Overview
This two-day course introduces Responsible Artificial Intelligence (RAI) and provides a practical understanding of ISO/IEC 42001:2023, EN 18286:2026, and the EU AI Act. Participants learn how to establish and operate an AI management system, address quality-management requirements for high-risk AI systems, and prepare for conformity assessment under the EU AI Act.
The course examines how ISO/IEC 42001 provides a foundation for organizational AI governance and how EN 18286 complements that foundation with a regulatory quality-management framework for high-risk AI systems. Participants explore AI policies, objectives, risk and impact assessments, lifecycle controls, documentation, and post-market processes, including how these can be integrated into existing organizational management systems.
Participants apply these concepts through practical exercises and an automotive case study involving pedestrian detection in automated driving systems (ADS). Although this scenario provides continuity throughout the training, the principles and processes can be applied across industries and AI applications, including organizations developing, providing, or deploying AI systems in the European market.
Intended Audience
This course is intended for professionals involved in developing, governing, evaluating, or overseeing AI systems. It is particularly relevant for:
- Executives and decision-makers establishing organizational AI strategies, principles, or policies
- AI system providers, developers, and deployers
- AI governance, risk, compliance, legal, regulatory, and quality professionals
- Systems, software, data, and machine-learning engineers developing AI-enabled products and services
- Product managers and engineering leaders responsible for AI system development or deployment
- Safety, cybersecurity, privacy, and assurance professionals evaluating AI-related risks
- Organizations implementing or evaluating an AI management system aligned with ISO/IEC 42001
- Professionals addressing quality-management requirements for high-risk AI systems or preparing for EU AI Act conformity assessment
- Companies integrating AI into regulated products and services across automotive, industrial, medical, financial, and public-sector applications
Objectives
By the end of this course, participants will be able to:
- Interpret the requirements of ISO/IEC 42001 for organizational AI governance, including AI principles, policies, objectives, roles, and responsibilities
- Explain how EN 18286 complements ISO/IEC 42001 and supports quality-management requirements for high-risk AI systems under the EU AI Act
- Define the scope of an AI management system and a regulatory quality management system for AI
- Identify, assess, and treat AI-related risks, conduct an AI system impact assessment, and identify appropriate AI controls
- Interpret key EU AI Act obligations and explain relevant conformity-assessment pathways
- Identify lifecycle processes and controls for AI inception, design, development, verification, validation, data management, operations, monitoring, and retirement
- Describe processes for post-market monitoring, incident reporting, and handling non-compliance
- Identify documentation and evidence needed to support audits and conformity assessment
- Explain how ISO/IEC 42001 and EN 18286 can be integrated into existing organizational management systems
- Evaluate and improve AI management processes through management review, monitoring and measurement, audits, and corrective action
Agenda
Below you will find an outline of the training course schedule.
DAY 1
- Responsible AI Foundations
- AI system characteristics and lifecycle
- Trustworthiness, safety, and fundamental rights
- Case study: Automotive pedestrian detection
- EU AI Act Overview
- Structure and scope of the Regulation
- High-risk AI system classification
- Obligations for providers, deployers, importers, and distributors
- Fundamental rights protections and risk-based regulatory design
- ISO/IEC 42001: AI Management System
- Purpose and structure of the standard
- Organizational context and roles
- AI policy, objectives, and governance
- Risk assessment, risk treatment, and impact assessment
- Documented information and control requirements
- Integrating ISO/IEC 42001 with EN 18286
- Introduction to EN 18286
- How organizational AI governance differs from regulatory QMS
- Mapping between the standards (based on EN 18286 Annex C)
- When each standard applies and how they reinforce each other
DAY 2
- EN 18286: Quality Management System for High-Risk AI Systems
- Identifying regulatory requirements
- Determining QMS scope
- Strategy for regulatory compliance
- Quality policy and quality objectives
- Documented information requirements
- AI System Realization
- Inception, design, and development
- Verification and validation
- Data management requirements
- AI system identification
- Retirement and decommissioning
- Continuous learning systems and predetermined changes
- Product documentation requirements for conformity assessment
- Operations and Control
- Placing on the market vs. putting into service
- Support services and technical documentation
- Supply chain controls and contractual requirements
- Modifications to AI systems
- Post-market monitoring
- Serious incident reporting
- Handling non-compliance
- Performance Evaluation and Continuous Improvement
- Management review
- Planning changes
- Monitoring and measurement
- Corrective actions and audit readiness
- Integrated Case Study
- Defining scope and roles
- Performing risk and impact assessments
- Establishing lifecycle controls
- Creating required documentation
- Designing post-market monitoring and incident reporting workflows
- Preparing for conformity assessment under the EU AI Act
