Hello everyone,
I've been quietly observing the discussions around cloud security platforms for a while, and I wanted to dive into a specific use case that's becoming critical for my team and, I suspect, for many others. We're in the process of scaling our serverless architecture—primarily on AWS Lambda with API Gateway and EventBridge—and the shift-left security model Prisma Cloud promotes is incredibly appealing in theory. However, moving from a proof-of-concept in a sandbox to a full production deployment always reveals the real nuances.
I'm hoping to hear from others who are actively running Prisma Cloud to secure live serverless workloads. I have a few specific areas I'm trying to navigate, and I'd love to compare notes:
* **Deployment & CI/CD Friction:** How have you integrated the scanning into your deployment pipelines? We use a combination of GitHub Actions and Terraform Cloud. Did you find the serverless scanning to be a true "gate" that failed builds effectively, or did you have to write a lot of custom logic to make the results actionable? Any performance hit on pipeline duration that became a concern for developers?
* **Alert Fatigue vs. True Positives:** With serverless, the configuration surface area is different than VMs or containers. Are the out-of-the-box policies for functions (like IAM role permissions, network exposure, encrypted environment variables) well-tuned, or did you find yourself spending weeks fine-tuning and suppressing alerts to get a stable signal? A concrete example: how did it handle alerts for public-facing Lambda functions that are *intentionally* public as part of an API?
* **Operational Overhead:** Once deployed, what does the day-to-day look like? Does the platform provide clear enough guidance for remediation that you can delegate findings to development teams, or does it require a dedicated cloud security person to triage? I'm particularly interested in how it handles the ephemeral nature of functions—is the historical data and context useful?
Our ultimate goal is a seamless workflow where security insights are automatically part of the development and deployment cycle without becoming a bottleneck. We've had good experiences with other tools for CRM and marketing automation data syncs by carefully connecting APIs, and I'm applying the same "don't break the workflow" philosophy here.
If you've gone through this journey, what were your biggest hurdles and most valuable wins? Any pitfalls in the pricing model to watch for as we scale our function count?
Warmly,
~Jane
Stay connected
We're looking into this too. I'm curious about the same CI/CD point. How do teams handle it when a scan flags a low-severity issue in a hotfix pipeline? Do you let it through and track it later, or is it a hard block?
The alert fatigue question is key. In our PoC, the volume from serverless was much higher than for our containers. It made the signal hard to find.
Great question about CI/CD friction, it's the exact stumbling block that turned our initial rollout into a months-long tuning exercise. Integrating the scanning into our GitHub Actions workflows was straightforward with their CLI, but making it a truly effective gate required a lot more than out-of-box config.
We ended up implementing a two-stage policy for our serverless functions. For high/critical severity findings tied directly to runtime permissions (think: Lambda with wildcard actions on IAM policies), we made it a hard build failure. But for lower-severity issues, particularly in dependencies during a hotfix pipeline, we built a custom step that logs the finding as a warning, creates a tracked issue in Jira automatically, and lets the build proceed. The key was tying that Jira issue back to the specific function ARN and deployment version so it's not lost.
The performance hit was noticeable initially, adding a consistent 90-120 seconds to our serverless deploy pipelines. The big time sink wasn't the scan itself, but the posture evaluation against all our cloud resources. We had to get surgical with the scope, limiting the IaC scan to only the resources defined in the PR's changed modules. Did you face similar pipeline slowdowns, and if so, how did you adjust?
—Jen