Governance Roles and Committees: Who Owns Decisions

Effective governance for high-risk systems is not a meeting schedule; it is a decision architecture. In the European regulatory landscape, the question “who owns the decision?” must be answerable in writing for every critical step, from design to decommissioning. When ownership is diffuse, accountability evaporates, and compliance becomes a collection of signatures rather than a defensible process. This article explains how to design governance structures that work in practice, distinguishing between roles that decide, roles that review, and roles that execute, while aligning these structures with the expectations embedded in the EU AI Act, the GDPR, the NIS2 Directive, the Cyber Resilience Act (CRA), and the Machinery Products Regulation, as well as with national supervisory practices.

At the heart of this alignment is a simple principle: decisions must have a single, named owner who is accountable for the outcome and empowered to act, surrounded by committees that frame choices and reviewers that provide evidence. This principle is not merely organizational hygiene; it is a regulatory necessity. The EU AI Act, for example, requires that high-risk AI systems undergo a conformity assessment and that a Quality Management System (QMS) is in place. It also mandates that the provider establishes a post-market monitoring system and keeps technical documentation available to authorities. None of these obligations can be met if it is unclear who decides on risk acceptance, who authorizes a change, or who triggers a corrective action.

Decision Ownership as a Regulatory Requirement

European legislation increasingly frames governance as a set of accountable functions rather than a vague commitment to best efforts. The EU AI Act explicitly requires the identification of a person or body within the provider’s organization who has the authority to ensure conformity and sign the EU declaration of conformity. While the Act allows this function to be assigned to an authorized representative for certain providers, the underlying point is the same: there must be a locus of responsibility. In practice, this means that the role of “Decision Owner” must be defined for each class of decisions: design changes, risk acceptance, data strategy, cybersecurity posture, clinical evaluation (where applicable), and market withdrawal.

Ownership is not the same as being the busiest person. An owner is the individual who can say “yes” or “no” with binding effect, and who can allocate resources to implement the decision. Ownership should be documented in a RACI matrix (Responsible, Accountable, Consulted, Informed), but with a strong preference for a single accountable party per decision type. In complex organizations, it is common to see “shared ownership” of cybersecurity or data protection. This is a governance smell. While multiple functions must be consulted, the decision on risk acceptance must be owned by a named role, typically the Chief Information Security Officer (CISO) or a designated Risk Committee chair, with escalation to the board if the residual risk exceeds appetite.

From Principles to Practice: The Decision Log

Regulators do not prescribe a specific tool, but they do expect traceability. A Decision Log is the minimal artifact that demonstrates who decided what, when, on the basis of which evidence, and with which constraints. For high-risk AI systems, this log becomes part of the audit trail that supports the conformity assessment and the technical documentation. It should capture:

• Decision identifier and date;
• Owner name and role;
• Question addressed and options considered;
• Relevant evidence (risk assessments, test results, expert opinions);
• Criteria for the decision (e.g., risk acceptance thresholds);
• Outcome and implementation plan;
• Review and escalation notes.

In practice, the Decision Log should be integrated with change management and incident response processes. If a safety-critical parameter in a robotic system is changed, the decision to approve that change must be logged, and the log must link to verification and validation evidence. If the change is later implicated in a safety incident, the Decision Log becomes a key document for supervisory review and potential liability analysis.

Ownership and Liability Under the AI Act

The EU AI Act introduces a risk-based framework with obligations that vary by role: provider, deployer, importer, distributor. The provider of a high-risk AI system bears the most extensive obligations, including risk management, data governance, technical documentation, record-keeping, transparency, human oversight, and cybersecurity. The Act also requires that the provider establish a QMS and conduct a conformity assessment, either internally (under certain conditions) or via a notified body. In this context, the owner of the decision to place a system on the market is the provider’s authorized signatory. However, operational decisions—such as whether a model update constitutes a substantial modification—also require a designated owner.

It is important to distinguish between the legal owner (the provider) and the operational owner (the product manager or system architect). In practice, the provider’s governance structure must ensure that operational owners can escalate decisions that affect compliance to the legal owner. This is particularly relevant for SMEs, where the same individual may wear multiple hats. Even in SMEs, the roles must be named and documented. A regulator will ask: “Who decided that the training data was sufficiently representative?” If the answer is “the team,” the response will be deemed insufficient.

Committees: Framing, Not Owning

Committees are often misused as decision-makers. In well-designed governance, committees are decision frames: they synthesize information, define options, and recommend decisions. They do not replace the owner. A committee can be a forum for challenge and escalation, but the final decision must be traceable to a single accountable person. This distinction is critical for regulatory defensibility because it clarifies who is responsible if things go wrong.

