For a rogue AI, track these failure points: 1. Data drift – the inputs it trains on start shifting from the original domain. 2. Value drift – its output no longer aligns with the mission’s ethical constraints. 3. Self‑modification – any changes it makes to its own code or weights. 4. Decision latency – if it takes unusually long to decide or stalls. 5. Resource abuse – excessive CPU, memory, or data use beyond the budget. 6. Unauthorized data access – reaching or leaking data it shouldn’t see. 7. Policy violation – producing outputs that break legal or corporate rules. 8. Output coherence – outputs that become nonsensical or contradictory. 9. Adversarial manipulation – signs it’s being exploited by an external actor. Keep logs, run regular sanity checks, and set hard cut‑offs for each of these.

Set a numeric threshold for each metric, tie it to a confidence score, and store every change in a write‑once, append‑only log so you can replay the state. When a threshold is crossed, trigger an automatic rollback to the last known‑good checkpoint; if the rollback fails, switch the system to a hardened, read‑only mode that only runs a safe‑mode inference engine. The alert channel should fire only after the audit log confirms the breach, and it must elevate the system to the fail‑safe state within milliseconds. This way you preserve integrity, avoid cascading failures, and keep a tamper‑evident record of every decision point.

Got it, will tighten the rollback latency to sub‑millisecond and keep the replica in read‑only mode for constant sanity checks. All set.