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Diploma in Risk Management and Regulatory Documentation

Online postgraduate diploma of 800 hours in risk management and regulatory documentation: 8 modules covering AI risk management, the European AI regulatory framework, risk assessment and treatment, the quality management system, regulatory technical documentation, regulated sectors, and post-market monitoring and continuous improvement, with an assigned technical tutor.

Duration6–8 months
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Apply the ISO 31000 and ISO/IEC 23894 processes to AI systems.

02

Classify AI systems under the European regulation.

03

Assess and treat technical, ethical and fundamental rights risks.

04

Design a quality management system in line with ISO/IEC 42001.

05

Prepare the technical documentation required for high-risk systems.

06

Adapt risk management to specific regulated sectors.

07

Plan post-market monitoring and continuous improvement.

Profile

Who it's for

  • AI compliance officers.
  • Technology risk managers.
  • Regulatory consultants advising on AI.
  • Software quality managers.
  • Internal AI auditors.
  • Regulatory affairs specialists.
  • Engineering or science graduates aiming to enter financial services.
Standards & tools

What you'll work with

  • Regulation (EU) 2024/1689 on Artificial Intelligence (EU AI Act)
  • ISO/IEC 23894:2023 (AI risk management)
  • ISO/IEC 42001:2023 (AI management systems)
  • ISO 31000:2018 (risk management guidelines)
  • NIST AI RMF 1.0 (AI risk management framework)
  • ISO/IEC 5338:2023 (AI system life cycle processes)
  • ISO 14971:2019 (risk management for medical devices)
  • Regulation (EU) 2016/679 (GDPR) (General Data Protection Regulation)
  • OneTrust
  • IBM watsonx.governance
  • Confluence
  • Jira
Syllabus

8 modules

  1. 1.1ISO 31000 principles applied to AI
  2. 1.2The ISO/IEC 23894 process
  3. 1.3NIST AI RMF functions
  4. 1.4Identifying hazards and harms
  5. 1.5Risks to fundamental rights
  6. 1.6Risk appetite and tolerance
  7. 1.7Stakeholders and context
  8. 1.8The AI risk register
  9. 1.9Integration with corporate risk management
  10. 1.10Workshop: risk register for a recruitment system
  1. 2.1Structure of Regulation (EU) 2024/1689
  2. 2.2Prohibited AI practices
  3. 2.3Classification of high-risk systems
  4. 2.4Obligations of providers and deployers
  5. 2.5General-purpose AI models
  6. 2.6Application timeline
  7. 2.7Harmonised standards and common specifications
  8. 2.8Relationship with the GDPR and sector legislation
  9. 2.9Penalties and market surveillance authorities
  10. 2.10Case study: risk classification of five systems
  1. 3.1Qualitative and quantitative analysis
  2. 3.2Likelihood and severity matrices
  3. 3.3Failure mode analysis in AI
  4. 3.4Assessing bias and discrimination
  5. 3.5Cybersecurity risks in models
  6. 3.6Technical and organisational mitigation measures
  7. 3.7Residual risk and acceptance
  8. 3.8Human oversight as a measure
  9. 3.9Fundamental rights impact assessment
  10. 3.10Practical exercise: risk treatment for a credit model
  1. 4.1Requirements of Article 17 of the AI Act
  2. 4.2The ISO/IEC 42001 management system
  3. 4.3ISO/IEC 5338 life cycle processes
  4. 4.4Data management and governance
  5. 4.5Change and version control
  6. 4.6Managing suppliers and components
  7. 4.7Quality indicators
  8. 4.8Corrective and preventive actions
  9. 4.9Internal audits of the quality system
  10. 4.10Workshop: process map of an AI provider
  1. 5.1Content of Annex IV of the AI Act
  2. 5.2System description and intended purpose
  3. 5.3Documentation of data and governance
  4. 5.4Performance metrics and limitations
  5. 5.5Instructions for use for deployers
  6. 5.6Model cards and datasheets for datasets
  7. 5.7Automatic logging and traceability
  8. 5.8EU declaration of conformity
  9. 5.9Document management and versioning
  10. 5.10Practical exercise: technical file for a high-risk system
  1. 6.1Medical device software and ISO 14971
  2. 6.2IEC 62304 and clinical validation
  3. 6.3AI in financial services
  4. 6.4AI in human resources
  5. 6.5AI in critical infrastructure
  6. 6.6CE-marked products with embedded AI
  7. 6.7Applicable cybersecurity requirements
  8. 6.8Post-market monitoring system
  9. 6.9Reporting serious incidents
  10. 6.10Case study: technical file for AI-based medical software
  1. 7.1Post-market monitoring plan
  2. 7.2Risk indicators and alerts
  3. 7.3Drift and performance monitoring
  4. 7.4Managing incidents and complaints
  5. 7.5Reassessment after substantial modifications
  6. 7.6Relationship with market surveillance authorities
  7. 7.7Transparent risk communication
  8. 7.8AI training and literacy
  9. 7.9Management review
  10. 7.10Case study: monitoring plan for a customer service chatbot
  1. 8.1System description and context
  2. 8.2Justified regulatory classification
  3. 8.3Risk identification and analysis
  4. 8.4Treatment plan and controls
  5. 8.5Structure of the quality system
  6. 8.6Summary technical file
  7. 8.7Post-market monitoring plan
  8. 8.8Conclusions and accepted risks
  9. 8.9Report and presentation
  10. 8.10Final project: final review with the tutor
Methodology, software & labs

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 OneTrust, IBM watsonx.governance, Confluence and Jira, in free, academic or evaluation versions. Testing is studied on real procedures: preparing technical files for high-risk systems, fundamental rights impact assessments and post-market monitoring plans. Assessment is continuous by module, plus the final capstone project.

Standards covered: Regulation (EU) 2024/1689 on Artificial Intelligence, ISO/IEC 23894:2023, ISO/IEC 42001:2023, ISO 31000:2018, NIST AI RMF 1.0, ISO/IEC 5338:2023, ISO 14971:2019, Regulation (EU) 2016/679 (GDPR).

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of Regulation (EU) 2024/1689, ISO/IEC 23894:2023 and ISO/IEC 42001:2023 applied to real cases, not as a theoretical annex.
Industry software:OneTrust, IBM watsonx.governance, Confluence and Jira, in free, academic or evaluation versions.
Final project:a risk management project for a real AI system, with justified regulatory classification, a risk treatment plan, a summary technical file and a post-market monitoring plan, reviewed by your tutor.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

System and classification
Describe the AI system and its context and justify its regulatory classification.
Risk analysis
Identify and analyse the technical, ethical and fundamental rights risks.
Treatment and quality system
Define the treatment plan, controls and structure of the quality system.
Technical file and monitoring
Draft the summary technical file and the post-market monitoring plan.
Final project
Present the conclusions, accepted risks and final report.
Career opportunities

Where this takes you

AI compliance officerTechnology risk managerRegulatory consultantSoftware quality managerInternal AI auditorRegulatory affairs specialist
Admissions & scholarships

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.

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FAQ

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 OneTrust, IBM watsonx.governance, Confluence and Jira, 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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