Constrain access and rehearse rollback

Separate runtime and operator roles for containerized answer API releasing image v2 at 5%→25%→100% weights. Runtime may only perform the narrow actions that release commander watching revision-sliced error rate needs; operators get audited break-glass with TTL. Encode a negative probe that denies the privilege trick related to automation promotes while scrape_up{revision=v2}==0.

Redact prompts/PII at collection. Secrets enter via a manager or workload identity — never source, fixtures, or exception strings. Incident CANARY-PROMOTE-BLIND-6 should be impossible if these controls hold. Output allowlists and schema checks stay in force on error paths.

Rehearse the rollback or kill switch timed against a clock. Record actor, reason, prior revision/secret/flag, and verification query. Invariant reminder: promote only if canary error_rate ≤ baseline+0.5pp and groundedness ≥ baseline−1pp for two 10m windows.

Abuse cases unique to this lab include the privilege path implied by automation promotes while scrape_up{revision=v2}==0. Prove a read-only role cannot mutate. Break-glass tokens expire; leftover tokens fail the drill. Dependency pin/digest story matters when images or models move under you. Document how to rotate the credential that containerized answer API releasing image v2 at 5%→25%→100% weights uses without a full outage window longer than your dual-run plan. Security evidence is part of ship, not an appendix nobody reads.

List network egress destinations and justify each. Ensure debug endpoints are off by default in the shipping config. Verify that error responses do not echo secrets or raw stack frames to clients. For multi-tenant paths, add a cross-tenant probe fixture. Time the rollback drill twice — once with the author, once with a peer. Store the drill transcript beside the threat notes for the incident id. In this chapter the product is containerized answer API releasing image v2 at 5%→25%→100% weights, the human stakeholder is release commander watching revision-sliced error rate, and the incident id you design against is CANARY-PROMOTE-BLIND-6. Re-state the oracle in your notes — 5% canary healthy 20m → promote to 25%; guardrail breach → weight 0 in < 2m — and keep the invariant visible: promote only if canary error_rate ≤ baseline+0.5pp and groundedness ≥ baseline−1pp for two 10m windows. Track canary_weight, error_rate_by_revision, grounded_rate_by_revision as the scoreboard. Surface under change control: POST /answer. If you only have forty minutes, finish the fixture for automation promotes while scrape_up{revision=v2}==0 before polishing UI. Promotion language stays ternary: promote, hold, or roll back based on evidence, not hope.