What is AI Governance?
Short answer: AI governance defines what the organization may do with a model output: what is automated, what is reviewed, what evidence is retained, and who is accountable.
Definition
AI governance is the set of rules, thresholds, exceptions and records that turn a model output into an allowed action or into human review. It does not describe how the model works. It describes what the system is authorised to do once the model has spoken.
Why it matters
Without governance, each team improvises: some automate too much, others review everything, and nobody can reconstruct afterwards why an action was taken. The failure shows up when a decision has to be defended to audit, a regulator or a customer.
Governance does not replace the model. It sets the perimeter in which that model may act, and the escalation path when the case leaves that perimeter.
What it is not
It is not a maturity score or a “readiness” checklist. Those tools imply a methodology that has to be validated; Azuba does not publish them.
It is not the same as explainability. You can explain a result and still lack a policy on whether it may be automated. You can have a policy and still be unable to explain the specific case.
In the Azuba model
Explainable AI and Intelligent Decision places governance at the end of the sequence: after explaining and verifying, the organization decides whether to automate within policy or escalate to a person.
AyGLOO can occupy part of that decision and record layer over existing models. Governance is Azuba’s problem; AyGLOO is a technology inside that problem.
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Is AI governance only a legal topic?
No. It has legal and compliance consequences, but the everyday failure is operational: an automatic action nobody can reconstruct, or human review without criteria that becomes a bottleneck.
Do we need a new model in order to govern?
No. Governance applies over the models that already exist. Replacing the model does not fix the absence of rules about what to do with its output.