Module 1: AI foundations and technology ecosystem
- Understand core AI concepts and system components
- Explore AI lifecycle, MLOps, and DataOps practices
- Analyse AI architectures and deployment models
- Apply AI use cases across industries
Module 2: AI concerns, ethical principles, and responsible AI
- Identify ethical, societal, and security concerns in AI
- Apply global AI ethics principles and standards
- Implement responsible AI practices across development
- Integrate ethics into governance and operations
Module 3: AI strategy and planning
- Define AI vision and organisational readiness
- Develop AI roadmaps and prioritised use cases
- Align AI initiatives with business objectives
- Manage scaling, adoption, and performance
Module 4: AI governance and frameworks
- Design AI governance structures and operating models
- Define roles, responsibilities, and decision authority
- Implement governance policies and controls
- Apply lifecycle governance and oversight mechanisms
Module 5: AI regulatory compliance
- Interpret global AI regulations and compliance requirements
- Manage accountability, liability, and user rights
- Implement compliance monitoring and reporting
- Prepare for regulatory audits and reviews
Module 6: AI risk and threat management
- Identify AI-specific threats and vulnerabilities
- Apply risk assessment and prioritisation techniques
- Implement AI risk management frameworks
- Conduct threat modelling and attack surface analysis
Module 7: Third-party AI risk management and supply chain security
- Assess risks across AI vendors and suppliers
- Conduct due diligence and contract governance
- Apply regulatory and compliance obligations
- Monitor third-party risks and incident response
Module 8: AI security architecture and controls
- Design secure AI architectures and frameworks
- Implement defence-in-depth strategies
- Secure models, data pipelines, and APIs
- Apply runtime monitoring and protection controls
Module 9: Building privacy, trust, and safety in AI systems
- Apply privacy-enhancing technologies
- Conduct privacy risk assessments
- Implement transparency and explainability mechanisms
- Ensure fairness, trust, and ethical system behaviour
Module 10: AI incident response and business continuity
- Develop AI-specific incident response plans
- Manage detection, containment, and recovery
- Implement business continuity and disaster recovery
- Conduct simulations and readiness testing
Module 11: AI assurance, testing, and auditing
- Design AI testing and validation strategies
- Conduct audits and assurance activities
- Develop audit evidence and reporting artefacts
- Ensure continuous monitoring and compliance
Hands-on learning
This course emphasises practical application through real-world scenarios and governance-focused exercises.
- Development of AI governance artefacts including charters and risk registers
- Hands-on exercises aligned to AI lifecycle governance
- Practical AI risk assessment and threat modelling activities
- Scenario-based compliance and audit readiness exercises
- Instructor-led walkthroughs of governance frameworks
- Peer discussions on real-world AI governance challenges
This hands-on approach ensures learners can apply governance, assurance, risk, and compliance principles effectively within AI-driven organisations.
Exams and assessments
This course includes structured assessments and full preparation for the certification exam.
- EC-Council Certified Responsible AI Governance and Ethics Professional exam voucher included
- Exam duration of three hours
- Total of 100 multiple-choice questions
- Passing score ranges between 70 and 80 percent
Learners will leave the course equipped with both theoretical knowledge and practical skills required to achieve certification and lead AI governance initiatives.
