Master’s in AI Governance and Compliance (EU AI Act)
Online master's programme of 1,900 hours in AI governance and compliance under the EU AI Act: risk, documentation, evaluation and ethics. 12 modules covering the regulatory ecosystem, roles and obligations, AI management systems, AI risk management, data governance, technical documentation, system evaluation and conformity assessment, with Regulation (EU) 2024/1689, ISO/IEC 42001 and ISO/IEC 23894, and an assigned technical tutor.
Master’s in AI Governance and Compliance (EU AI Act) — studying from Canada
Canada is a recognised centre for artificial intelligence research, with hubs in Toronto, Montréal and Edmonton supported by institutes such as the Vector Institute, Mila and Amii. Engineering teams across manufacturing, energy, finance and transport apply simulation, machine learning and data analytics to real operational problems. The federal government's Pan-Canadian AI Strategy and privacy regulation shape how organisations handle data. Typical employers include technology firms, banks, aerospace and automotive companies, and public research bodies.
Fees for Canada: 12,300 CA$ (Canadian dollar), with instalments and employer sponsorship available.
Relevant bodies in Canada
Typical roles
Questions from Canada
How are fees shown for Canadian learners?
All fees on this site are displayed in Canadian dollars (CAD). Instalment plans are available, and employer sponsorship can be discussed.
Are SEIUM programmes official Canadian degrees?
No. SEIUM qualifications are private awards issued by SEIUM Institute and are not official degrees recognised by Canadian federal or provincial authorities.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Interpret the European AI Regulation and its relationship with other legislation.
Classify AI systems according to their risk level.
Determine the obligations of each role in the value chain.
Implement an AI management system in line with ISO/IEC 42001.
Manage technical, ethical and fundamental-rights risks.
Prepare technical documentation and conformity processes.
Design AI governance structures within organisations.
Who it's for
- AI governance leads building the policies and controls their organisation needs under the AI Act.
- AI system auditors who assess fairness, robustness and documentation.
- Regulatory compliance consultants extending their practice to artificial intelligence.
- AI compliance officers responsible for inventories, risk registers and conformity.
- Technology risk analysts working in banking, insurance or healthcare.
- Data protection officers whose remit now covers AI systems.
- Engineering or science graduates looking to enter banking and insurance.
What you'll work with
- Regulation (EU) 2024/1689 (AI Act — harmonised rules on artificial intelligence)
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (guidance on AI risk management)
- Regulation (EU) 2016/679 (GDPR — protection of personal data)
- NIST AI RMF 1.0 (AI Risk Management Framework)
- ISO/IEC 22989 (artificial intelligence concepts and terminology)
- Directive (EU) 2024/2853 (liability for defective products)
- ISO/IEC 27001 (information security management systems)
- Fairlearn
- AI Fairness 360
- OneTrust
- Microsoft Responsible AI Toolbox
- Credo AI
12 modules
1.1Definition of an AI system in legislation1.2OECD and UNESCO principles1.3European AI strategy1.4Relationship with the GDPR, Data Act and DSA1.5Liability for defective products1.6International frameworks: US, China and UK1.7Council of Europe Framework Convention1.8National authorities and AESIA1.9Application timeline of the Regulation1.10Case study: regulatory map of a technology company
2.1Structure and scope2.2Prohibited practices under Article 52.3Classification of high-risk systems2.4Annex III and safety components2.5Transparency obligations2.6General-purpose AI models2.7Systemic risk and codes of practice2.8Penalties and supervisory regime2.9Regulatory sandboxes2.10Workshop: classifying a system inventory
3.1Provider, deployer and importer3.2Distributor and authorised representative3.3Substantial modifications and change of role3.4Obligations of high-risk system providers3.5Obligations of deployers3.6Fundamental rights impact assessment3.7Model supply chain3.8AI contracts and clauses3.9AI literacy for staff3.10Practical exercise: responsibility matrix for a project
