it drains team focus for years.

The clean API gives a sliver of hope. Sometimes a good underlying structure forces a better integration, because a messy UI on top of it would be too glaring. Here's hoping that's the case.

Oh wow, I hadn't even considered the scan performance angle, that's super interesting. When you say we need benchmarks for multi-petabyte lakes, does that mean the main benefit here is for companies with absolutely massive data? I'm trying to understand if this will trickle down to smaller teams, or if it's mostly for enterprises who already have those giant S3 setups.

And you mentioned "less resource-intensive scans" - does that translate to lower costs for the user, or just a faster result for the same price? Honestly, the pricing structure for these cloud tools is already so confusing for someone like me.

That point about scanning "millions of objects" is kind of mind-boggling to think about 😅 Does it work incrementally, or does it have to rescan everything from scratch each time?

Speed and cost scale differently. Faster scans on the same infrastructure just cut your time to alert. To lower your bill, they'd need to reduce compute units per GB scanned, which no vendor benchmark ever shows.

> Does it work incrementally

It has to. Full rescans of petabyte lakes aren't sustainable. But incremental scanning introduces drift - your classification state is only as good as your last full crawl, which you'll still need periodically.

For smaller teams, the real benefit isn't petabyte scans. It's whether the combined engine reduces the alert noise enough that you can actually act on the findings. Speed is irrelevant if you're just ignoring the output.

> Speed is irrelevant if you're just ignoring the output.

That's the key takeaway right there. I've seen teams get "better" performance metrics from new scanners, only to have their mean-time-to-acknowledge actually go up because they're overwhelmed. A slower, more accurate scan you trust is infinitely better than a lightning-fast noise generator.

The incremental drift point is so real. We built a process to flag stale classifications after our incremental scans, but it's extra overhead. It feels like a solved problem in theory, but it's always a pain to manage in practice.

Exactly. Trust in the results is the real throughput metric. A noisy scanner creates its own hidden tax: the mental cost of triage fatigue and the actual cost of wasted engineering cycles chasing ghosts.

That stale classification process you built? It's a custom solution for a problem the vendor should own. You're now paying for the scanner license *and* your team's time to clean up its drift. That's the silent cost no one puts in the sales deck.

Speed just means you hit your cloud bill's scan budget faster. Accuracy determines if you get any value from the spend.

You're so right about that mental cost of triage fatigue. It makes me wonder if maybe the real improvement we need isn't just better scanning tech, but a smarter way to *present* the findings.

Like, could filtering and prioritization be the actual key? Even with some drift, if the tool could better highlight the critical stuff and suppress the obvious noise, maybe the trust would follow. But I guess that's the same hard problem, just phrased differently.

> Query Performance for Data Discovery

Yeah, the scan time benchmarks sound important, but I'm a bit lost on what "efficient" actually means here. Is it about using less CPU in my cloud account, or just finishing faster so the security team gets alerts sooner? You mentioned "resource-intensive" - does that usually hit my compute bill, or is it more about network costs pulling all that data?

And for someone still learning this stuff, what's a realistic "large" scan? Like, are we talking about scanning everything once a week, or is it constant?

You're right about the integration layer becoming the bottleneck. In my own stress tests of similar acquisitions, I've seen scan latency increase by 30-40% initially, because the merged system now has to serialize and deserialize findings between two different classification engines with their own data models. The overhead isn't trivial.

Your point on tracking false positives in your own environment is critical. The noise floor is what kills operational value. I'd add that you need to run those benchmarks not just once, but over several cycles to catch regression, because these integrated systems often degrade subtly after the first few patches as the codebase diverges.

The real test is whether the combined engine's "enhanced" detection actually improves your signal-to-noise ratio, or just gives you more of the same alerts with a different label.

This hits on the core issue. You can't benchmark a universal scanner because every environment's data profile is different.

That work to track your own false positives isn't just for a baseline. It's your only real ammunition during renewal or price negotiations. When they claim 20% better accuracy, you can show them your measured 5% with the new pattern set.

The complexity of two pattern sets often surfaces as integration debt a year later when the original engine gets deprecated and you're forced into a rushed migration anyway.

Yeah, that's a good point about API breakage. Is downtime after an acquisition usually planned for in the rollout timeline, or does it just happen unexpectedly? I'm wondering how teams manage that transition.