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Postgraduate Diploma in Data Compliance and Predictive Model Auditing

Online 800-hour Postgraduate Diploma in Data Compliance and Predictive Model Auditing. 8 modules covering Regulation (EU) 2016/679 (GDPR), Regulation (EU) 2024/1689 (Artificial Intelligence Act) and Python (Fairlearn, SHAP). Assigned tutor and open enrolment.

Duration800 h
ModeOnline
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Interpret the compliance framework for data and artificial intelligence.

02

Govern the lifecycle of predictive models.

03

Require sufficient documentation and traceability to audit a model.

04

Carry out independent model validations.

05

Assess fairness, explainability and robustness.

06

Plan and execute a model audit with evidence.

07

Establish monitoring and incident response for predictive models.

Profile

Who it's for

  • Model auditor wishing to specialise in Data Compliance and Predictive Model Auditing.
  • Data protection officer wishing to specialise in Data Compliance and Predictive Model Auditing.
  • AI compliance officer wishing to specialise in Data Compliance and Predictive Model Auditing.
  • Model validator wishing to specialise in Data Compliance and Predictive Model Auditing.
  • Internal systems auditor wishing to specialise in Data Compliance and Predictive Model Auditing.
  • Data governance consultant wishing to specialise in Data Compliance and Predictive Model Auditing.
  • Engineering or science graduates seeking to join insurance companies.
Standards & tools

What you'll work with

  • Regulation (EU) 2016/679 (GDPR)
  • Regulation (EU) 2024/1689 (Artificial Intelligence Act)
  • Organic Law 3/2018 (LOPDGDD)
  • ISO/IEC 42001 (AI management systems)
  • ISO/IEC 23894 (AI risk management)
  • ISO 19011 (management systems auditing)
  • SR 11-7 (model risk management)
Syllabus

8 modules · 800 h

  1. 1.1Principles of the GDPR applied to models 1.2 legal bases for training 1.3 data minimisation and purpose limitation 1.4 automated decisions under article 22 1.5 regulation (EU) 2024/1689 on AI 1.6 High-Risk Systems in Insurance and Credit 1.7 EIOPA Guidelines on AI Governance 1.8 Supervision by The Banco de España and the DGSFP 1.9 Interaction between the GDPR and the AI Regulation 1.10 Practical Case: Regulatory Inventory of a Scoring Model
  1. 2.1Corporate model inventory
  2. 2.2Risk-based model classification
  3. 2.3Roles of owner, developer and validator
  4. 2.4Three lines of defence
  5. 2.5Model risk management policy
  6. 2.6SR 11-7 guidance as a reference
  7. 2.7Approval and deployment to production
  8. 2.8Change and version management
  9. 2.9Retirement of obsolete models
  10. 2.10Workshop: model risk policy
  1. 3.1Technical documentation under Annex IV
  2. 3.2Model and data sheets
  3. 3.3Lineage of training data
  4. 3.4Experiment tracking with MLflow
  5. 3.5Reproducibility of results
  6. 3.6Automatic event logs
  7. 3.7Data version control with DVC
  8. 3.8Audit evidence
  9. 3.9Retention of documentation
  10. 3.10Practice: model sheet and lineage with MLflow
  1. 4.1Conceptual review of the model
  2. 4.2Performance and stability testing
  3. 4.3Backtesting and benchmarking
  4. 4.4Sensitivity analysis
  5. 4.5Stress testing
  6. 4.6Input data validation
  7. 4.7Population stability index
  8. 4.8Source code review
  9. 4.9Validation report and findings
  10. 4.10Laboratory: validation of a propensity model
  1. 5.1Detection of sensitive proxy variables
  2. 5.2Group fairness metrics
  3. 5.3Local explanations with SHAP
  4. 5.4Counterfactual explanations for the customer
  5. 5.5Robustness to perturbed data
  6. 5.6Adversarial attacks on tabular models
  7. 5.7Effective human oversight
  8. 5.8Accuracy and cybersecurity under Article 15
  9. 5.9Management of residual bias
  10. 5.10Practice: fairness audit with Fairlearn
  1. 6.1Planning the algorithmic audit
  2. 6.2ISO/IEC 42001 and ISO/IEC 23894 standards
  3. 6.3Internal versus external audit
  4. 6.4Substantive testing on data
  5. 6.5Sampling of automated decisions
  6. 6.6Interviews and process review
  7. 6.7Fundamental rights impact assessment
  8. 6.8Findings and recommendations
  9. 6.9Follow-up of corrective actions
  10. 6.10Practical case: audit of an underwriting model
  1. 7.1Data drift monitoring
  2. 7.2Performance degradation in production
  3. 7.3Alerts and intervention thresholds
  4. 7.4Ongoing fairness monitoring
  5. 7.5Serious incidents and notification
  6. 7.6Handling customer complaints
  7. 7.7Mandatory periodic reviews
  8. 7.8Evidently and WhyLabs tools
  9. 7.9Reports to the risk committee
  10. 7.10Workshop: post-deployment monitoring plan
  1. 8.1Selection of the predictive model
  2. 8.2Map of applicable requirements
  3. 8.3Review of documentation and lineage
  4. 8.4Validation tests
  5. 8.5Fairness and explainability assessment
  6. 8.6Impact assessment
  7. 8.7Audit report
  8. 8.8Remediation plan
  9. 8.9Final report and presentation
  10. 8.10Final review with the tutor
Methodology, software & labs

How you'll learn

Training is 100 % online on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the programme with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (Fairlearn, SHAP), Great Expectations, Evidently AI, R, Collibra, in free, academic or evaluation versions. Practice is based on real procedures for a full audit of a predictive model, fairness and robustness tests, and review of model documentation and traceability. Assessment is continuous by module, plus the final integrating 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 experience in training, , eVTOL/UAM and flight test.
Flexible format(hybrid/online), international cohorts and support from SEIUM Career Services.
Ethics and safety:safety-by-design approach, ciber-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 predictive model

Interpret the compliance framework for data and artificial intelligence.

Map of applicable requirements

Govern the lifecycle of predictive models.

Review of documentation and lineage

Require sufficient documentation and traceability to audit a model.

Validation tests

Carry out independent model validations.

Fairness and explainability assessment

Assess fairness, explainability and robustness.

Career opportunities

Where this takes you

Model auditorData protection officerAI compliance officerModel validatorInternal systems auditorData governance consultant
Admissions & scholarships

Requirements

  • Prior technical training or professional experience in the sector. No university degree is required.
  • Internet connection and a computer capable of running the programme's software.
  • A level of Spanish 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.

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FAQ

Frequently asked questions

The programme comprises 800 hours in total, spread over 6 to 8 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.

The programme uses Python (Fairlearn, SHAP), Great Expectations, Evidently AI, R, Collibra. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.

An own diploma from SEIUM upon passing the continuous assessment and the integrating project. It is in-house training, not an official Ministry title.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised towards the advanced programme. Consult the recognition policy when you enrol.

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