Master’s in AI Governance, AI Act & Privacy Engineering in AD
Online master's programme of 1,900 hours in AI governance, AI Act and privacy engineering for autonomous driving, structured in 12 modules covering the EU AI Act, AI management systems under ISO/IEC 42001, data governance, GDPR and the Data Act for connected vehicles, transparency, robustness, human oversight, ethics, conformity and AI auditing, with an assigned technical tutor.
Master’s in AI Governance, AI Act & Privacy Engineering in AD — studying from the United States
The United States is a major center for autonomous driving and advanced driver assistance systems, with significant activity in California, Michigan, Pennsylvania and Texas. Automakers, technology firms and specialized startups develop sensor fusion, perception, planning and vehicle software platforms. The shift toward software-defined vehicles is reshaping traditional engineering roles and increasing demand for embedded software, cybersecurity and systems engineering talent. Federal and state rules on testing and safety create a demanding environment where validation and standards expertise is highly valued.
Fees for the United States: US$6,610 (US Dollars), with instalments and employer sponsorship available.
Relevant bodies in the United States
Typical roles
Questions from the United States
What US bodies and rules govern autonomous and ADAS vehicle engineering?
In the US, NHTSA sets federal vehicle safety standards and has issued guidance on automated driving systems, while states regulate testing and operation on public roads. SAE International's levels of driving automation are the common industry reference. Professional engineering licensure is handled by state boards. SEIUM's qualifications are private and not accredited by any national body, so their recognition is for employers or relevant bodies to decide.
Do US employers in autonomous driving and software-defined vehicles recognize private engineering qualifications?
US companies working on ADAS and software-defined vehicles, including automakers, Tier 1 suppliers and technology firms, mainly look for software, controls and safety-engineering experience. A private qualification can document specialist study, but it is not a standard hiring requirement and its value is assessed by each employer. Projects, code samples and relevant prior roles usually carry more weight.
Can I study autonomous and software-defined vehicle topics from the US alongside my job?
Yes. The programmes are online and taught in English, so you can study from home without a student visa. Expect a significant time commitment: about 1,900 hours for a master's, 800 hours for a postgraduate diploma, or 300 hours for a specialist course. Each student receives an assigned technical tutor. Fees may be paid in instalments, and some employers fund training, which you should discuss with your employer.
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 EU AI Act for vehicles.
Implement AI management systems in line with ISO/IEC 42001.
Design the governance of training data.
Apply the GDPR and the Data Act to connected vehicles.
Assess transparency, robustness and human oversight.
Prepare conformity documentation for AI systems.
Develop a complete AI governance project for automated driving.
Who it's for
- AI governance managers setting up policies and management systems.
- Data protection officers dealing with connected and automated vehicles.
- Trustworthy AI engineers working on robustness, explainability and data quality.
- Algorithm auditors assessing AI systems in automotive.
- Regulatory compliance consultants advising on the AI Act, GDPR and Data Act.
- Technology ethics leads shaping responsible AI practice.
- Engineering or science graduates aiming to enter the automotive sector.
