Master’s in AI Governance and Compliance Engineering
Online master's programme of 1,900 hours in AI governance and compliance engineering. 12 modules covering the European AI Act, high-risk system requirements, management frameworks, AI risk management, fairness and explainability, data protection, AI security, generative AI governance and conformity assessment under Regulation (EU) 2024/1689, ISO/IEC 42001 and the GDPR, with an assigned technical tutor.
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 obligations of the European AI Regulation.
Classify AI systems according to their level of risk.
Implement AI management systems in line with ISO/IEC 42001.
Assess risks and impacts on fundamental rights.
Audit the fairness, explainability and robustness of models.
Integrate data protection into the AI life cycle.
Prepare conformity documentation for high-risk systems.
Who it's for
- AI governance leads building a compliance framework for their organisation.
- AI systems auditors who need a technical grounding in models.
- Regulatory compliance consultants advising on the AI Act.
- Data protection officers whose remit now includes AI.
- Technology risk managers assessing AI use cases.
- Responsible AI engineers turning principles into controls.
- Engineering or science graduates looking to enter banking and insurance.
What you'll work with
- Regulation (EU) 2024/1689 (AI Act)
- ISO/IEC 42001 (AI management systems)
- Regulation (EU) 2016/679 (GDPR, General Data Protection Regulation)
- ISO/IEC 23894 (AI risk management)
- NIST AI RMF 1.0 (AI Risk Management Framework)
- ISO/IEC 5338 (AI system life cycle processes)
- ISO/IEC 22989 (AI concepts and terminology)
- ISO/IEC 27001 (information security management systems)
- Fairlearn
- AI Fairness 360
- SHAP
- Microsoft Responsible AI Toolbox
- Garak
12 modules · 1900 h
1.1Risks and benefits of AI1.2OECD and UNESCO principles1.3AI ethics: autonomy, fairness and explainability1.4Corporate governance and AI governance1.5Actors in the regulatory ecosystem1.6Regulatory approaches in the EU, the US and China1.7Generative AI and new risks1.8The relationship between governance and strategy1.9Governance maturity in organisations1.10Case study: governance assessment of a company
2.1Structure of Regulation (EU) 2024/16892.2Prohibited AI practices2.3Classification of high-risk systems2.4Obligations of providers2.5Obligations of deployers2.6General-purpose AI models2.7Transparency obligations2.8Application timeline2.9Penalties regime and authorities2.10Workshop: classifying a portfolio of AI systems
3.1Risk management system3.2Data and data governance3.3Technical documentation under Annex IV3.4Record-keeping (event logging)3.5Transparency and instructions for use3.6Human oversight measures3.7Accuracy, robustness and cybersecurity3.8Quality management system3.9Post-market monitoring plan3.10Practical: requirements matrix for a high-risk system
4.1AI management systems according to ISO/IEC 420014.2AI risk management according to ISO/IEC 238944.3The NIST AI RMF framework4.4Life cycle according to ISO/IEC 53384.5Harmonised standards from CEN-CENELEC JTC 214.6Data quality according to ISO/IEC 52594.7Integration with ISO 27001 and ISO 90014.8Certification and auditing4.9Choosing the right framework4.10Lab: gap analysis against ISO/IEC 42001
5.1Identifying risks by use case5.2Fundamental rights impact assessment5.3Risk matrices and risk appetite5.4Bias and discrimination risks5.5Security and robustness risks5.6Privacy risks5.7Third-party and supplier risks5.8Controls and mitigations5.9Monitoring and reassessment5.10Workshop: impact of a recruitment system
6.1Definitions of fairness and their incompatibilities6.2Measuring bias in data and models6.3Bias mitigation techniques6.4Local and global explainability6.5Model cards and datasheets6.6Transparency towards users6.7Explanations in automated decisions6.8Technical limitations of explainability6.9Algorithmic auditing methodologies6.10Practical: bias audit with Fairlearn
7.1GDPR applied to the AI life cycle7.2Legal bases for training7.3Automated decisions and Article 227.4Data protection impact assessments7.5Minimisation and anonymisation7.6Differential privacy and synthetic data7.7Data subject rights in models7.8International transfers of training data7.9Criteria of supervisory authorities7.10Case study: DPIA for a scoring system
8.1Threats specific to AI systems8.2Data poisoning8.3Adversarial examples and evasion8.4Prompt injection in LLMs8.5Model extraction and theft8.6OWASP and MITRE ATLAS frameworks8.7Red teaming of models8.8Cybersecurity and the Cyber Resilience Act8.9Robustness testing8.10Lab: red teaming an LLM-based assistant
9.1Obligations for general-purpose models9.2General-purpose AI Code of Practice9.3Copyright and training data9.4Marking of synthetic content9.5Internal use of generative AI9.6Acceptable use policies9.7Assessing model providers9.8Systemic risks of general-purpose models9.9AI literacy9.10Workshop: corporate policy for the use of generative AI
10.1AI ethics and governance committee10.2Roles: AI officer and lines of defence10.3Inventory of AI systems10.4Use case approval processes10.5Public and private procurement of AI10.6Training and literacy10.7Compliance indicators10.8Governance tools10.9AI incident response10.10Practical: inventory and approval workflow for use cases
11.1Internal conformity assessment11.2Role of notified bodies11.3CE marking of AI systems11.4EU declaration of conformity11.5Registration in the EU database11.6Third-party audits11.7Evidence and traceability11.8Regulated sectors: financial and healthcare11.9Preparing for inspections11.10Case study: technical file for a high-risk system
12.1Selecting the organisation or system12.2Situation assessment12.3Regulatory classification of the system12.4Risk and impact assessment12.5Designing the governance system12.6Technical and organisational controls12.7Implementation and audit plan12.8Conclusions and compliance roadmap12.9Master's 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 the intake, so you can combine the programme with your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Fairlearn, AI Fairness 360, SHAP, Microsoft Responsible AI Toolbox and Garak, in free, academic or evaluation versions. Real procedures are studied: regulatory classification of AI systems, fundamental rights impact assessment, and bias audits of decision-making models. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: Regulation (EU) 2024/1689 (AI Act), ISO/IEC 42001, Regulation (EU) 2016/679 (GDPR), ISO/IEC 23894, NIST AI RMF 1.0, ISO/IEC 5338, ISO/IEC 22989 and 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, SHAP, Microsoft Responsible AI Toolbox and Garak, 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 programme 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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