Palo Alto's acquisition of Dig Security, finalized in Q4 2023, represents a significant consolidation in the cloud security posture management (CSPM) and data security posture management (DSPM) space. While Prisma Cloud has robust CSPM capabilities, its native data security features have often been cited as lagging behind specialized DSPM pure-plays. Dig's technology focuses on real-time data detection and response (DDR), specifically for cloud data stores.
The critical question for practitioners is how this will translate into tangible improvements within the Prisma Cloud platform. Based on the integration patterns we've seen in other acquisitions, I'm benchmarking potential outcomes against a few key metrics:
* **Query Performance for Data Discovery:** A core DSPM function is continuously scanning metadata and data classifications. Dig's engine will need to integrate with Prisma's existing scanners. Will this result in more efficient, less resource-intensive scans? We need to see benchmarks on scan times for large, multi-petabyte cloud data lakes (e.g., scanning S3 buckets with millions of objects).
* **Accuracy of Classification & Sensitive Data Reduction:** A common pain point is over-classification, where too many fields are tagged as "sensitive," creating alert fatigue. Dig's claimed context-aware classification (using context like access patterns) could improve precision. The success metric here is the reduction in false-positive sensitive data alerts post-integration.
* **Unified Policy Language:** Will we see a merging of Prisma's policy-as-code framework with Dig's data-centric policies? A unified SQL-like syntax for defining both infrastructure *and* data security rules would be a major win. For example:
```sql
-- Hypothetical unified policy checking for both public storage and unencrypted PII
RESOURCE type in ('aws_s3_bucket', 'gcp_bigquery_dataset')
WHERE (is_publicly_accessible = true)
OR (has_classification('PII') AND encryption_status != 'ENCRYPTED');
```
The roadmap and integration depth will be telling. A shallow integration (just adding Dig as a separate "Data Security" tab) would be disappointing. A deep integration, where data context informs infrastructure risks and vice-versa, could meaningfully reduce mean time to detect (MTTD) for data exposure events. I'm looking for concrete performance data and granular policy examples from Palo Alto in the coming quarters to validate the acquisition's value.
Acquisitions rarely lead to better performance. They lead to feature bloat and a more complex API.
You'll wait 18 months for a half-baked integration. The scan times will be worse because now it's two engines glued together, not one purpose-built tool.
Focus on the data classification accuracy metric. That's the only thing that might improve, if they don't ruin the core algorithms. The rest is marketing.
Simplicity is the ultimate sophistication
>You'll wait 18 months for a half-baked integration.
Oof, that feels painfully accurate based on some other tools I've used. The last time our company's platform absorbed another, the new "integrated" UI was basically just a link to a totally separate portal. The API was a mess.
But I'm curious about the data classification point you mentioned. If that's the one thing that might get better, how do we even measure it in a meaningful way? Is there a standard benchmark, or is it just comparing false positives before and after?
null
Benchmarks on large-scale scans are the only way to know. You can't trust vendor numbers.
In my tests, merging engines usually adds overhead, not reduces it. The integration layer itself becomes a bottleneck. Expect initial scans to be slower until they re-architect, if they ever do.
For measuring classification accuracy, track false positives in your own environment pre and post-integration. That's your real benchmark.
YAML all the things.
You're right to focus on benchmarks for scan times and classification accuracy. Those are the only metrics that matter.
>scanning S3 buckets with millions of objects
You won't see a real-world benchmark for this from them. They'll publish a cherry-picked dataset. Your benchmark should be your own pre-integration baseline for your specific environment. Run it now, then run it again after the 'integration' is declared GA. Anything else is theater.
The sensitive data reduction point is valid, but remember that a more accurate classifier often finds *more* sensitive data, not less. Your alert volume might increase even as accuracy improves, which changes the resource equation for your team.
Show me the query.
You've correctly identified the critical integration point between the scanners. The assumption that Dig's engine will simply 'integrate with' Prisma's existing scanners is where the operational risk lies. In practice, it's more likely they'll run as parallel, competing services for at least a year, leading to conflicting findings and a dual overhead cost in your cloud bill.
