AI is moving from decision support to decision execution across utilities: forecasting, dispatch, asset monitoring, and incident response. When that happens, the key question shifts from “Was the model accurate?” to “Can we independently verify what the system did, when it did it, and why?”
In regulated or safety-critical operations, ordinary logs are not enough. Logs can be incomplete, overwritten, or selectively disclosed after an incident. What we need is a “flight recorder” approach for AI-enabled operations:
Tamper-evident event logs (cryptographically chained)
External time anchoring (so timelines can’t be rewritten)
Evidence that third parties can verify without trusting the operator
Minimal, privacy-preserving provenance rather than sensitive raw data
This is not about blaming AI. It’s about making automation auditable and dispute-resistant. If we want AI to earn trust in grid and utility workflows, verifiability must be designed in from day one—before the first major incident forces the change.
Curious to hear from utility operators and vendors: where do you see the biggest gap today—logging, governance, or incident reconstruction?