AI governance is the organisational infrastructure that ensures an organisation's use and development of AI systems is consistent with its legal obligations, its risk appetite, and its values.
It is not a compliance checklist or a one-off documentation exercise: it is a system of policies, procedures, accountability structures, and technical controls that functions continuously, adapts as the organisation's AI use changes, and produces a documented record that can withstand regulatory scrutiny.
The EU AI Act makes AI governance legally mandatory for organisations that provide or deploy high-risk AI systems. The Act requires risk management systems, technical documentation, human oversight mechanisms, post-market monitoring, and incident reporting, all of which are governance functions, not merely technical features. But good AI governance extends beyond the Act's minimum requirements. An organisation that builds its governance framework solely around the Act's letter misses the broader benefits: better quality AI outputs, reduced operational risk, more defensible decisions, and a stronger position in negotiations with AI vendors and regulators.
A functional AI governance framework has five core components. First, an AI inventory: a documented register of all AI systems used or developed within the organisation, covering the system's purpose, the data it processes, the decisions it influences, the risk tier applicable under the AI Act, and the provider/deployer role for each system. Without visibility of the full AI portfolio, governance is impossible.
Second, risk classification and impact assessment: for each AI system in the inventory, a documented assessment of its risk tier under the AI Act, any applicable GDPR DPIA obligations, and, for high-risk systems, the Fundamental Rights Impact Assessment required by the AI Act for certain deployers. These assessments must be documented, reviewed at defined intervals, and updated whenever the system or its use changes materially.
Third, accountability structures: defined roles and responsibilities for AI oversight within the organisation. The AI Act implicitly requires someone to be accountable for each AI system's compliance, and this should be formalised. The AI literacy obligation under Article 4 requires organisations to ensure that staff working with AI have sufficient understanding of the systems they use and the risks involved. Governance frameworks should define who is responsible for AI Act compliance, who monitors system performance, and who makes decisions about deploying, modifying, or suspending AI systems.
Fourth, operational controls: the technical and procedural controls that implement the governance policies in practice. For high-risk AI systems, this includes human oversight mechanisms designed into the system's workflow, logging and audit trail functionality, incident detection and reporting procedures, and performance monitoring against defined metrics. For GPAI models used within the organisation, this includes copyright compliance policies for training data use and procedures for handling content generated by these models.
Fifth, vendor management: contracts with AI vendors and service providers must address the AI Act's division of responsibilities between providers and deployers. This includes requirements for technical documentation, instructions for use, support for conformity assessments, notification of significant system changes, and post-market monitoring support. Vendor contracts entered into before the AI Act was adopted should be reviewed and updated.
Organisations that are building AI systems from the ground up have the advantage of being able to design governance into the system architecture and development process rather than retrofitting it. The AI Act's requirements for technical documentation, logging, and human oversight are significantly easier and cheaper to implement during system design than after deployment. Organisations that acquired AI systems before the Act was adopted, or that built systems without the Act in mind, face a more complex retrofitting exercise that may require changes to system architecture, operational procedures, and vendor contracts.
The retrofitting challenge is amplified for organisations with large numbers of AI systems in use. An organisation that deployed dozens of AI tools over the past five years, across HR, finance, customer service, fraud detection, and operational management, may be simultaneously running multiple Annex III systems (requiring full conformity assessment and registration), limited-risk systems (requiring transparency disclosures), and minimal-risk systems (no mandatory obligations). Managing this portfolio without a systematic governance framework creates gaps and inconsistencies that are difficult to detect before they become enforcement problems.
For high-risk AI systems, the AI Act requires a risk management system that operates continuously throughout the system's lifecycle, quality management procedures, technical documentation, logging capabilities, transparency and instructions for use, human oversight mechanisms, and post-market monitoring. For deployers specifically: implementation of provider instructions, fundamental rights impact assessment in certain contexts, worker notification in employment contexts, human oversight, incident reporting, and performance monitoring. These requirements define the minimum; an effective governance framework should go beyond the minimum to address the organisation's full AI risk profile.
Article 4 of the AI Act requires providers and deployers to take measures to ensure adequate AI literacy among their staff and any other persons dealing with the operation or use of AI systems on their behalf. AI literacy is not a background desideratum; it is a legal obligation. This includes technical literacy for those operating AI systems, legal literacy for those responsible for compliance, and management literacy for decision-makers who rely on AI outputs. Governance frameworks should include training programmes, competency requirements, and documentation of the literacy measures taken.
AI governance should be integrated with, not separate from, existing risk management, compliance, legal, and data protection functions. For many organisations, AI governance is a new responsibility that sits at the intersection of IT, legal, HR, and operations. The DPO (where one is appointed) has a natural role in AI governance given the GDPR-AI Act overlap; the Chief Risk Officer or equivalent has a role in AI risk assessment; legal counsel has a role in contractual and regulatory compliance. A governance framework that operates in isolation from these functions will be less effective and will duplicate work that those functions already perform.
Providers and deployers both have obligations to suspend a high-risk AI system if they identify a risk that the system poses to health, safety, or fundamental rights, or if a serious incident occurs. Governance frameworks should define the thresholds that trigger a suspension review, the authority levels required to make a suspension decision, the procedures for notifying the relevant market surveillance authority and the system's provider, and the conditions under which a suspended system can be redeployed. Suspension decisions should be documented and treated as part of the post-market monitoring record.
%402x.svg){kind=icon}
%402x.svg){kind=small}
%402x.svg){kind=icon}
