Building practical AI governance

Short answer

AI governance defines who may use which AI for what purpose and data, how risks are assessed, results reviewed, exceptions handled and evidence retained. It should enable use while making accountability visible.

What the concept actually means

A workable model connects principles, use-case inventory, roles, risk tiers, approval, procurement, operations, training and incident management.

Why it matters in the enterprise

The EU AI Act creates risk-based obligations; applicability requires qualified case-specific assessment. This article provides methodological guidance, not legal advice.

A controlled method

The CTPM practice framework for controllable AI applications uses seven stages: understand the task, clarify context and data, apply AI deliberately, review professionally, handle deviations, approve accountably and document transfer. It is a transparent working framework, not a certification.

Define task and impact
Clarify data, context and permissions
Review against domain criteria
Control deviations, approval and evidence

CTPM practice example

CTPM practice example: An enterprise starts with ten use cases. Purpose, affected parties, data, model, impact, controls and owner are recorded for each. Only approved combinations enter pilot.

Quality and test criteria

The following criteria make quality observable for this use case:

Use cases and systems are inventoried.
Risk tier determines control depth.
Roles have genuine decision authority.
Incidents and changes trigger review.

Risks and common misconceptions

Governance fails when it consists only of a policy, treats all uses alike or diffuses accountability across a committee.

Example transfer artefact

Transfer artefact: a governance canvas with use-case inventory, roles, risk tiers, controls, evidence and review cycle.