Course in European AI Act Regulation Applied to Defence
Online 300 h course in European AI Act regulation applied to defence. 6 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), ISO/IEC 42001 (AI management systems) and Python (scikit-learn). Assigned tutor and open enrolment.
Course in European AI Act Regulation Applied to Defence — studying from the United Kingdom
The United Kingdom maintains a substantial defence industry, with prime contractors such as BAE Systems and Babcock, and a wide supply chain across the Midlands, the North West, Bristol and Scotland. Land systems, armoured vehicles, munitions and military engineering programmes provide long-term employment for engineers in design, integration, test and sustainment. The Ministry of Defence, Defence Equipment and Support and the Defence Science and Technology Laboratory oversee procurement and research. Security clearance requirements shape recruitment, and professional standards are maintained through UK defence engineering frameworks.
Fees for the United Kingdom: £455 (Pound sterling), with instalments and employer sponsorship available.
Relevant bodies in the United Kingdom
Typical roles
Questions from the United Kingdom
Which UK bodies and regulators are relevant to defence and military land systems engineering?
In the UK, the Ministry of Defence sets its own engineering standards and the Engineering Council regulates professional registration. Professional bodies such as the Institution of Mechanical Engineers (IMechE) and the Royal Engineers' professional structures are relevant. SEIUM's qualifications are private and not accredited by these bodies, so recognition for security-cleared or MOD roles is decided by the employer and the relevant authority.
Will a private qualification help with UK defence industry careers?
UK defence employers, including primes and suppliers, often prioritise security clearance eligibility, relevant experience and formal qualifications recognised by the MOD or industry bodies. A private qualification may support your profile but is unlikely to replace these requirements. Ask prospective employers how they would assess it before enrolling.
Is it practical to study this online while working in the UK, and what about language and funding?
Online study means you can continue working in the UK, and no student visa is needed to study from home. Teaching is in English, so good technical English is essential. Fees can be paid in instalments, and some employers offer training sponsorship, though policies vary. Each programme has a fixed number of hours and an assigned technical tutor. Confirm the workload and costs before enrolling.
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
Translate the AI Act requirements into verifiable engineering requirements.
Implement a risk management system compliant with ISO/IEC 23894.
Establish governance and quality practices for training data.
Incorporate robustness, accuracy, cybersecurity and human oversight requirements into design.
Prepare the technical documentation and records required by the regulation.
Plan conformity assessment and post-market surveillance.
Develop a technical compliance project for a dual-use AI system.
Who it's for
- AI compliance engineer who wants to specialise in European AI Act regulation applied to defence.
- Machine learning engineer who wants to specialise in European AI Act regulation applied to defence.
- AI risk management lead who wants to specialise in European AI Act regulation applied to defence.
- Technical auditor of AI systems who wants to specialise in European AI Act regulation applied to defence.
- Data quality engineer who wants to specialise in European AI Act regulation applied to defence.
- Conformity assessment consultant who wants to specialise in European AI Act regulation applied to defence.
- Engineering or science graduates seeking to enter the defence and security industry.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
- ISO/IEC 22989 (AI concepts and terminology)
- ISO/IEC 25059 (quality of AI systems)
- IEEE 7000-2021 (ethical considerations in system design)
- NIST AI RMF 1.0 (AI risk management framework)
6 modules · 300 h
1.1Technical requirements of Chapter III of the AI Act1.2Defence industry with dual-use products1.3When a defence provider becomes subject1.4High-risk systems in Annex III1.5Regulated products in Annex I1.6Harmonised standards in preparation1.7Presumption of conformity1.8Product portfolio and regulatory triage1.9Responsibilities across the supply chain1.10Workshop: regulatory triage of a product portfolio
2.1Risk management system under Article 92.2ISO/IEC 23894 applied2.3Data governance under Article 102.4Data quality and representativeness2.5Classified data and its handling2.6Bias detection and mitigation2.7Synthetic data in defence2.8Data lineage traceability2.9Integration with ISO/IEC 420012.10Practice: risk register for a vision system
3.1Technical documentation under Annex IV3.2Automatic event logging3.3Transparency and instructions for use3.4Human oversight by design3.5Accuracy, robustness and cybersecurity3.6Resilience to adversarial attacks3.7Data and model poisoning3.8Human-in-the-loop architectures3.9Information security requirements3.10Laboratory: designing logs for audit
4.1Internal control under Annex VI4.2Notified body assessment under Annex VII4.3Quality management system under Article 174.4EU declaration of conformity4.5CE marking of AI systems4.6Registration in the EU database4.7Regulatory sandboxes4.8Testing in real-world conditions4.9Relationship with AQAP 2110 and EN 91004.10Case study: conformity file for a civil surveillance drone
5.1Post-market monitoring5.2Reporting serious incidents5.3Management of substantial modifications5.4Retraining and version control5.5MLOps in secure environments5.6Internal audits5.7Interaction with market surveillance authorities5.8Cybersecurity and CRA5.9Compliance indicators5.10Case study: managing a serious incident
6.1Selection of the dual-use system6.2Risk classification6.3Gap analysis6.4Risk management plan6.5Design of technical controls6.6Technical documentation6.7Conformity assessment plan6.8Monitoring plan6.9Report and presentation6.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded lectures and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn), MLflow, Great Expectations, DVC (data version control), IBM AI Fairness 360, in free, academic or evaluation versions. Testing procedures are studied using real conformity assessment procedures for high-risk systems, data quality audits, and robustness and accuracy testing of models. Assessment is continuous for each module plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Translate the AI Act requirements into verifiable engineering requirements.
Implement a risk management system compliant with ISO/IEC 23894.
Establish governance and quality practices for training data.
Incorporate robustness, accuracy, cybersecurity and human oversight requirements into the design.
Prepare the technical documentation and records required by the regulation.
Where this takes you
Requirements
- No prior qualification is required.
- Internet connection and a computer capable of running the programme's software.
- English language proficiency sufficient to follow technical regulatory texts.
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 comprises 300 hours in total, spread over 3 to 5 months. That is an average of 10 to 15 hours per week, compatible with a full-time job.
The course works with Python (scikit-learn), MLflow, Great Expectations, DVC (data version control), IBM AI Fairness 360. Free, academic or evaluation versions are used, and the tutor indicates the open-source alternative where one exists.
An own diploma from SEIUM Institute upon passing the continuous assessment and the integrative project. This is in-house training, not an official title from the Ministry.
Yes. Modules passed in a course or Postgraduate Diploma in the same area are recognised towards the higher-level programme. Check the recognition policy when you enrol.
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