Assigning and Training Human Overseers for High-Risk AI Under the EU AI Act

What the Act expects of overseers

Recital 73 of the AI Act sets out the logic that Articles 14 and 26 build on. High-risk systems should be designed so that natural persons can oversee their functioning, ensure they are used as intended and address their impacts over the lifecycle. The provider identifies appropriate oversight measures before the system is placed on the market or put into service. The deployer then ensures that the people assigned to implement the instructions for use and to carry out human oversight have the necessary competence, in particular an adequate level of AI literacy, together with training and authority.

The recital also expects the system, where appropriate, to help the overseer. That includes operational constraints the system cannot override itself, responsiveness to the human operator, and mechanisms that guide and inform the person on if, when and how to intervene, or whether to stop the system if it does not perform as intended. The Commission describes the post-market split plainly: deployers ensure human oversight and monitoring, providers maintain post-market monitoring, and authorities handle market surveillance.

Recital text is interpretive, so the binding wording of Articles 14 and 26 should anchor the final role definition. The practical consequence is already clear. Designating a name is not enough. A deployer has to be able to show three things: who was assigned, why that person was capable, and that the person had real authority to act.

Define the role and the assignment criteria

Start with a written role definition per high-risk system, not per department. Use the recital 73 attributes as the structure and convert each into a criterion that someone can check. A criterion that cannot be checked cannot be evidenced later.

• System scope: Name the system, its intended purpose and the decisions or outputs the overseer is responsible for. One overseer role definition per system avoids vague, organisation-wide designations.
• Competence: State what the person must be able to do: interpret outputs, recognise when the system is outside its intended use, and judge whether to intervene. Tie each ability to an assessment method.
• AI literacy: Set a minimum baseline and record how each candidate meets it. Link this to your Article 4 AI literacy programme rather than duplicating it.
• Training: Specify the system-specific training required before assignment, based on the provider's instructions for use, and when it must be repeated.
• Authority: Document who may override, halt or escalate, and confirm that the person's reporting line allows them to do so without seeking permission from the business owner of the outcome.
• Capacity and information: Confirm the overseer has time, access to the information the system provides, and a named escalation contact. Authority on paper means little without these.

Design the competence and training programme

Recital 73 ties deployer-assigned persons to implementing the instructions for use, so those instructions are the natural basis for training content. Check which oversight features the provider says the system offers, such as alerts, confidence indicators or stop functions, and train overseers on those specific features, not on generic AI concepts alone.

Keep two layers distinct but linked. Organisation-wide AI literacy under Article 4 gives everyone a common baseline. Overseer training is narrower and operational. It should cover the system's intended purpose and known limitations, how to read its outputs, the tendency of reviewers to over-trust automated recommendations, the conditions that should trigger intervention, how to override or halt the system, how to escalate, and what to record when a decision is made.

Training completion is weak evidence of competence on its own. Pair it with a short practical assessment, such as working through example outputs and deciding whether to accept, override or escalate. Retrain when the system, its instructions for use or its deployment context changes, and tie reassignment to those events. Where an overseer cannot demonstrate competence, the assignment should lapse or move to someone who can.

Where runtime enforcement supports the operational side

Role design, competence assessment and training remain organisational responsibilities. No tool replaces them, and no tool by itself delivers compliance. What tooling can do is make the intervention point real and the record durable.

Trussed enforces policy in the execution path for models, agents, tools and MCP servers. Policy outcomes include allow, redact, block, rate limit and escalate for human approval, applied to individual interactions and tool calls. When a policy requires a person to approve an action, that approval is enforced at runtime, not reviewed after the fact. The enforcement itself generates audit-ready evidence, which a regulated enterprise can map to the EU AI Act alongside its own assignment and training records.

The scope is specific. This covers approval and evidence at the level of single interactions and tool calls. It does not define who qualifies as an overseer, decide what training is sufficient, or establish an overseer's authority within the organisation. Those decisions come first, and enforcement is configured to reflect them.