Who Should Own EU AI Act Compliance? Legal vs GRC Guide.

The EU AI Act spans every organisational function at once: a legal obligation, a technical standard, a risk management framework and an operational discipline, covering risk classification and technical documentation before deployment, human oversight and incident reporting during operation, and post-market monitoring after a system goes live. Every stage needs input from more than one team.

Three patterns consistently fail:

1. Legal owns everything: Legal interprets the obligations correctly, but can't generate the operational evidence or sustain continuous monitoring alone, so compliance becomes a paper exercise.
2. IT or Security owns everything: Technical controls get built, but the governance framework, policy documentation and regulatory reporting stay thin, leaving the organisation able to demonstrate what it built without evidence of how it's governed.
3. A working group owns it: Cross-functional in theory, but accountability diffuses without a defined operating model, and when regulators ask who owns a specific obligation, the group needs a named individual with the authority to answer.

The real gap is accountability: EU AI Act compliance has to be demonstrated with evidence, and that requirement carries real weight under enforcement. The fix is a RACI-style operating model where each function holds a specific, bounded role, and GRC provides the framework that connects them into one coherent, auditable programme.

Legal's contribution is essential and bounded. Its primary responsibility is interpretation: translating the regulation's obligations into requirements the organisation can act on.

What Legal owns

1. Obligation mapping: Determining which provisions apply based on the organisation's role as provider, deployer, importer or distributor, and the risk classification of its AI systems.
2. Contractual requirements: Drafting and reviewing agreements with AI providers and deployers so obligations flow correctly through the supply chain.
3. Regulatory engagement: Acting as the primary contact with national competent authorities and managing formal regulatory correspondence.
4. Policy sign-off: Reviewing and approving the AI governance policy and use-case documentation to confirm they're legally defensible.

Risk assessments, technical documentation, conformity records and monitoring logs are operational outputs that need input from Risk, Security, Data and business functions. Holding them inside Legal tends to produce evidence assembled reactively, just ahead of a review. Legal defines what's required; other functions produce and maintain the evidence that demonstrates it's been done.

Risk and Compliance sits at the operational centre of EU AI Act compliance. This function is best placed to manage the governance framework, run the controls programme and produce the reporting that demonstrates ongoing compliance.

What Risk and Compliance owns

1. AI risk register: A live register of AI systems in scope, their risk classifications, associated controls and residual risk ratings.
2. Controls framework: Designing and operating the controls that address human oversight, bias monitoring, incident response and post-market surveillance for high-risk systems.
3. Compliance reporting: Producing internal reporting that gives leadership and the board visibility of compliance status, plus the external documentation regulators may request.
4. Framework alignment: Mapping EU AI Act obligations against existing frameworks such as ISO/IEC 42001:2023 to avoid duplicating effort across programmes.

Risk and Compliance has the structural mandate to manage cross-functional obligations. It's used to running frameworks that span multiple business units, coordinating evidence from different owners and reporting upward to governance bodies, exactly the operating model needed to satisfy Article 26 deployer obligations under the EU AI Act. Programme ownership here means owning the governance framework that determines what evidence is needed, who produces it and how it's maintained, while operational teams generate the evidence itself.

For organisations already running GRC programmes across DORA, NIS2 or ISO 27001, the AI Act compliance programme belongs inside that same governance structure. Separation creates duplication and gaps.

The EU AI Act sets specific technical obligations for high-risk AI systems: resilience, cybersecurity, accuracy and activity logging that ensures traceability of results. Security and IT own these controls.

What Security and IT own

1. Technical safeguards: Implementing cybersecurity and fault-tolerance controls for high-risk systems, including access controls, input validation and adversarial resilience measures.
2. Activity logging: Ensuring AI systems generate the audit logs the regulation requires, capturing inputs, outputs, decisions and human interventions in a form regulators can review.
3. Incident detection and response: Operating the monitoring capability that identifies serious incidents and malfunctions, and meeting the regulation's incident reporting obligations.
4. Infrastructure governance: Managing the technical environment AI systems run in, including data pipeline integrity and model deployment controls.

Security and IT teams are used to owning technical controls inside defined infrastructure boundaries. AI systems complicate that: a third-party AI tool deployed by a business unit can sit outside the standard IT estate, with no logging, no access controls and no incident detection in place. Shadow AI is Security's EU AI Act problem, and it needs a complete inventory of AI systems across the organisation, maintained by Risk and Compliance and informed by business units, before Security can govern what's actually in use. It also needs the Article 4 AI literacy obligation covered for every team operating those systems, including the ones sitting outside the tracked estate.

For organisations that develop or significantly configure AI systems, beyond simply deploying off-the-shelf tools, Data teams and AI product owners carry obligations no other function can discharge on their behalf.

