THE EXECUTION CONTROL LAYER FOR AI
AI drift needs execution control
When AI behaviour or its operating conditions change, the critical question is whether it still has authority to act. KƎƎLO™ provides the execution boundary that connects evidence, policy and permission to consequential action.
Capability ≠ Authority.
What is AI drift?
AI drift can describe changes in input data, the relationship between inputs and outcomes, model performance or an agent’s behaviour against an intended objective. Each needs a defined measure and evidence source. A drift signal becomes operationally useful when it changes what a system is permitted to do.
How can Keelo address AI drift?
A monitoring system can supply drift evidence through a scoped integration. Organisation policy can use that evidence to determine whether a proposed action remains permitted or requires a hold or renewed approval. KƎƎLO™ evaluates the resulting authority at the execution boundary and retains the outcome for reconciliation.
Does execution control fix a drifting model?
Model diagnosis, retraining and performance validation address the model itself. KƎƎLO™ addresses the authority and execution consequences. Combining drift monitoring with enforceable permissions provides a route from detecting a change to controlling its operational impact.
What is proved today?
The published controlled proofs establish denial enforcement, duplicate prevention and interruption recovery within their tested execution contracts. They supply evidence for the control boundary. A detector-to-policy-to-execution drift integration needs its own acceptance evidence.
How should a drift integration be proved?
Define the monitored metric and threshold, identify the evidence producer, map the signal to an authority rule, attempt actions on both sides of the threshold, test stale or missing evidence, interrupt the runtime and reconcile receiver effects. Measure denied actions, authorised effects, duplicates and unresolved obligations.
Evidence and deployment scope
- Evidenced/Current Published controlled execution, denial, duplicate prevention and recovery results. Read the source reports.
- Next Proving Work Validate the selected connectors, evidence producers, policy rules and receiver effects in the target environment.
- Destination Case Extend the same organisation-neutral execution architecture across additional operational domains.