Diploma in Conformity Assessment and AI Audits
Online postgraduate diploma of 800 hours in conformity assessment and AI audits: 8 modules covering conformity assessment, management system audits, technical model audits, bias and fairness, transparency, robustness and cybersecurity, audits of generative models, and audit reporting and management, 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
Describe the conformity assessment procedures of the AI Act.
Plan and carry out management system audits according to ISO 19011.
Perform technical audits of models and their data.
Assess bias and fairness with suitable metrics and tools.
Check transparency, robustness and cybersecurity requirements.
Analyse the specific obligations of generative models.
Write audit reports with findings and remediation plans.
Who it's for
- AI systems auditors.
- Conformity assessors.
- Regulatory compliance consultants.
- Algorithmic fairness specialists.
- Management system auditors extending their scope to AI.
- AI assurance leads.
- Engineering or science graduates aiming to join certification bodies.
What you'll work with
- Regulation (EU) 2024/1689 on Artificial Intelligence (EU AI Act)
- ISO/IEC 42001:2023 (AI management systems)
- ISO 19011:2018 (guidelines for auditing management systems)
- ISO/IEC 42006:2025 (requirements for bodies auditing AI management systems)
- ISO/IEC 17065:2012 (bodies certifying products, processes and services)
- ISO/IEC 17021-1:2015 (bodies providing management system audit and certification)
- ISO/IEC TR 24027:2021 (bias in AI systems)
- ISO/IEC TR 24029-1:2021 (robustness of neural networks)
- Fairlearn
- AI Fairness 360
- Giskard
- SHAP
8 modules
1.1ISO/IEC 17000 concepts1.2Conformity assessment modules1.3Internal control according to Annex VI1.4Assessment with a notified body according to Annex VII1.5Harmonised standards and presumption of conformity1.6CE marking and EU declaration1.7Notified bodies and accreditation1.8Relationship with product legislation1.9Substantial changes and new assessment1.10Workshop: conformity route for a biometric system
2.1ISO 19011 principles2.2The audit programme2.3ISO/IEC 42001 audits2.4ISO/IEC 42006 requirements for certification bodies2.5Planning and checklists2.6Interviews and evidence sampling2.7Findings and non-conformities2.8The audit report2.9Follow-up of corrective actions2.10Practical exercise: simulated audit of an AIMS
3.1Review of the model life cycle3.2Audit of training data3.3Reproducibility of results3.4Performance metrics by subgroup3.5Robustness testing with perturbed inputs3.6Explainability analysis3.7Review of code and dependencies3.8Assessment of third-party models3.9Limitations and scope of the audit3.10Lab: technical audit of a classifier
4.1Sources of bias according to ISO/IEC TR 240274.2Parity and equal opportunity metrics4.3Incompatibility between metrics4.4Disparate impact testing4.5Mitigation in pre-processing, training and output4.6Protected attributes and sensitive data4.7The anti-discrimination legal framework4.8Fairness documentation4.9Review with affected parties4.10Lab: fairness audit with Fairlearn and AIF360
5.1Transparency and information requirements5.2Effective human oversight5.3Accuracy and its declaration5.4Adversarial attacks and poisoning5.5Assessment according to ISO/IEC 240295.6Model security and extraction5.7Red teaming tests5.8Logging and event traceability5.9Resilience to failures5.10Practical exercise: red teaming a conversational assistant
6.1Obligations for general-purpose models6.2The general-purpose AI code of practice6.3Assessing capabilities and systemic risks6.4Harmful content testing6.5Hallucinations and factuality6.6Copyright and training data6.7Marking synthetic content6.8Benchmarks and their limitations6.9Documentation for downstream providers6.10Case study: evaluation of a language model
7.1Auditor ethics and independence7.2Competences of the audit team7.3Evidence management and confidentiality7.4Grading findings7.5Writing the conformity report7.6Communication with management7.7Remediation plans7.8Follow-up audits7.9The auditor’s professional responsibility7.10Case study: audit report for a board
8.1Selection of the system and scope8.2Applicable audit criteria8.3Audit plan and programme8.4Documentary review of the technical file8.5Technical tests on the model8.6Fairness and robustness assessment8.7Findings and classification8.8Report and remediation 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 Fairlearn, AI Fairness 360, Giskard and SHAP, in free, academic or evaluation versions. Testing is studied on real procedures: simulated audits of AI management systems, fairness tests on classification models and red teaming exercises on conversational assistants. Assessment is continuous by module, plus the final capstone project.
Standards covered: Regulation (EU) 2024/1689 on Artificial Intelligence, ISO/IEC 42001:2023, ISO 19011:2018, ISO/IEC 42006:2025, ISO/IEC 17065:2012, ISO/IEC 17021-1:2015, ISO/IEC TR 24027:2021, ISO/IEC TR 24029-1:2021.
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 Fairlearn, AI Fairness 360, Giskard and 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 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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