AI governance and the EU AI Act: Practical preparation

Good AI governance does not begin with a long policy document. It begins with questions that can be answered: Which AI is used for which purpose? Which data does it access? Who may approve results? And how can the organization later understand what happened?

The EU AI Act follows a risk-based approach. The obligations depend on the specific use case. A broad claim that a product is “AI Act compliant” without assessing that use case is therefore not meaningful. Organizations can, however, establish technical and organizational foundations that support responsible use.

Five practical building blocks

1. Document use cases: Purpose, users, data sources and expected results should be described in plain language.

2. Define responsibilities: Operations, subject-matter review, privacy and approvals need clear owners.

3. Make data flows visible: Especially when external models are involved, users must know which information leaves the organization.

4. Keep results traceable: Sources, relevant context and consequential actions should be recorded.

5. Build in human control: The greater the potential impact, the more important approvals and safe interruption points become.

Governance must be part of the product

Policies alone are insufficient if software cannot enforce them. PaulinAI uses roles, source-level permissions, transparent data release before external calls, audit logs and human-in-the-loop steps. Read-only connections are the safe default; write operations and external actions can be tied to approval rules.

The automatic sensitive-data safeguard goes one step further: when a protected source enters a conversation, external AI and web search can be disabled for the entire conversation so confidential context remains local.

Technical controls do not replace an individual legal assessment. They create the evidence and control points that privacy officers, management and specialist teams need to evaluate a concrete AI deployment.