April 24, 2026
The Institute of Informatics & Telecommunications (IIT) of NCSR Demokritos announces a specialised professional training programme focused on the operationalisation of AI governance under the European regulatory framework.
As the EU AI Act introduces concrete and enforceable obligations, organisations are required to move beyond high-level awareness and adopt structured, auditable, and scalable approaches to AI governance. In this context, AI-focused Quality Management Systems (QMS) are emerging as a key mechanism for embedding compliance, accountability, and continuous oversight into organisational processes.
📅 AI Act Compliance Training | 11 June 2026
📍 NCSR Demokritos Campus | 🌍 Language: English | 🕒 1 day | 🍽️ Catering included
📍 On-site participation available for those living/working in Athens
💻 Online access for remote participants from anywhere in the world
Register here
This training provides a practical, implementation-oriented approach to AI Act compliance through the design and application of an AI Quality Management System (QMS).
Drawing on emerging European standardisation efforts — including approaches such as prEN 18286 — the programme explores how organisations can translate regulatory requirements into operational processes, governance structures, and measurable controls across the AI lifecycle.
Participants will develop a clear understanding of how to structure:
- governance frameworks and internal responsibilities
- documentation and evidence-based compliance mechanisms
- lifecycle controls, monitoring, and review processes
- continuous improvement and audit readiness
The programme is designed to bridge the gap between AI governance strategy and real-world implementation, supporting organisations in building robust and future-proof compliance capabilities.
Participants who successfully attend the training will receive a Certificate of Participation issued by the Institute of Informatics and Telecommunications of NCSR Demokritos.
Why This Programme Matters
AI quality management is rapidly becoming a core enabler of trustworthy, accountable, and compliant AI systems.
For most organisations, the challenge is no longer whether AI governance is necessary, but how to implement it in a way that is coherent, scalable, and operationally effective.
This programme supports that transition by enabling participants to:
- translate regulatory and governance requirements into practical processes
- strengthen internal coordination and organisational accountability
- improve compliance readiness and auditability
- align with evolving European regulatory and standardisation developments
Intended Audience
This training is designed for professionals requiring a structured and practice-oriented understanding of AI governance and quality management, including:
- AI governance and compliance professionals
- Legal, policy, and regulatory experts
- Quality managers and internal assurance teams
- AI product owners and innovation managers
- Data governance professionals
- Technical leads, engineers, and data scientists
- Consultants advising on AI governance, standards, and compliance
- Public-sector professionals involved in AI oversight, procurement, or deployment
- Private-sector decision-makers strengthening organisational AI governance capacity
Course Structure
- AI Quality Management and the EU AI Act
- AI quality management as a strategic organisational capability
- Transitioning from principles to implementation-oriented governance
- The role of standards and structured frameworks in AI assurance
- The Logic of AI-Specific QMS Approaches
- Applying Quality Management System thinking to AI
- Governance, accountability, consistency, and traceability
- Supporting organisational preparedness and regulatory alignment
- ISO/IEC 42001 and AI Management Systems
- Scope and structure of ISO/IEC 42001
- Organisational AI management systems in practice
- Governance models, roles, and review mechanisms for AI oversight
- Core Components of an AI Quality Management System
- Governance structures, roles, and responsibilities
- Policies, procedures, and documentation frameworks
- Lifecycle controls and cross-functional coordination
- Monitoring, internal review, and continuous improvement
- Supplier and third-party risk and assurance considerations
Register here
Registration is open. Seats are limited.
Contact us with your questions: aicertifications@iit.demokritos.gr
Please note: Your registration will only be considered as completed upon full payment of the fees.
Three-Tier Fee Structure:
| Registration Tier | Deadline (Athens Time) | Fee |
| Tier 0 | For students till the age of 26 | €240 |
| Early bird | Until 21 May 2026, 23:59 | €420 |
| Normal Fee | After 21 May 2026 | €550 |
*Registration is confirmed upon completion of payment.
Participants will receive a confirmation email with further details regarding the training, including access information (for online attendance) and instructions.
Register here
About the instructor
Alexandros Nousias is a Research Associate at the Institute of Informatics & Telecommunications of NCSR-D in Digital and AI law, governance and ethics. He consults research organisations and tech companies in the context of research and real market projects. In Academia, Alexandros is lead instructor of the Applied Ethics module in the context of MSc in AI at University of Piraeus, and MSc in Data Science at the University of Peloponnese. Additionally he lectures at several other universities, while serving as trainer and mentor for corporates and startups. As a technical consultant to the High-Level Advisory Committee on Artificial Intelligence for the Prime Minister, Alexandros contributed to drafting the “Blueprint for Greece’s AI Transformation.” He also actively contributes to the National Commission for Bioethics and Technoethics and serves as a Technical Representative at the Standards Working Group under the EU AI Board. Finally, Alexandros is an IEEE CertifAIEd AI Ethics Lead Assessor. He lectures on topics related AI ethics in the context of the two interdepartamental Masters of Science of IIT (MSc in AI, MSc in Data Science).