Certified Responsible AI Governance & Ethics Professional

Overview

The Certified Responsible AI Governance and Ethics Professional course is a three-day, expert-led programme, with exam voucher included, designed to equip professionals with the knowledge and practical skills required to govern AI systems responsibly across their lifecycle. The course focuses on policy, oversight, controls, compliance, and assurance to ensure AI systems are ethical, secure, and accountable.

Aligned to the certification exam, this course prepares learners to implement governance structures, define decision authority, and operationalise ethical AI principles. Participants will gain hands-on experience in AI risk management, regulatory compliance, testing, validation, and auditing, enabling them to lead AI governance and AI Assurance initiatives across organisations.

Learners will develop practical governance and assurance artefacts such as AI governance charters, risk registers aligned to recognised frameworks, model accountability maps, and audit evidence templates. These outputs support audit readiness, regulatory compliance, and executive-level oversight.

Prerequisites

There are no formal prerequisites for this course

Target audience

This course is designed for professionals responsible for ensuring AI systems are ethical, compliant, and accountable. It is particularly suitable for:

• Governance, assurance, risk, and compliance leaders and managers
• Risk management and enterprise risk professionals
• Compliance and regulatory affairs specialists
• Chief privacy officers and data protection officers
• Internal audit and technology audit professionals
• Chief AI officers and AI governance leaders
• AI risk managers and AI ethics specialists
• AI auditors and AI assurance professionals
• AI programme managers and lifecycle managers
• AI security architects and policy advisors

Objectives

By the end of this course, learners will be able to:

• Build and implement enterprise AI governance frameworks
• Identify, assess, and mitigate AI risks across the lifecycle
• Apply ethical, transparent, and accountable AI assurance practices
• Map AI programmes to recognised frameworks and regulations
• Coordinate governance across legal, technical, and risk teams
• Develop audit-ready governance artefacts and documentation

Outline

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.