What you'll work with
- Regulation (EU) 2024/1689 (AI Act)
- Regulation (EU) 2016/679 (GDPR)
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
- Regulation (EU) 2023/2854 (Data Act)
- ISO/PAS 8800 (safety and AI in road vehicles)
- ISO/IEC 22989 (AI concepts and terminology)
- Directive (EU) 2024/2853 (liability for defective products)
- Python
- PyTorch Captum
- OneTrust
- Great Expectations
- MLflow
12 modules · 1900 h
1.1Uses of AI in perception and decision-making1.2AI in the cabin and voice assistants1.3AI in connected services1.4Risks specific to AI1.5Opacity and limited explainability1.6Dependence on data1.7Accountability for automated decisions1.8Social expectations and trust1.9European ethical framework for trustworthy AI1.10Case study: inventory of an OEM's AI systems
2.1Structure of Regulation (EU) 2024/16892.2Classification by risk level2.3High-risk systems and annexes2.4Relationship with vehicle type approval2.5Obligations of providers and deployers2.6General-purpose AI models2.7Application timeline2.8Harmonised standards under development2.9Penalties and supervision2.10Lab: classification of a vehicle's systems
3.1The ISO/IEC 42001 standard3.2AI policies and objectives3.3Roles and responsibilities3.4AI impact assessment3.5Risk management under ISO/IEC 238943.6AI system lifecycle3.7Annex A controls3.8Management system audit3.9Integration with ISO 9001 and IATF3.10Practical exercise: a company's AI policy
4.1Data quality under the AI Act4.2Representativeness and bias4.3Data lineage and traceability4.4Labelling and quality control4.5Synthetic data and its governance4.6Management of driving datasets4.7Documentation with datasheets4.8Data retention and deletion4.9Data governance in the organisation4.10Workshop: datasheet for a driving dataset
5.1GDPR applied to connected vehicles5.2EDPB guidelines on connected vehicles5.3Image data of third parties on public roads5.4Anonymisation of faces and number plates5.5Biometric data in the cabin5.6Legal bases for processing5.7Data protection impact assessment (DPIA)5.8International data transfers5.9Data subject rights5.10Practical exercise: DPIA for a test fleet
6.1Regulation (EU) 2023/28546.2User access to generated data6.3Sharing with third parties6.4Trade secrets and their protection6.5Data access interfaces6.6Data for repair and maintenance6.7Relationship with the GDPR6.8Design obligations6.9The debate on data access in automotive6.10Case study: user data portal
7.1Transparency requirements of the AI Act7.2Post hoc explainability techniques7.3Saliency maps and attribution7.4Interpretable-by-design models7.5Explanations for users and authorities7.6Logs and traceability of decisions7.7Mandatory technical documentation7.8Model cards and system cards7.9Limitations of explainability7.10Lab: saliency of a pedestrian detector
8.1Robustness requirements of the AI Act8.2Adversarial attacks and poisoning8.3Robustness to distribution shift8.4Declared accuracy metrics8.5Cybersecurity of AI models8.6Security of the model supply chain8.7Robustness testing8.8Post-market monitoring of models8.9Relationship with ISO/PAS 88008.10Practical exercise: data poisoning test
9.1Human oversight in automated systems9.2Ethical dilemmas in driving9.3The Commission report on ethics of automated vehicles9.4Fairness and non-discrimination9.5Civil and product liability9.6The Product Liability Directive9.7Ethics committees9.8Stakeholder engagement9.9Risk communication9.10Workshop: ethical assessment of a function
10.1Conformity assessment of high-risk systems10.2Annex IV technical documentation10.3Quality management system10.4Registration in the EU database10.5CE marking of AI systems10.6Management of serious incidents10.7Harmonised standards and presumption of conformity10.8Notified bodies and their role10.9Integration with type approval10.10Case study: technical file of a system
11.1Internal AI audits11.2External algorithmic audits11.3Governance metrics11.4Change management in models11.5Revalidation after updates11.6Management of AI suppliers11.7AI training and literacy11.8Organisational culture of responsible AI11.9Compliance indicators11.10Practical exercise: AI audit plan
12.1Inventory and classification of systems12.2AI risk assessment12.3Design of the management system12.4Data governance12.5Privacy impact assessment12.6Transparency and oversight plan12.7Compliance roadmap12.8Conclusions and recommendations12.9Thesis report12.10Master's Thesis: defence before a panel
How you'll learn
Training is 100% online 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 the programme can be combined with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python, PyTorch Captum, OneTrust, Great Expectations and MLflow, in free, academic or evaluation versions. Exercises are based on real procedures for privacy impact assessments, audits of AI systems and robustness testing of models. Assessment is continuous by module, plus the final Master's Thesis.
Standards covered: Regulation (EU) 2024/1689 (AI Act), Regulation (EU) 2016/679 (GDPR), ISO/IEC 42001, ISO/IEC 23894, Regulation (EU) 2023/2854 (Data Act), ISO/PAS 8800, ISO/IEC 22989, Directive (EU) 2024/2853 (liability for defective products).
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
- A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
- An internet connection and a computer capable of running the programme's 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 Python, PyTorch Captum, OneTrust, Great Expectations and MLflow, 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's 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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