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What does AI governance mean for enterprise workflows?
Governance is often discussed as a policy document. In running workflows it is a set of controls that either exist in the software or do not.
What governance means here
For an enterprise workflow, AI governance means being able to answer four questions about every action software took on the company's behalf: what it was allowed to do, what it actually did, who approved it, and how you would prove that to an auditor. If those answers require someone to reconstruct events from screenshots and logs after the fact, the workflow is not governed.
The four practical parts
Scoped permissions
An agent acts within a defined boundary of systems, data, and value limits, not with a general-purpose credential.
Human approval where it matters
Defined points where a person decides, with the role and the policy context captured alongside the decision.
An auditable record
A queryable trail of actions, inputs, outputs, and artifacts, available without a forensic exercise.
Versioned process
The rules that were in force when an action ran are recoverable later, even after the process has changed.
What it is not
Governance is not a review board that approves AI projects, and it is not a model policy pinned to an intranet page. Those help, but they do not constrain a running agent. The controls have to sit in the execution path, applied while the work happens.
How VeroTX handles it
In VeroTX, the rules live in the WorkStream Playbook: the stages, the policy checks, and the points where a person approves or overrides. Every action is written to the Execution Ledger, tied to the WorkStream version that was active when it ran, so what happened, who approved it, and under what authority are all answerable from one place.