How to Set Up an AI Governance Framework for EU AI Act Compliance

Most companies approaching EU AI Act compliance focus on the visible deliverables: documentation, risk assessments, conformity assessments. But these are outputs. What produces them reliably — and keeps them current as your systems evolve — is governance. Without an AI governance framework, compliance is a one-time sprint that decays immediately.

Why you need AI governance

The EU AI Act does not use the phrase "AI governance framework," but it effectively mandates one through Article 17, which requires high-risk AI system providers to implement a quality management system. That QMS must cover:

• A compliance strategy and procedures for conformity assessment
• Techniques, procedures, and systematic actions for system design, development, and testing
• Procedures for data management, including acquisition, collection, analysis, and labelling
• Risk management procedures (Article 9)
• Post-market monitoring (Article 72)
• Procedures for incident reporting
• Procedures for communication with authorities, users, and other stakeholders
• Resource management and accountability

That list is a governance framework by any other name. Building it explicitly — rather than bolting it together when a regulator asks — is the practical approach.

Core components of an AI governance framework

A practical AI governance framework for EU AI Act compliance needs five components:

• AI system inventory. A central register of every AI system your organisation builds, deploys, or procures. For each system: purpose, risk classification, data sources, responsible owner, compliance status. ActReady's System Register automates this.
• Policy framework. Written policies covering: acceptable AI use, data governance for AI, model development standards, testing requirements, deployment approval gates, and incident response. These don't need to be long — they need to be specific and enforceable.
• Review and approval process. A defined gate before any AI system goes to market or into service. At minimum: risk classification confirmed, documentation complete, testing passed, human oversight controls verified.
• Monitoring and reporting. Ongoing monitoring of deployed systems with defined KPIs, alert thresholds, and reporting cadence. Post-market monitoring is a legal requirement, not a best practice.
• Change management. Procedures for what happens when an AI system changes — new training data, model updates, expanded use cases, new geographies. Any change could affect compliance status.

Roles and responsibilities

You do not need a large governance team. For most SMBs and mid-market companies, three roles are sufficient:

• AI Compliance Lead. One person with overall accountability for EU AI Act compliance. They own the governance framework, coordinate across teams, and are the point of contact for regulators. In smaller companies, this is often the CTO, Head of Product, or General Counsel. The key requirement is that they have authority to block a deployment if compliance is not met.
• System Owners. Each AI system has a designated owner — typically the product manager or engineering lead responsible for that system. They are accountable for maintaining documentation, completing obligations, and ensuring the system operates as described in its technical file. They report compliance status to the AI Compliance Lead.
• Review Board. A lightweight, cross-functional group that reviews AI systems at key milestones: initial classification, pre-deployment, significant updates, and incidents. Membership: AI Compliance Lead, relevant System Owner, a technical reviewer, and (for high-risk systems) a legal/compliance reviewer. Quarterly meetings are sufficient for most organisations, with ad-hoc reviews for urgent matters.

Key governance processes

AI system onboarding

When a new AI system is proposed, built, or procured:

• Register it in the AI system inventory
• Classify its risk level
• Assign a System Owner
• Determine applicable obligations
• Create a compliance timeline

Pre-deployment review

Before any AI system goes live or receives a significant update:

• Technical documentation complete and reviewed
• Risk assessment current and mitigations in place
• Testing results documented and meeting thresholds
• Human oversight controls verified and operational
• Transparency disclosures in place
• Review Board sign-off recorded

Incident response

When something goes wrong:

• Defined escalation path from System Owner to AI Compliance Lead
• Criteria for what constitutes a "serious incident" requiring regulatory notification
• Root cause analysis procedure
• Documentation of incident, response, and corrective actions
• Update to risk assessment if the incident reveals a previously unidentified risk

Getting started in 4 weeks

• Week 1: Appoint an AI Compliance Lead. Inventory all AI systems. Classify each system using the free classifier.
• Week 2: Assign System Owners. For each high-risk system, create a compliance action plan with deadlines. Start generating documentation using ActReady's document generator.
• Week 3: Draft your AI governance policy. Define the pre-deployment review process. Set up the Review Board with an initial membership list and meeting cadence.
• Week 4: Conduct the first Review Board meeting. Review the inventory, classification results, and compliance status of each system. Identify gaps and assign corrective actions.

You now have a functioning governance framework. It does not need to be perfect — it needs to exist, be documented, and be actively used. Refine it as you work through compliance for each system. The transparency deadline is 73 days away. The high-risk deadline is 560 days away. A governance framework that starts today gives you structure to meet both.