Courses · In-house training
EN 18286: AI quality management for the EU AI Act
In-house training and workshops on connecting Article 17 quality management with responsibilities, processes, controls and evidence. Explore EN 18286 through structured information modelling and explicit rule logic.
Who this is for
Legal, compliance, quality, governance and technical teams working with providers of high-risk AI systems.
What you will work on
Identify the relevant requirements and their scope. Connect each requirement to an accountable role, an operational process and supporting evidence. Make assumptions, exceptions and review triggers explicit.
Course topics
Scope, roles and the regulatory context
QMS responsibilities and operational processes
Lifecycle controls and change management
Data governance and structured documentation
Obligations, conditions and exceptions
Evidence, traceability and review
The KROG approach
The proposed programme combines KROG data model information modelling with KROG rule logic. The focus is on making the relationship between requirements, facts, responsibilities and evidence explicit.
Practical exercise
Develop a draft traceability map for a selected use case, linking a requirement to its owner, process, evidence and review trigger.
Format
In-house training and workshops on request. Scope and exercises are agreed for the participating team.
Training does not constitute certification or a conformity assessment. Detailed work with the standard requires appropriate access to the published text.