Military AI Compliance Course
Online 300-hour course in compliance in military AI. 6 modules covering ISO/IEC 42001 (AI management systems), ISO/IEC 23894 (AI risk management) and Python (scikit-learn). Assigned tutor and open enrolment.
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
Understand the ethical and regulatory frameworks applicable to AI in defence.
Classify machine learning systems according to their risk level.
Apply data governance, privacy and security requirements.
Define evidence of verification, traceability and explainability.
Assess bias, robustness and model drift with open tools.
Establish human oversight, incident management and continuous improvement processes.
Design an AI management system aligned with ISO/IEC 42001.
Who it's for
- AI compliance officer who wants to specialise in Military AI Compliance.
- Auditor of machine learning systems who wants to specialise in Military AI Compliance.
- AI risk analyst who wants to specialise in Military AI Compliance.
- Technology ethics consultant who wants to specialise in Military AI Compliance.
- Data governance engineer who wants to specialise in Military AI Compliance.
- Adviser on defence and technology policy who wants to specialise in Military AI Compliance.
- Graduates in engineering or sciences seeking to enter the defence industry.
What you'll work with
- ISO/IEC 42001 (AI management systems)
- ISO/IEC 23894 (AI risk management)
- NIST AI RMF 1.0 (AI risk management framework)
- NATO Principles for the Responsible Use of AI (2021)
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- DoD Directive 3000.09 (autonomy in weapon systems)
- Regulation (EU) 2016/679 (GDPR)
6 modules · 300 h
1.1Why these systems pose a compliance problem of their own1.2Opacity, model evolution and data1.3Actors: developer, integrator, operator and user1.4Allocation of responsibilities across the chain1.5Applicable regulatory landscape1.6European artificial intelligence regulation1.7Data protection regulation1.8Export control of technology and models1.9Vocabulary of regulatory compliance1.10Exercise: obligations map for a system
2.1Determining intended use2.2Classifying the system by risk level2.3Exclusions and prohibited uses2.4Risk management requirements2.5Data governance requirements2.6Required technical documentation2.7Activity logging and retention2.8Transparency and information for the user2.9Mandatory human oversight2.10Practice: classify a system and list its requirements
3.1Legal basis for processing training data3.2Data minimisation and purpose limitation3.3Special category data and exceptions3.4Data protection impact assessment3.5International transfers of data and models3.6Security of the model and data3.7Extraction and poisoning attacks3.8Model vulnerability management3.9Security of the model supply chain3.10Practice: impact assessment of a processing activity
4.1Evaluation plan and acceptance criteria4.2Performance, robustness and bias testing4.3Documentary evidence for each requirement4.4Traceability between requirement and test4.5Conformity assessment and bodies4.6Declaration of conformity and marking4.7Internal audit of the management system4.8Preparing for an inspection4.9Management of deviations and action plan4.10Practice: compliance matrix for a system
5.1Post-deployment monitoring5.2Drift detection and alert thresholds5.3Retraining and change management5.4Management of serious incidents and notification5.5System withdrawal and contingency plan5.6Training of operators and users5.7Complaints channel and rights of individuals5.8Compliance indicators5.9Management review and continuous improvement5.10Practice: incident response protocol
6.1Choice of system and its context6.2Classification and obligations map6.3Risk management and data governance6.4Verification plan and evidence6.5Technical and transparency documentation6.6Monitoring and incident plan6.7Audit and training programme6.8Complete compliance matrix6.9Compliance manual6.10Defence of the work
How you'll learn
100% online training on the SEIUM campus, with downloadable material, recorded classes 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, IBM AI Fairness 360, SHAP, in free, academic or evaluation versions. Tests are studied using real documentary compliance audit procedures, bias and robustness assessment on open datasets, and analysis of ethical and regulatory case studies. Assessment is continuous per 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.
Understand the ethical and regulatory frameworks applicable to AI in defence.
Classify machine learning systems according to their risk level.
Apply data governance, privacy and security requirements.
Define evidence of verification, traceability and explainability.
Assess bias, robustness and model drift with open tools.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient command of Spanish 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 between 10 and 15 hours per week on average, compatible with a full-time job.
The course uses Python (scikit-learn), MLflow, IBM AI Fairness 360 and SHAP. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own diploma upon passing the continuous assessment and the integrative project. It is own training, not an official qualification from the Ministry.
Yes. Modules passed in a course or postgraduate diploma in the same field are recognised in the advanced programme. Consult the recognition policy when you enrol.
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