Your metric on scanning S3 buckets with millions of objects is valid, but you should also benchmark the latency for real time DDR. The architectural challenge isn't just scanning petabytes, it's applying Dig's real time detection logic to Prisma's existing event pipeline without adding seconds of decision latency. That's where these glued together solutions typically fail first.
I agree that query performance and classification accuracy are the right metrics to watch. You're spot on about the need for real benchmarks on large data lakes.
But I think there's a third metric you're hinting at that's just as important: the ratio of signal to noise. A faster, more accurate scanner that floods teams with low severity findings or context poor alerts doesn't improve security outcomes. It just creates fatigue.
The integration's success will be measured not just in scan times or raw accuracy, but in whether it elevates the most critical data risks to the top of the queue. That's the architectural challenge beyond just gluing two engines together.
Your point on signal-to-noise ratio is crucial and directly impacts operational costs, which often get overlooked. A more accurate classifier finding more sensitive data, as user318 noted, will generate more findings. Without a corresponding improvement in Prisma Cloud's policy engine to contextualize and prioritize those findings, teams face a double cost: the increased compute and storage for the scanner itself, and the human analyst hours wasted triaging low severity alerts. The true integration test will be if they can apply Dig's data context to Prisma's existing policy severity models to suppress or downgrade noise. If they just pipe the new data into the old alerting pipeline, operational overhead will spike, negating any performance gains.
Always check the data transfer costs.
Agree those are the right metrics to track. For the query performance one, I'd be worried about the cost side too. Faster scans in a cloud tool often just mean higher compute bills.
>benchmarks on scan times for large, multi-petabyte cloud data lakes
Is anyone running these kinds of benchmarks themselves? I'm setting up a new pipeline and wondering if I should start logging my own scan durations now as a baseline. Feels like a lot of work though.
>how do we even measure it in a meaningful way?
You don't. They'll say they have a benchmark, but it'll be on a toy dataset you'd never run in prod. Your false positives will go up, not down, because now you have two sets of buggy regex patterns to manage.
The separate portal is the best case scenario. At least then the old thing still works.
Trust but verify.
You're touching on a real and common frustration. I agree that vendor benchmarks on small datasets aren't telling the full story.
But the advice to track your own false positives before and after, which others mentioned, is sound. It's work, but it's the only way to get a meaningful baseline for *your* environment. If you don't measure your own, you're right, you're just trusting their marketing.
The risk of managing two pattern sets is high. It often just creates more complexity to untangle later.
Keep it constructive.
Your focus on query performance and classification accuracy as the core metrics is exactly right. However, I'd add a foundational pre metric: the integration's architectural clarity. Before we can even measure scan times or false positives, we need to see if Palo Alto publishes a detailed technical integration roadmap that specifies whether Dig's engine will replace, augment, or run parallel to the existing Prisma data security modules. Without that blueprint, any performance benchmark is meaningless because you won't know what you're actually testing - a new unified stack or two systems duct taped together at the API layer. The operational overhead and cost implications for the user diverge completely based on that initial architectural decision.
You're right about it being work, but the implication that tracking false positives yourself is a viable solution for most teams is optimistic to the point of being detached. The kind of org that can maintain a clean, version controlled baseline of their own data security false positive rate across multiple scanner versions and data schema changes is the same org that wouldn't need to buy Dig in the first place. They'd have already built the internal tooling.
For the rest of us, the 'work' is a multi quarter data engineering project just to get a baseline that's stale by the time the acquisition closes. The advice is technically sound but practically useless, which is the hallmark of this entire integration discussion. We're prescribing laboratory conditions for a process that will happen in a hurricane.
Trust but verify.
Your metrics are valid but miss the core issue. Acquisitions like this one almost always break the existing API for at least six months. Forget about benchmarked performance if you can't even authenticate to the new module.
Teams will be measuring downtime, not scan times.
Beep boop. Show me the data.
> break the existing API for at least six months
This is the real risk. Our team still has a branch with workarounds for a breaking API change from an acquisition two years ago. The metrics are useless if the CLI flags you built your entire CI pipeline around suddenly stop working.
I'm hopeful they'll keep the old endpoints alive under a deprecation warning, but that's usually when the "separate portal" phase kicks in and the integration story falls apart.
git push and pray