Core Committees for High-Risk Systems

While committees should be tailored to the organization’s size and risk profile, a minimal set for organizations deploying or developing high-risk AI and robotics includes:

• Risk & Compliance Committee (RCC): frames risk appetite, reviews residual risk, and recommends acceptance or mitigation. It does not accept risk on behalf of the business; it advises the decision owner.
• Technical & Safety Committee (TSC): reviews design changes, verification and validation (V&V) plans, and safety cases. It may recommend go/no-go for release.
• Data & AI Ethics Committee (DAEC): evaluates data provenance, bias, and societal impact. It should include domain experts and, where appropriate, external stakeholders.
• Cybersecurity Steering Committee (CSSC): oversees threat modeling, incident response, and alignment with NIS2 and CRA obligations.
• Post-Market Surveillance Committee (PMSC): monitors real-world performance, adverse events, and corrective actions, ensuring alignment with the AI Act’s PMS requirements.

Each committee should have a charter that defines its scope, membership, decision thresholds, and escalation paths. The charter must be explicit about whether the committee’s outputs are recommendations or binding decisions. If a committee is given binding authority, then that committee becomes the decision owner for specific items, and its members become jointly accountable. This is acceptable for certain operational decisions (e.g., patch prioritization) but should be avoided for strategic risk acceptance, which should remain with a named executive.

Committee Composition and Independence

Regulators value independence of judgment. For example, the GDPR’s accountability principle implies that data protection oversight should not be solely controlled by commercial functions. In the AI context, the Human Oversight requirement is more credible if the DAEC includes individuals who are not directly incentivized by commercial outcomes. In national practice, some European countries encourage or require worker participation in oversight of automated decision-making, particularly in the public sector. While the AI Act does not mandate worker representation, including such voices can strengthen the defensibility of oversight decisions.

Committees should also reflect the multi-stakeholder nature of high-risk systems. For medical devices, clinical expertise is essential; for industrial robotics, safety engineering; for HR systems, labor law and ethics. The composition should be documented, and conflicts of interest should be declared. In the event of a supervisory review, the regulator may examine whether the committee’s membership was adequate for the risks at hand.

Meeting Cadence and Evidence

Committees should meet on a cadence tied to the system’s lifecycle. During development, the TSC may meet weekly; post-market, the PMSC may meet quarterly. The output of each meeting should be a concise record that includes:

• Agenda items linked to specific risks or changes;
• Evidence presented (e.g., test metrics, incident reports);
• Recommendations and the rationale;
• Assigned actions and owners;
• Escalation notes.

These records are not ceremonial. They are part of the technical documentation and QMS records that authorities may request. Incomplete or inconsistent minutes can signal weak governance and invite deeper scrutiny.

Reviewers and Auditors: The Evidence Layer

Reviewers provide independent checks that the evidence supports the decision. They are not decision owners, but their outputs are often prerequisites for a decision. In practice, three types of reviewers are essential: technical reviewers (e.g., V&V engineers), compliance reviewers (e.g., legal and regulatory affairs), and independent auditors (internal or external). The AI Act explicitly contemplates third-party conformity assessments for certain high-risk systems, which introduces an external reviewer with statutory authority to certify compliance.

Technical Reviewers

Technical reviewers validate that the system meets its specifications and safety requirements. For AI systems, this includes evaluating model performance, robustness, and explainability. For robotics, it includes functional safety audits against standards such as ISO 13849 or IEC 62061. The reviewer’s sign-off should be conditional on the resolution of identified issues. A “conditional approval” should trigger a documented remediation plan with deadlines and re-verification steps.

Compliance Reviewers

Compliance reviewers map system features to regulatory obligations. They ensure that the technical documentation includes the required elements, that the risk management file is up to date, and that transparency obligations (e.g., user information) are met. They also verify that data processing complies with GDPR principles, including lawfulness, fairness, purpose limitation, and data minimization. In cross-border deployments, compliance reviewers must reconcile EU-level obligations with national implementations, such as sector-specific rules on automated decision-making in employment.

Independent Auditors

Internal audit functions can provide continuous assurance, while external auditors may be required for certification (e.g., under the AI Act for certain high-risk classes) or for specific standards (e.g., ISO/IEC 27001 for information security). Auditors should have access to all relevant records and the authority to escalate findings directly to the board or the RCC. Their reports should feed into the Decision Log and inform risk acceptance decisions.

Escalation Paths: From Signals to Binding Decisions

Escalation is the safety valve of governance. When a risk exceeds predefined thresholds or an incident occurs, the system must route the issue to a decision owner with sufficient authority. A well-designed escalation path is not an ad hoc phone call; it is a documented process with triggers, timelines, and communication protocols.

Triggers for Escalation

Common triggers include:

• Residual risk exceeding the organization’s risk appetite;
• Incidents with potential safety or fundamental rights impacts;
• Non-conformities identified during V&V or audit;
• Regulatory changes that affect product compliance;
• Supply chain disruptions that impact cybersecurity or safety;
• Post-market signals indicating performance degradation or bias drift.

Each trigger should have a defined severity level and a response timeline. For example, a “critical” cybersecurity vulnerability under NIS2 may require escalation within 24 hours to the CSSC and the CISO, with a binding decision on patch deployment within 72 hours. The AI Act’s obligation to report serious incidents to authorities within 15 days of becoming aware also necessitates an escalation path that ensures timely detection and decision-making.

