AI governance has a blind spot: what happens after a “valid” decision goes wrong?
Most AI governance frameworks answer one question really well:
“Was this decision allowed?”
But in high-consequence environments like healthcare, that’s not the hardest question.
The harder question is:
“What happens next if that decision was valid… but still wrong?”
There’s a growing split between Integrity (proving a record is tamper-evident) and Correctness (proving the outcome was right).
Most systems are getting very good at integrity — audit trails, hash chains, traceability.
And at runtime, they focus on admissibility — checking whether an action is allowed at a decision surface.
But that still leaves a gap.
If a system drifts from its governing intent, should refusal just be a log entry?
Or should it become a constraint on what the system is allowed to do next?
In other words:
Is governance just evaluating actions…
or actually shaping continuation?
Because when the cost of a “wrong but authorized” decision is high, we can’t rely on the system to “improve next time.”
Curious how others are thinking about this boundary — especially in agentic systems where the path isn’t predefine