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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.

Duration300 h
ModeOnline
Start datesOctober / March
LocationEurope (Madrid)
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Translate the AI Act requirements into verifiable engineering requirements.

02

Implement a risk management system compliant with ISO/IEC 23894.

03

Establish governance and quality practices for training data.

04

Incorporate robustness, accuracy, cybersecurity and human oversight requirements into design.

05

Prepare the technical documentation and records required by the regulation.

06

Plan conformity assessment and post-market surveillance.

07

Develop a technical compliance project for a dual-use AI system.

Profile

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.
Standards & tools

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)
Syllabus

6 modules · 300 h

  1. 1.1Technical requirements of Chapter III of the AI Act
  2. 1.2Defence industry with dual-use products
  3. 1.3When a defence provider becomes subject
  4. 1.4High-risk systems in Annex III
  5. 1.5Regulated products in Annex I
  6. 1.6Harmonised standards in preparation
  7. 1.7Presumption of conformity
  8. 1.8Product portfolio and regulatory triage
  9. 1.9Responsibilities across the supply chain
  10. 1.10Workshop: regulatory triage of a product portfolio
  1. 2.1Risk management system under Article 9
  2. 2.2ISO/IEC 23894 applied
  3. 2.3Data governance under Article 10
  4. 2.4Data quality and representativeness
  5. 2.5Classified data and its handling
  6. 2.6Bias detection and mitigation
  7. 2.7Synthetic data in defence
  8. 2.8Data lineage traceability
  9. 2.9Integration with ISO/IEC 42001
  10. 2.10Practice: risk register for a vision system
  1. 3.1Technical documentation under Annex IV
  2. 3.2Automatic event logging
  3. 3.3Transparency and instructions for use
  4. 3.4Human oversight by design
  5. 3.5Accuracy, robustness and cybersecurity
  6. 3.6Resilience to adversarial attacks
  7. 3.7Data and model poisoning
  8. 3.8Human-in-the-loop architectures
  9. 3.9Information security requirements
  10. 3.10Laboratory: designing logs for audit
  1. 4.1Internal control under Annex VI
  2. 4.2Notified body assessment under Annex VII
  3. 4.3Quality management system under Article 17
  4. 4.4EU declaration of conformity
  5. 4.5CE marking of AI systems
  6. 4.6Registration in the EU database
  7. 4.7Regulatory sandboxes
  8. 4.8Testing in real-world conditions
  9. 4.9Relationship with AQAP 2110 and EN 9100
  10. 4.10Case study: conformity file for a civil surveillance drone
  1. 5.1Post-market monitoring
  2. 5.2Reporting serious incidents
  3. 5.3Management of substantial modifications
  4. 5.4Retraining and version control
  5. 5.5MLOps in secure environments
  6. 5.6Internal audits
  7. 5.7Interaction with market surveillance authorities
  8. 5.8Cybersecurity and CRA
  9. 5.9Compliance indicators
  10. 5.10Case study: managing a serious incident
  1. 6.1Selection of the dual-use system
  2. 6.2Risk classification
  3. 6.3Gap analysis
  4. 6.4Risk management plan
  5. 6.5Design of technical controls
  6. 6.6Technical documentation
  7. 6.7Conformity assessment plan
  8. 6.8Monitoring plan
  9. 6.9Report and presentation
  10. 6.10Final review with the tutor
Methodology, software & labs

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.

Accreditable laboratories(EN ISO/IEC 17025) with a rotor test rig, EMC/Lightning pre-compliance, HIL/SIL, vibration/acoustics.
Evidence-oriented Master's Thesis:safety case, test plan, compliance dossier and operational limits.
Industry mentoring:lecturers with track records in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:a safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Selection of the dual-use system

Translate the AI Act requirements into verifiable engineering requirements.

Risk classification

Implement a risk management system compliant with ISO/IEC 23894.

Gap analysis

Establish governance and quality practices for training data.

Risk management plan

Incorporate robustness, accuracy, cybersecurity and human oversight requirements into the design.

Design of technical controls

Prepare the technical documentation and records required by the regulation.

Career opportunities

Where this takes you

AI compliance engineerMachine learning engineerAI risk management leadTechnical auditor of AI systemsData quality engineerConformity assessment consultant
Admissions & scholarships

Requirements

  • No prior qualification is required.
  • Internet connection and a computer capable of running the programme's software.
  • Spanish 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.

FAQ

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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