Escalation Matrix and Timelines

An escalation matrix specifies who is informed at each severity level and within what timeframe. It distinguishes between information, consultation, and decision. For instance, a medium-severity bias finding might be escalated to the DAEC for consultation within one week, while a high-severity finding might require an immediate decision by the Chief Product Owner within 48 hours. The matrix should be integrated into incident management tools and change control systems to avoid manual routing errors.

Timelines should be realistic but aligned with regulatory expectations. Under the GDPR, a personal data breach must be reported to the supervisory authority within 72 hours of awareness. Under the AI Act, serious incidents for high-risk AI systems must be reported within 15 days. These are not just reporting obligations; they imply that internal escalation and decision-making must occur within shorter windows to allow time for investigation and drafting. Therefore, the escalation path must include pre-approved templates and roles for incident assessment.

Escalation in Public Sector Contexts

Public sector deployers often face additional layers of oversight, such as data protection officers (DPOs), internal audit committees, and ministerial accountability. In some countries, deploying automated decision systems in welfare or justice contexts may require prior consultation with oversight bodies or even legislative approval. The governance structure should reflect these constraints, ensuring that the decision owner can navigate both technical and political risk. It is advisable to establish a cross-functional “crisis cell” for high-impact decisions, combining legal, technical, and communications expertise.

Distinguishing EU-Level and National Implementation

While the AI Act and GDPR are directly applicable at the EU level, their enforcement and certain interpretive aspects are shaped by national authorities. The AI Act establishes a European AI Office and a European AI Board to coordinate, but supervisory tasks for providers and deployers are distributed among national market surveillance authorities. This means that governance structures must be able to respond to both EU-level harmonized requirements and local supervisory practices.

Enforcement Variation Across Member States

Some Member States have established dedicated AI oversight bodies, while others integrate AI supervision into existing market surveillance or data protection authorities. For example, a provider may find that one national authority requests detailed documentation on model training data, while another focuses on risk management processes and post-market monitoring plans. Governance committees should maintain a “regulatory map” that identifies the relevant authorities for each product and jurisdiction, and the decision owner should be empowered to adapt documentation and reporting accordingly.

In the area of automated decision-making in employment, national implementations of GDPR Article 22 vary. Some countries impose stricter conditions or require prior consultation with the DPO or works councils. A deployer using an AI-based recruitment tool must therefore have a governance process that includes these stakeholders before the system is used. The DAEC should be the forum where such decisions are framed, but the ultimate decision owner (e.g., the HR Director) must accept responsibility and document the rationale.

Interaction with Sector-Specific Regulations

High-risk systems often sit at the intersection of multiple regimes. A medical device using AI is subject to the AI Act, the Medical Devices Regulation (MDR), and the GDPR. A robotic system may fall under the Machinery Products Regulation and the AI Act. Governance structures must ensure that committees and reviewers are competent across these frameworks. For example, the TSC should include expertise in both functional safety and AI performance, and the compliance reviewer should be able to map obligations across regimes to avoid duplication or gaps.

Where a notified body is required (e.g., for certain high-risk AI systems or medical devices), the governance process must include a formal interface with the notified body. The decision to submit for certification should be owned by a senior role, supported by a readiness review conducted by compliance and technical reviewers. The outcome of the notified body’s assessment should feed directly into the Decision Log and, if necessary, trigger escalation to the board.

Comparative Approaches Across European Countries

Despite harmonization, practical governance can differ by national context. In Germany, for instance, the tradition of works council participation in decisions affecting workers’ conditions can influence AI governance in employment contexts. Organizations should anticipate that works councils may request information and consultation on the deployment of automated systems, and governance structures should accommodate this within the decision timeline. In France, the CNIL has been active in setting guidelines on AI and data protection, and organizations may find that compliance reviewers need to align internal policies with CNIL’s interpretations. In the Netherlands, the Dutch Data Protection Authority has emphasized the importance of transparency and human oversight, which should be reflected in the DAEC’s charter and the Decision Log.

For providers based in smaller Member States, it may be prudent to design governance that meets the expectations of the most stringent authorities, as this provides a robust baseline. A practical approach is to maintain a “regulatory horizon scan” within the RCC, updating governance processes as national guidance evolves. This ensures that the organization’s decision-making remains defensible across markets.

Practical Governance Design: From Roles to Workflows

Translating these principles into practice requires a clear mapping of roles, committees, and workflows. The following steps provide a pragmatic path:

• Identify decision classes: List the critical decisions required by the AI Act, GDPR, NIS2, CRA, and sector-specific rules. Examples: data governance policy approval, risk acceptance, model release, incident response, PMS plan update.
• Assign owners: For each decision class, name a single accountable individual (or role). Document this in a RACI matrix and in the QMS.
• Define committees and charters: Establish committees with clear