Diploma in AI Governance for Autonomous Systems
Online postgraduate diploma of 800 hours in AI governance for autonomous systems. 8 modules covering governance concepts, AI regulation, AI management systems, technical assurance, functional safety, MLOps and traceability, and audit and certification, with an assigned technical tutor.
Diploma in AI Governance for Autonomous Systems — studying from India
India's defence sector is a major engineering employer, with public undertakings such as Bharat Electronics, Hindustan Aeronautics, Bharat Dynamics and Bharat Earth Movers, alongside private firms like Tata Advanced Systems and Larsen & Toubro. Research and development is coordinated by the Defence Research and Development Organisation, and procurement and capability planning sit within the Ministry of Defence. Engineers work on land systems, armoured vehicles, weapon platforms and military electronics, with a strong policy push towards indigenous design and manufacturing under the Make in India initiative.
Fees for India: ₹95,000 (Indian Rupee), with instalments and employer sponsorship available.
Relevant bodies in India
Typical roles
Questions from India
How are defence and military engineers recognised in India?
Defence engineering in India is largely recognised through government channels such as DRDO, the Ministry of Defence, and the armed forces' engineering branches, with recruitment governed by public service rules. The Institution of Engineers (India) is the general professional body. SEIUM's qualifications are private and not accredited by any national body, so recognition is decided by the employer or relevant authority.
Which employers in India hire defence and land systems engineers?
Employers include DRDO laboratories, Ordnance Factory Board units, defence public sector undertakings such as BEL and HAL, and private defence manufacturers. Military engineering roles are filled through official recruitment. A private online qualification can add technical depth, but government and defence employers mainly assess formal qualifications, security clearance and experience.
Is this programme workable with a job in India, and does it need a visa?
The programme is 100% online and taught in English, so no student visa is needed if you study from India. Time required is about 300 hours for specialist courses, 800 for diplomas and 1,900 for master's programmes. You get an assigned technical tutor, and fees can be paid in instalments. Ask your employer about sponsorship, since many defence firms fund further training.
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 principles and actors of AI governance.
Apply the European AI Act to autonomous systems.
Implement AI management systems in line with ISO/IEC 42001.
Build technical assurance cases for AI-based systems.
Integrate functional safety and SOTIF into autonomous systems.
Establish MLOps practices with full traceability.
Plan AI audits and certification processes.
Who it's for
- AI governance leads.
- AI systems auditors.
- Regulatory compliance consultants.
- Functional safety engineers.
- MLOps engineers.
- Technology risk analysts.
- Engineering or science graduates looking to enter the defence sector.
What you'll work with
- Regulation (EU) 2024/1689 (AI Act)
- ISO/IEC 42001:2023 (AI management systems)
- NIST AI RMF 1.0 (AI Risk Management Framework)
- ISO/IEC 23894:2023 (AI risk management)
- ISO 21448:2022 (SOTIF, safety of the intended functionality)
- UL 4600 (safety of autonomous products)
- IEC 61508 (functional safety)
- ISO/PAS 8800:2024 (AI in road vehicles)
- MLflow
- DVC
- IBM AI Fairness 360
- Python (SHAP)
8 modules
1.1Ethics, governance and compliance1.2AI system life cycle1.3Stakeholders and responsibilities1.4OECD AI Principles1.5UNESCO Recommendation on the Ethics of AI1.6NATO Principles of Responsible Use1.7National AI strategies1.8Risks specific to autonomy1.9Organisational AI maturity1.10Case study: stakeholder map of an autonomous programme
2.1Structure of the European AI Act2.2Classification by risk level2.3Requirements for high-risk systems2.4General-purpose AI models2.5Harmonised standards from CEN-CENELEC JTC 212.6Machinery Regulation (EU) 2023/12302.7Liability for defective products2.8Defence and national security exclusions2.9Regulatory sandboxes2.10Workshop: classifying a system under the AI Act
3.1ISO/IEC 42001 requirements3.2Risk management with ISO/IEC 238943.3NIST AI RMF 1.03.4AI policies and objectives3.5Roles and competences3.6Impact assessment with ISO/IEC 420053.7Management of AI suppliers3.8Internal audit of the management system3.9Continual improvement3.10Exercise: draft corporate AI policy
4.1Assurance cases with GSN4.2Data requirements4.3Model verification4.4Robustness and adversarial testing4.5In-service monitoring4.6Model change management4.7Explainability for auditors4.8Required technical documentation4.9Automatic event logging4.10Lab: assurance case in GSN
5.1IEC 61508 applied to AI-based systems5.2ISO 21448 and SOTIF5.3UL 4600 for autonomous products5.4ISO/PAS 8800 on AI in road vehicles5.5STPA analysis5.6Hazard identification5.7Operational design domain5.8Safe fallback strategies5.9Acceptable residual risk5.10Case study: STPA of an autonomous vehicle
6.1Training and deployment pipelines6.2Data and model versioning6.3Model registries6.4Drift monitoring6.5Reproducibility6.6Configuration management6.7Model supply chain6.8Machine learning security6.9Technical audit of pipelines6.10Lab: governed pipeline with MLflow
7.1Algorithmic auditing7.2Conformity assessment7.3Notified bodies7.4ISO/IEC 42001 certification7.5Governance performance indicators7.6Transparency reports7.7AI incident management7.8Public incident databases7.9Communication with regulators7.10Workshop: audit plan for an autonomous system
8.1Selection of the autonomous system8.2Regulatory classification8.3Risk and impact assessment8.4Management system design8.5Assurance case8.6Monitoring and MLOps plan8.7Audit plan8.8Conclusions8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
100% online training on SEIUM's 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 it fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MLflow, DVC, IBM AI Fairness 360 and Python (SHAP), in free, academic or evaluation versions. The practical work is based on real procedures: regulatory classification of autonomous systems, preparation of assurance cases and audits of machine learning pipelines. Assessment is continuous by module, plus the final project.
Standards covered: Regulation (EU) 2024/1689 (AI Act), ISO/IEC 42001:2023, NIST AI RMF 1.0, ISO/IEC 23894:2023, ISO 21448:2022, UL 4600, IEC 61508 and ISO/PAS 8800:2024.
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
- Prior technical training or professional experience in the sector. A university degree is not required.
- Internet connection and a computer able to run 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 800 hours over 6–8 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.
You work with MLflow, DVC, IBM AI Fairness 360 and Python (SHAP), 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 final project. 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 are recognised in a higher programme in the same area (postgraduate diploma or master's). Ask about the recognition policy when you enrol.
Prior technical training or professional experience in the sector (no university degree required), a computer able to run the 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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