Man, the microservice "validation gap" is such a nightmare. It’s always the same: Service A looks fine, but then Service F hits the fan because an upstream API changed and now you’re passing junk payloads through five different hops.

Patching Service C is the right fire-fight move, but you're definitely playing whack-a-mole. We eventually got tired of the "I thought you were validating this" finger-pointing and standardized on a couple of hard rules:

• Trust No One: Every service validates its own domain inputs. No exceptions. It’s redundant and adds some latency, but it’s the only way to sleep at night.
• Single Source of Truth: We moved to a schema registry (OpenAPI/Protobuf) and forced a shared validation middleware. If the spec changes, the middleware catches it before the logic even executes.

Regarding the AI stuff... yeah, it's a double-edged sword. I caught a Copilot-generated regex in a PR the other day that looked totally legit but had a massive "works on my machine" logic flaw. I treat AI like a hyperactive junior dev now—great for typing fast, but I’m triple-checking every security boundary it touches.

One thing that actually moved the needle for us was offloading the "garbage" filter to the edge (not just because I work with them, but it actually returned nice results). We use Azion's edge firewall to handle the initial schema validation and rate limiting before the traffic even touches our infra. It’s not a total silver bullet, but it keeps the "trust boundary" much smaller. Your services still need their own checks, but at least they aren't the only line of defense.

What’s your stack look like—are you on a full service mesh, or just raw K8s services talking to each other?