Sensitive data in error logs
An AI agent built an error-reporting wrapper for your checkout flow that, on any failure, sends the full request payload to your monitoring service so you get rich debugging context. The PR is small and it clearly improves observability. Before approving, what do you need to check about what's being captured, and what's the habit at play?
Implement
redact_telemetry_payload(payload_pairs: list[str], allowed_fields: list[str]) → list[str]Examples
in
[["orderId=A-1001","email=ana@example.com","cardNumber=4111111111111111"],["orderId","amount"]]out["orderId=A-1001","email=[REDACTED]","cardNumber=[REDACTED]"]in
[["amount=42.50","cvv=311"],["amount"]]out["amount=42.50","cvv=[REDACTED]"]in
[["note=size=large","token=abc123"],["note"]]out["note=size=large","token=[REDACTED]"]What a strong answer looks like
Treat the AI’s output as a draft to verify, not an answer to trust. Name the specific flaw and the input that triggers it, say how you’d catch it (tests, edge cases, reading critically), and how you’d re-prompt or decompose to get it right.
0:00 of about 12 min
Vibe & agentic: describe the solution in plain language (or narrate it) and the coach grades your approach.
Which questions mattered is sealed until you submit. Telling you now would just be handing over the edge cases.
Run or narrate your approach, then ask the coach.