4.1ISO/IEC 42001: structure4.2AI policy and objectives4.3Integration with ISO 9001 and ISO/IEC 270014.4Inventory of systems and use cases4.5Annex A controls4.6Internal audit and review4.7Certification of the management system4.8Performance indicators4.9Continual improvement4.10Lab: designing an AIMS for an SME
5.1ISO/IEC 23894 and risk management5.2NIST AI RMF: govern, map, measure, manage5.3Identifying harms and affected parties5.4Bias and discrimination risks5.5Security and robustness risks5.6Privacy risks5.7Assessment and treatment5.8Residual risk and acceptance5.9Risk register5.10Practical exercise: risk analysis of a recruitment system
6.1Requirements of Article 106.2Quality, representativeness and bias6.3Data lineage and provenance6.4Personal data and legal bases6.5Data protection impact assessments6.6Synthetic data and anonymisation6.7Copyright and text and data mining6.8Dataset documentation6.9Retention and erasure6.10Case study: auditing a training dataset
7.1Contents of Annex IV7.2Model cards and datasheets7.3Instructions for use7.4Event logging and traceability7.5Transparency for end users7.6Labelling of generated content7.7Explainability tailored to the audience7.8EU database of systems7.9Life-cycle document management7.10Workshop: technical file for a high-risk system
8.1Performance and accuracy metrics8.2Bias and fairness testing8.3Robustness and cybersecurity of models8.4Adversarial testing and red teaming8.5Evaluation of generative models8.6Effective human oversight8.7CEN-CENELEC harmonised standards8.8Open evaluation tools8.9Evaluation reports8.10Lab: fairness audit with Fairlearn
9.1Internal control procedures9.2Assessment by a notified body9.3CE marking of AI systems9.4EU declaration of conformity9.5AI systems in regulated products9.6AI-enabled medical devices9.7Machinery and vehicles with AI9.8Post-market monitoring9.9Reporting serious incidents9.10Case study: conformity route for an AI-enabled device
10.1Ethics and AI committees10.2Roles: AI officer and compliance officer10.3Policies on the use of generative AI10.4Use-case approval process10.5Managing AI suppliers10.6Training and culture10.7Whistleblowing channel10.8Indicators for the board10.9Integration with criminal compliance10.10Practical exercise: corporate AI policy
11.1Banking: credit scoring and model risk11.2Insurance and pricing11.3Human resources and employment11.4Healthcare and diagnosis11.5Public administration11.6Critical infrastructure11.7Education and assessment11.8Biometrics and surveillance11.9Precedents and rulings11.10Case study: impact assessment of a public service
12.1Selecting the organisation or system12.2Inventory and classification12.3Regulatory gap analysis12.4Risk assessment12.5Designing controls12.6Technical documentation plan12.7Compliance roadmap12.8Indicators and monitoring12.9Written thesis report12.10Master's Thesis: defence before a panel
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Fairlearn, AI Fairness 360, OneTrust, Microsoft Responsible AI Toolbox and Credo AI, in free, academic or evaluation versions. Practice is based on real procedures: AI system audits, fundamental rights impact assessments and compliance gap analyses. Assessment is continuous for each module, plus the final Master's Thesis.
Standards covered: Regulation (EU) 2024/1689 (AI Act), ISO/IEC 42001, ISO/IEC 23894, Regulation (EU) 2016/679 (GDPR), NIST AI RMF 1.0, ISO/IEC 22989, Directive (EU) 2024/2853 (Product Liability), ISO/IEC 27001.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Where this takes you
Requirements
- University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- Internet connection and a computer capable of running the programme software.
- English sufficient to read technical standards.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with Fairlearn, AI Fairness 360, OneTrust, Microsoft Responsible AI Toolbox and Credo AI, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.
A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.
Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.
A university degree in engineering, science or architecture (or equivalent professional experience), a computer able to run the software, and English sufficient to read technical standards.
Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.
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