Diploma in Privacy, Traceability and Applied Ethics
Online postgraduate diploma of 800 hours in privacy, traceability and applied ethics: 8 modules covering privacy and data protection, privacy by design, impact assessments, data and model traceability, applied AI ethics, transparency and explainability, and ethical governance in the organisation, with an assigned technical tutor.
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
Apply the GDPR and the LOPDGDD to AI-based processing.
Embed privacy by design in data systems.
Carry out data protection and fundamental rights impact assessments.
Implement end-to-end traceability of data and models.
Analyse ethical dilemmas using recognised frameworks and principles.
Design suitable transparency and explainability mechanisms.
Establish organisational governance for responsible AI.
Who it's for
- Data protection officers.
- Responsible AI leads.
- Privacy consultants.
- MLOps engineers who need traceable, compliant pipelines.
- Technology ethics analysts.
- Compliance auditors.
- Engineering or science graduates aiming to enter the healthcare sector.
What you'll work with
- Regulation (EU) 2016/679 (GDPR) (General Data Protection Regulation)
- Organic Law 3/2018 (LOPDGDD) (Spanish data protection and digital rights law)
- Regulation (EU) 2024/1689 on Artificial Intelligence (EU AI Act)
- ISO/IEC 27701:2019 (privacy information management)
- ISO/IEC 20889:2018 (privacy-enhancing de-identification techniques)
- ISO/IEC 29100:2011 (privacy framework)
- IEEE 7000-2021 (addressing ethical concerns during system design)
- ISO/IEC 42001:2023 (AI management systems)
- MLflow
- DVC
- ARX Data Anonymization Tool
- SHAP
- DiCE
8 modules
1.1Principles of Article 5 of the GDPR1.2Lawful bases for processing1.3Special categories of data1.4Data subject rights1.5Automated decisions and Article 221.6Controller, processor and joint controllers1.7International data transfers1.8Specific features of the LOPDGDD1.9The role of the AEPD (Spanish Data Protection Agency)1.10Workshop: record of processing activities for an AI system
2.1Privacy by design and by default2.2Data minimisation and purpose limitation2.3Pseudonymisation and anonymisation2.4ISO/IEC 20889 techniques2.5Re-identification risk2.6Synthetic data and its limits2.7Federated learning as a measure2.8Access control and encryption2.9Retention and erasure2.10Practical exercise: anonymisation of a clinical dataset
3.1When a DPIA is mandatory3.2The AEPD methodology3.3Necessity and proportionality3.4Risks to rights and freedoms3.5Mitigation measures3.6Fundamental rights impact under the AI Act3.7Prior consultation with the authority3.8Periodic review of the assessment3.9Integration with AI risk management3.10Practical exercise: DPIA for an intelligent video surveillance system
4.1Data lineage from source to prediction4.2Data versioning with DVC4.3Experiment tracking with MLflow4.4Metadata and the W3C PROV standard4.5System event logs4.6Reproducibility of training runs4.7The model supply chain4.8Model cards and datasheets4.9Auditing traceability4.10Lab: end-to-end lineage with DVC and MLflow
5.1EU ethics principles for trustworthy AI5.2The UNESCO Recommendation on the Ethics of AI5.3Ethical theories applied to technical decisions5.4Autonomy, justice and non-maleficence5.5Value-based design according to IEEE 70005.6Dilemmas in automated decision-making5.7Ethics committees and review5.8Participation of affected parties5.9Social and environmental impact5.10Case study: dilemmas of a triage system
6.1Global and local explainability6.2LIME, SHAP and counterfactual explanations6.3Explanations users can understand6.4The right to explanation and its scope6.5Transparency of generative systems6.6Communicating uncertainty6.7Limits of post hoc explanations6.8Assessing the quality of explanations6.9Decision logging6.10Lab: counterfactual explanations with DiCE
7.1Responsible AI use policies7.2The roles of the DPO and the AI officer7.3ISO/IEC 27701 and ISO/IEC 42001 management systems7.4Managing ethical and privacy incidents7.5Whistleblowing channels7.6Training and culture7.7Responsible AI indicators7.8Relationship with suppliers7.9Accountability7.10Case study: a company’s responsible AI policy
8.1Description of the system and processing operations8.2Analysis of lawfulness and purposes8.3Impact assessment8.4Design of privacy measures8.5Data and model traceability plan8.6Ethical analysis with stakeholders8.7Transparency mechanisms8.8Governance and monitoring plan8.9Report and presentation8.10Final project: final review with the tutor
How you'll learn
100% online training 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 it fits around your job.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is MLflow, DVC, ARX Data Anonymization Tool, SHAP and DiCE, in free, academic or evaluation versions. Testing is studied on real procedures: impact assessments of AI-based processing, anonymisation of sensitive datasets and reconstruction of the lineage of a model in production. Assessment is continuous by module, plus the final capstone project.
Standards covered: Regulation (EU) 2016/679 (GDPR), Organic Law 3/2018 (LOPDGDD), Regulation (EU) 2024/1689 on Artificial Intelligence, ISO/IEC 27701:2019, ISO/IEC 20889:2018, ISO/IEC 29100:2011, IEEE 7000-2021, ISO/IEC 42001:2023.
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. No university degree is required.
- An internet connection and a computer capable of running 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, ARX Data Anonymization Tool, SHAP and DiCE, 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 programme 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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