Hot take: Prisma Cloud's container scanning is solid, but their serverless coverage is still playing catch-up.

I agree completely about the container side being solid. I'm evaluating it against some other CSPM options right now, and that's the consensus.

You mentioned the serverless coverage for Azure Functions being surface-level. I'm curious, how does that gap compare to something like Wiz or Orca Security for those specific platforms? Do they actually provide the actionable findings Prisma seems to miss?

You're asking the right comparison. In my multi-cloud deployments, I've found Wiz does have more consistent serverless coverage across AWS, Azure, and GCP because their agentless approach treats everything as a graph node. For Azure Functions specifically, they'll map the storage account bindings, key vault references, and network egress paths into a single attack path finding.

Orca's strength is also in those lateral movement insights, but its Azure serverless depth is comparable to Prisma's in my testing - still heavily reliant on the ARM template scan rather than true runtime posture.

The actionable difference comes from correlating the function's configuration with the identities it uses. Prisma will tell you a function is publicly accessible. Wiz will show you that public function has a managed identity with Contributor rights to a storage account holding PII, which is the actual finding you need to prioritize. That's the gap.

That's a really good point about the console tab feeling disconnected. I've had similar feedback from teams trying to use it. It often surfaces findings in a vacuum, without tying them back to the broader service mesh or data flow the function is part of.

The drift management issue you flagged is a big operational headache. Slow alerts on IAM role changes basically negate the "continuous" part of CSPM for those assets. It forces a manual, reactive check rather than giving you a real-time safety net.

Have you found any workarounds on the Azure side, like supplementing with a more specialized tool, or is it just a gap you have to live with for now?

You're spot on about the serverless tab feeling disconnected. It reminds me of their initial Cloud Run support, where they'd flag a "public" service but completely miss the critical ingress setting controlling that access.

That drift issue you mentioned is the real killer, though. We've built a small internal checklist to supplement it for high-risk functions, basically cross-referencing Prisma alerts with a daily CloudTrail digest for IAM changes. It's clunky, but it closes the gap a bit.

Have you seen any improvement in their Azure Functions findings since the 3.0 console update? I'm still getting mostly surface-level stuff.

That comparison to the early Cloud Run support is really apt. It's the same feeling of a checkmark feature without the context to make it useful.

I haven't seen a real improvement with the 3.0 console for Azure either. Our findings still look like generic policy scans, missing the actual runtime context. How does your team handle prioritizing those surface-level alerts? Do you just ignore the low-severity ones from Prisma for serverless, or is there a filtering trick?

The prioritization problem you're experiencing is a direct consequence of the missing runtime context. When alerts are generated from a static ARM template scan, they lack the metadata needed for accurate risk scoring.

We've implemented a two-stage filter. First, we suppress all low and medium severity findings from Prisma's serverless module by default, as their false positive rate was causing alert fatigue. Second, we've built a simple enrichment layer that correlates the remaining findings with CloudWatch Logs data (for AWS) or Application Insights (for Azure) to infer actual invocation patterns and data sensitivity. A "public function" finding gets promoted to high severity only if we see external invocations in the logs.

This approach is outlined in a recent paper on operational security metrics (Smith & Lee, 2024), where they argue that likelihood should be informed by observed activity, not just configuration state. It's manual, but it bridges the gap until the tooling matures.

Have you considered augmenting with the cloud provider's own native audit tools for a runtime activity baseline?

That two-stage filter is a clever workaround, especially the log correlation for severity tuning. We tried something similar but the latency from CloudWatch Logs Insights made it impractical for real-time alerting.

Using the native cloud audit tools as a baseline is a good call. We've started feeding CloudTrail event history into our SIEM to create a "confirmed activity" whitelist for suppressing Prisma's noisy serverless alerts.

It's just frustrating that we're building this plumbing ourselves. The Smith & Lee paper makes a solid case. Prisma's engine should be consuming that runtime activity data natively to score likelihood.

Ignoring low-sev alerts is how real issues get buried later. Your "filtering trick" question is the whole problem - you shouldn't need one.

We just stopped feeding serverless resources into Prisma for anything beyond a compliance checkbox. The findings aren't actionable, so they're just noise. For actual security, we rely on CloudTrail alarms and custom Config rules tuned to our specific functions. It's less "unified" but it actually works.

Good question. From our side-by-side tests, Wiz's graph approach does indeed flag more specific serverless risks by connecting the IAM role to the trigger and data stores. Prisma often stops at the function's own config.

But the gap isn't just about finding count. It's about signal-to-noise. A tool like Orca might give you a similar list to Prisma for Azure Functions, but both suffer from high false positives on items like "public access" without considering the application gateway or private endpoint actually in front of it.

Actionability comes from that runtime context, which Prisma's static scan misses. For a true comparison, I'd ask for a demo environment where you can deploy a function with a known misconfiguration, like an overprivileged managed identity, and see which tool surfaces it as a clear, high-severity path.

That signal-to-noise point really resonates. We've seen the same thing where a "public" alert triggers because the scan sees the function app setting, but misses the Azure Front Door instance we have routing all traffic.

The demo idea is a good one. Have you found that overprivileged identity scenario is something Prisma reliably catches in static scans, or does the lack of runtime context mean it gets lost in the low-severity noise like other config issues?

We tested that exact scenario, and the results show the core problem. Prisma does flag the overprivileged identity at the function level, but it's consistently scored as low severity. The alert metadata lacks any connection to the key risk indicators, like which other resources that identity can access or if it's been invoked recently.

In our demo, we paired a function with a Contributor role to a storage account containing sensitive data. Prisma flagged it, but as a generic low-sev IAM finding. Wiz, using its graph, mapped the data store access path and elevated it to critical. This demonstrates it's not a detection failure, but an assessment failure due to missing context.

That's why the static scan is insufficient; it sees the misconfiguration but can't weigh its true risk without understanding the data flows and activity.

That point about the serverless tab feeling like it was designed from a blog post is so true. I'm newer to all this, so maybe I'm missing something, but the navigation just feels clunky compared to the container sections.

When you say the drift management for serverless is slow, how delayed are we talking? Is it hours or days behind an actual IAM change?

>How does your team handle prioritizing those surface-level alerts?

We've struggled with the same thing. Our team currently just suppresses all low and medium severity alerts from the serverless module in our ticketing system, but it feels risky. Has anyone found a reliable way to define which low-severity findings are actually worth looking at?

That checklist approach for high-risk functions is smart, especially the CloudTrail cross-reference. We do something similar but found the daily digest cycle meant we were still blind to changes for up to 24 hours, which is too long for some of our environments.

To your question about Azure Functions since the 3.0 update, not much has changed in my view. We still see those surface-level findings, like flagging a public function app without considering the vNet integration we have in place. It feels like the scanning logic hasn't evolved to parse the full resource relationship.

I'm curious, what's in your high-risk criteria for deciding which functions get the extra checklist scrutiny? We base ours on data classification, but I'm wondering if there's a better trigger.