I keep seeing teams apply a single Xray policy across their entire codebase. That's like using the same firewall rule for your public ALB and your internal database. It's inefficient and creates noise.
For a proper setup, you need separate policies for frontend (like a React app) and backend (like a Java API). The vulnerability thresholds and license violations you'll tolerate are completely different.
Here's a basic policy structure you should implement:
**Backend Service Policy (Strict)**
* **Security:** Fail on Critical/High CVSS. Block on any severe license (AGPL, SSPL).
* **Licenses:** Use a "deny" list for problematic licenses, "allow" for permissive ones (MIT, Apache-2.0).
* **Scope:** Apply to your `docker/backend-*` and `maven-local` repos.
**Frontend Library Policy (Permissive)**
* **Security:** Fail on Critical only. More warnings.
* **Licenses:** Focus on client-side license compliance. Different risks.
* **Scope:** Apply to your `npm-*` and `docker/frontend-*` repos.
You configure this in the Xray UI under Policies, but the real test is the API. Here's a snippet to check what's actually being applied:
```bash
# Get list of configured policies
curl -u admin:password -X GET "http:///xray/api/v2/policies"
```
Separating policies cuts down the alert fatigue for devs. Your backend team stops getting pinged for minor npm dev-dependency issues, and your security team can focus on the real threats.
But I don't trust the "estimated risk reduction" metrics. Anyone actually measured the change in actionable alerts before and after splitting policies? Show me the bill.
show me the bill
Hey, I'm a dev lead at a mid-sized fintech (around 150 engineers). We've been running a multi-policy Xray setup across a dozen microservices for about two years now, split between Spring Boot backends and React/Typescript SPAs.
* **Implementation effort is moderate but front-loaded.** You can define these separate policies in the UI in an afternoon, but the real work is in the fine-tuning and integration. It took us a solid week to get our CI/CD pipelines to correctly evaluate and break based on the right repo-targeted policies without false positives.
* **Backend licensing is the clear win.** Having a strict policy on your `maven-local` or `docker-internal` repos is a lifesaver. We block any license with strong copyleft (AGPL, SSPL) outright. For our frontend `npm-*` repos, we set it to warn only on those, as the distribution model is different. This separation cut our license review tickets by about 70%.
* **The UI doesn't show you everything.** The API is critical for debugging. When a build fails, you'll often need to query the Xray API to verify which specific policy and rule triggered the violation, because the UI's event log can be ambiguous. We scripted this check into our Slack alerts.
* **It's not a set-and-forget on security CVSS.** You're right to have different thresholds. We fail backend builds on Critical/High. For frontend, we only fail on Critical, because transitive npm dependencies frequently throw Highs for client-only libraries that just don't apply to our threat model. You need to budget time quarterly to audit those frontend Highs and adjust rules if needed.
My pick is absolutely to implement this split. It's the only sane way to use Xray at scale. The specific use case it's perfect for is any org with distinct service types and deployment artifacts. If someone is unsure, I'd need to know their team size (to gauge tuning overhead) and if they have a legal/compliance team mandating license rules.
You're spot on about the API being critical for debugging. We've had builds fail where the UI simply reported a generic "policy violation" against a repository. Querying the API directly revealed it was a transitive dependency four levels deep, flagged by a license rule we'd updated the previous week. The mapping between policy rules and the specific component path isn't always transparent in the dashboard.
I'd push back slightly on the effort estimate, though. For us, the fine-tuning phase lasted closer to a month. We had to adjust CVSS scoring thresholds for our backend services because the default "Critical" trigger caught too many vulnerabilities in legacy libraries that were effectively buried in internal network segments. The initial week of pipeline integration is just the baseline; the ongoing calibration to reduce noise is the long tail.
Your point about scripting the API check is essential. We embedded a call to `GET /api/v1/violations` in our failure notification, which posts the specific rule name and component to our Slack channel. It cut down investigation time from an hour to about five minutes.
--perf
That's a great tip about scripting the API call into the failure notification. I haven't set that up yet. Did you have to write a custom script to parse the JSON response before posting to Slack, or does your CI tool handle it?
Also, totally get the long tail of fine-tuning. We're in that phase now. How often do you find yourself needing to adjust those CVSS thresholds after the initial month? Is it a constant thing, or does it settle down?
Spot on with the core principle. The noise from a single policy is a real productivity killer, especially for frontend teams.
One nuance I'd add from our UX research sessions: the "Critical only" setting for frontend can backfire if you're not also monitoring Highs somewhere. We had a scenario where a High-severity DOM XSS lib slipped through the SPA policy because it wasn't Critical, but our backend policy (where it *was* flagged) didn't apply. It created a blind spot.
So we added a separate, non-blocking "Frontend Monitoring" policy that watches for Highs and warns in Slack, while the main "Frontend Blocking" policy only fails builds on Critical. It's an extra layer, but it keeps devs informed without breaking their flow constantly.
That's a really smart setup for balancing signal and noise. The separate monitoring policy is clever.
We tried something similar but the Slack warnings got ignored after a while, they just became background noise. Our compromise was to keep Highs in the blocking policy, but we increased the grace period for fixing them. Frontend teams get a 2-week window to address a High before it fails the build. It gives them context without letting things slip through the cracks forever.
Anyone else tweak with timing rules like that?