What Data and AI product owners own

1. System-level technical documentation: Covering system architecture, training data, performance metrics and intended purpose, kept current throughout the system's lifecycle.
2. Training data governance: Ensuring datasets used to train or fine-tune models meet the regulation's requirements for quality, representativeness and freedom from bias, with governance records maintained.
3. Intended use definition: Documenting the specific context and purpose each AI system is designed for, and flagging when business deployment drifts from that intended use.
4. Model change management: Maintaining records of model updates, retraining events and performance changes that could affect risk classification or compliance status.

The technical documentation obligations need granular, system-specific detail that only the teams building and maintaining the AI system can produce. Risk and Compliance can track the obligation but needs the underlying data to verify it, and Security can audit the output once it exists. This is also where the provider/deployer distinction becomes operationally significant: an organisation that customises a third-party AI system enough to own the model's intended purpose may have assumed provider obligations, including full technical documentation requirements, regardless of the original vendor relationship. Data and product owners need to understand that boundary, and Risk and Compliance need to track it.

Where a Data Protection Officer role exists, it sits alongside Data and product owners rather than replacing them. The DPO advises on the data protection impact assessment that Article 10's bias-testing and special-category-data provisions require, and signs off the privacy dimension of AI use-case documentation. Organisations without a dedicated DPO fold that work into Legal or Risk and Compliance, but the assessment itself still needs to happen before a high-risk system reaches deployment.

GRC provides the connective tissue: the accountability structure, the evidence framework and the reporting mechanism that turns individual function contributions into one coherent compliance programme.

GRC carries that accountability across every row in the table above: it owns the framework that keeps each obligation moving, backed by evidence, all the way to the board.

What the governance structure needs

1. An AI register: A maintained, centralised record of every AI system in scope, its risk classification, the function responsible for each obligation, control status and evidence location.
2. A governance forum: A standing cross-functional body with a defined remit, regular cadence and an escalation path to the board, since AI governance now sits as a board-level risk.
3. An evidence framework: A defined approach to what evidence each obligation needs, how it's produced, where it's stored and how it's retrieved under audit.
4. Independent assurance: Internal Audit tests whether the controls Risk and Compliance designed, and Security and Data and product owners built, are actually operating as documented, on a cycle independent of the teams running them.

For organisations with existing GRC programmes, integrate the AI Act compliance programme into that same platform and operating model; separation creates duplication, inconsistency and gaps that regulators will find. GRC teams are already taking ownership of responsible AI governance in exactly this way, building the structural steps directly into their existing programme.

Demonstrating compliance under the EU AI Act runs as a continuous exercise: the regulation requires ongoing monitoring, incident records, human oversight logs and documentation reflecting each AI system's current state. Regulators want the evidence that underpins a compliance report, checked against the system in production.

Three principles for an auditable AI compliance programme:

1. Evidence has to be continuous: The most common audit failure traces back to missing evidence that controls were operating at the relevant time, more often than to the controls themselves being absent. Logging, monitoring records and oversight documentation need to generate as a by-product of normal operations.
2. The evidence trail has to be immutable: Where AI systems drive significant decisions, such as credit assessments, employment screening or access to essential services, the regulation requires that activity can be traced. Any GRC platform used to manage AI Act compliance should enforce immutable audit trails as a baseline capability.
3. Accountability has to be visible at the control level: A policy assigning responsibility to a function needs a named owner underneath it, a defined review cadence and a current status per control, so the answer to who's responsible for a specific system is retrievable in seconds.

The RACI model only delivers auditable compliance when it runs inside a system that connects obligations, controls, evidence and owners in one place; spreadsheets and shared drives can't maintain that connection at scale. SureCloud's AI Governance capabilities maintain the evidence trail, connect obligations to controls, and give compliance teams the visibility to report with confidence, with Gracie AI Agents with Personas and Skills keeping the register current as systems, owners and controls change.

If your organisation is at the policy-building stage, the guide to building an AI governance framework sets out the foundational steps before the operational programme stands up. For the complete set of EU AI Act obligations behind this operating model, see the SureCloud EU AI Act Complete Compliance Guide.

EU AI Act compliance ownership doesn't reduce to a single answer. Legal, Risk and Compliance, Security and IT, and Data and product owners each hold obligations only they can discharge, and leaving ownership undefined is the real mistake.

GRC's job is to hold that framework together: naming owners, tracking evidence and keeping the reporting line to the board live. That's the accountability structure the EU AI Act demands, and the one that holds up when regulators come looking for evidence. The organisations ahead of this built a cross-functional governance model before the enforcement deadline landed, regardless of Legal team size or how sophisticated their AI systems are.