Certified Responsible AI Governance & Ethics Professional Training in Sweden

The Certified Responsible AI Governance & Ethics Professional course is an intensive three-day programme designed to help professionals establish, manage, and oversee responsible AI governance across the entire AI lifecycle. The course includes an official certification exam voucher and combines governance theory with practical implementation techniques.

Participants will learn how to design governance structures, implement accountability frameworks, manage AI-related risks, and ensure regulatory compliance while promoting ethical and trustworthy AI systems. The programme also covers AI assurance, governance controls, auditing, testing, validation, and organisational oversight to help businesses deploy AI responsibly.

Throughout the course, learners will create practical governance artefacts that can be used in real-world organisations, including AI Governance Charters, AI Risk Registers aligned with recognised frameworks, Model Accountability Maps, and audit evidence documentation. These deliverables help organisations improve regulatory readiness, strengthen governance processes, and provide executive-level visibility into AI initiatives.

Upon completion, participants will possess the practical knowledge and governance skills required to lead Responsible AI and AI Assurance programmes across enterprise environments.

There are no formal prerequisites for attending this course.

Module 1: AI Foundations and Technology Ecosystem

• Core AI concepts and terminology
• AI system components
• AI lifecycle management
• MLOps and DataOps practices
• AI architectures and deployment models
• Industry use cases and implementation examples

Module 2: Responsible AI and Ethical Principles

• Ethical, societal, and security challenges of AI
• International AI ethics principles
• Responsible AI frameworks
• Embedding ethics into governance processes
• Operationalising trustworthy AI

Module 3: AI Strategy and Organisational Planning

• Defining organisational AI vision
• Assessing AI readiness
• Building AI roadmaps
• Prioritising AI use cases
• Aligning AI initiatives with business objectives
• Scaling AI adoption across the organisation

Module 4: AI Governance Frameworks

• Designing governance operating models
• Establishing governance structures
• Defining organisational roles and responsibilities
• Decision-making authority
• Governance policies and control mechanisms
• Lifecycle governance and oversight

Module 5: AI Regulatory Compliance

• Understanding global AI regulations
• Compliance requirements and obligations
• Accountability and liability management
• User rights and transparency
• Compliance monitoring and reporting
• Preparing for regulatory reviews and audits

Module 6: AI Risk and Threat Management

• AI-specific risks and vulnerabilities
• Risk identification and assessment
• Risk prioritisation techniques
• AI risk management frameworks
• Threat modelling
• Attack surface analysis

Module 7: Third-Party AI Risk Management

• Vendor and supplier risk assessment
• Third-party due diligence
• Contract governance
• Regulatory obligations for external AI providers
• Continuous vendor monitoring
• Third-party incident management

Module 8: AI Security Architecture

• Designing secure AI environments
• Defence-in-depth strategies
• Protecting AI models
• Securing data pipelines
• API security
• Runtime monitoring and protection controls

Module 9: Privacy, Trust, and Safety in AI

• Privacy-enhancing technologies (PETs)
• Privacy risk assessments
• Explainability and transparency mechanisms
• Fairness in AI systems
• Building trustworthy and accountable AI solutions

Module 10: AI Incident Response and Business Continuity

• Developing AI-specific incident response plans
• Threat detection and containment
• Recovery strategies
• Business continuity planning
• Disaster recovery for AI systems
• Readiness exercises and simulations

Module 11: AI Assurance, Testing, and Auditing

• AI testing and validation methodologies
• Assurance frameworks
• AI audit processes
• Developing audit evidence
• Compliance reporting
• Continuous monitoring and governance improvement

Practical Workshops

The course includes extensive hands-on exercises designed around realistic governance scenarios.

Participants will gain practical experience by:

• Developing AI Governance Charters
• Building AI Risk Registers
• Applying AI lifecycle governance practices
• Performing AI risk assessments
• Conducting threat modelling exercises
• Completing compliance and audit readiness scenarios
• Reviewing governance frameworks
• Collaborating on real-world AI governance case studies

These practical activities enable learners to confidently apply governance, assurance, compliance, and risk management principles within AI-enabled organisations.

Examination and Assessment

This programme includes structured assessments together with full preparation for the official certification examination.

EC-Council Certified Responsible AI Governance & Ethics Professional

• Official exam voucher included
• Examination duration: 3 hours
• 100 multiple-choice questions
• Passing score between 70% and 80%

Upon successful completion, learners will possess both the theoretical understanding and practical governance expertise required to achieve certification and successfully lead enterprise AI governance initiatives.

Key Benefits

• Learn how to establish enterprise AI governance programmes.
• Develop practical expertise in AI risk management and regulatory compliance.
• Understand how to implement Responsible AI and AI Assurance frameworks.
• Gain experience producing governance documentation and audit-ready evidence.
• Build knowledge of AI security, ethics, privacy, and accountability.
• Prepare for the internationally recognised EC-Council Certified Responsible AI Governance & Ethics Professional certification.
• Acquire practical governance skills that can be immediately applied to AI initiatives across public and private sector organisations.