AI Governance, Ethics and Responsible AI

The programme

Programme overview

As organisations adopt AI more widely, governing its use responsibly has become a board-level concern, not just a technical one. This AI Governance, Ethics and Responsible AI course builds practical frameworks for AI governance, ethical use, data privacy, and risk management — equipping participants to establish policies and oversight structures that allow their organisation to adopt AI confidently and defensibly.

The regulatory landscape is moving fast: the EU AI Act’s enforcement for high-risk systems begins in 2026, carrying penalties of up to €35 million or 7% of global turnover, while ISO/IEC 42001 has emerged as the first certifiable international AI management system standard and the NIST AI Risk Management Framework as the de facto US benchmark. This AI Governance, Ethics and Responsible AI course treats these as a layered governance architecture organisations need to satisfy together, not separate compliance exercises. Case-study driven, using real-world AI governance failures and near-misses as discussion anchors, you’ll leave with a draft AI governance policy framework built for your own organisation.

Who should attend

Who is this programme for?

This AI Governance, Ethics and Responsible AI course is designed for:

• Risk, Compliance, and Legal Professionals Responsible for AI-Related Policy
• Senior Managers and Executives Sponsoring or Overseeing AI Initiatives
• IT Governance and Data Protection Officers
• Board and Audit Committee Members Seeking AI Governance Literacy

Learning outcomes

What you will learn

This AI Governance, Ethics and Responsible AI course equips you to design, implement, and defend AI governance frameworks that satisfy global regulatory expectations while remaining proportionate to your organisation’s scale and risk appetite.

By the end of this AI Governance, Ethics and Responsible AI course, you’ll be able to:

• Apply leading AI governance frameworks to design organisational AI policy
• Navigate the global AI regulatory landscape, including the EU AI Act, ISO/IEC 42001, and the NIST AI Risk Management Framework
• Identify and assess ethical risks in AI deployment, including bias, transparency, and accountability
• Apply data privacy and protection principles specific to AI systems and training data
• Design oversight structures and approval processes for AI initiatives
• Apply a risk-tiered approach to AI oversight, including ongoing monitoring and incident response
• Build a practical AI governance policy appropriate to their organisation’s scale and risk appetite
• Prepare for evolving regulatory requirements without over-engineering governance for smaller-scale AI use
• Regulatory Clarity in a Moving Landscape: Understand how the EU AI Act, ISO 42001, and NIST AI RMF fit together, instead of treating each as a separate compliance exercise.
• Defensible AI Adoption: Build oversight structures that let your organisation adopt AI confidently, not fearfully.
• Real Risk Recognition: Identify bias, transparency, and accountability risks before they become incidents or headlines.
• Privacy-Ready Governance: Apply data privacy principles specific to AI systems and training data, not generic data protection theory.
• A Policy You Can Actually Use: Leave with a draft AI governance policy tailored to your organisation’s scale and risk appetite.

Explore the programme

Detailed programme outline

Module 1: Foundations of AI Governance

• Why AI requires distinct oversight from traditional IT governance
• The board-level shift: AI governance as a strategic, not purely technical, concern
• Core principles of responsible AI: fairness, accountability, transparency, and safety
• Building the business case for proactive AI governance

Practical Exercise: Map your organisation’s current AI governance gaps against core governance principles.

Module 2: The Global AI Regulatory Landscape

• The EU AI Act: risk-based obligations, enforcement timeline, and penalties
• ISO/IEC 42001 as a certifiable AI management system standard
• The NIST AI Risk Management Framework and its role in US and global practice
• Using the three frameworks together as a layered governance architecture, not competing alternatives

Practical Exercise: Map which regulatory frameworks apply to your organisation’s AI use, and identify overlapping requirements.

Module 3: Ethical Risks in AI

• Identifying bias in AI systems and its business and reputational consequences
• Fairness considerations across different AI use cases
• Transparency and explainability: what stakeholders are entitled to know
• Accountability structures when AI systems cause harm or error

Practical Exercise: Assess a sample AI use case for bias, fairness, and transparency risks.

Module 4: Data Privacy and Protection in AI Systems

• Privacy considerations specific to training data and model inputs
• Consent and data subject rights in AI-driven decision-making
• Cross-border data transfer considerations for AI systems
• Common data privacy failures in AI deployments and how to avoid them

Practical Exercise: Review a sample AI system for data privacy compliance gaps.

Module 5: Designing AI Oversight Structures

• Structuring approval processes for new AI initiatives
• Defining roles and responsibilities across risk, compliance, and business functions
• Setting escalation pathways for high-risk AI decisions
• Embedding AI oversight into existing governance structures rather than building from scratch

Practical Exercise: Design an approval workflow for a new AI initiative at your organisation.

Module 6: Risk Tiering and Ongoing Monitoring

• Applying a risk-based tiering approach to AI use cases
• Setting monitoring requirements proportional to risk tier
• Post-deployment monitoring and incident reporting practices
• Keeping governance proportionate as AI use scales across the organisation

Practical Exercise: Build a risk tiering matrix and assign monitoring requirements to a set of sample AI use cases.

Module 7: Building an AI Governance Policy — Capstone Exercise

• Structuring a complete AI governance policy document
• Tailoring policy scope and rigour to organisational size and risk appetite
• Presenting a governance policy for leadership sign-off
• Planning for policy review and updates as regulation and technology evolve

Practical Exercise: Draft an AI governance policy framework tailored to your own organisation.

Governance Lens: Relevant South African Requirements

Delegates apply the following requirements proportionately to the selected use case. This is an implementation lens, not legal advice; sector-specific obligations must still be confirmed by the organisation.

• POPIA (Act 4 of 2013)
• Why it matters: Personal information used for prompting, training, decisions, reporting or retrieval must be lawfully and securely processed.
• Control designed in class: Data minimisation, permission checks, confidentiality, retention and human approval rules.
• Cybercrimes Act 19 of 2020
• Why it matters: AI-enabled workflows can increase exposure to unauthorised access, data misuse or harmful disclosure.
• Control designed in class: Access boundaries, incident escalation, secure use rules and logging expectations.
• ECTA (Act 25 of 2002)
• Why it matters: Digital records and electronic transactions require sound integrity and control practices.
• Control designed in class: Verification, version control, sign-off and evidence retention for AI-assisted outputs.
• Employment / Sector Regulation
• Why it matters: High-impact use cases in hiring, lending, insurance, procurement or public services require additional fairness and accountability checks.
• Control designed in class: Use-case-specific red flags, human decision ownership and legal review gates.
• AI Policy Development
• Why it matters: South Africa has an AI policy framework; the 2026 draft policy was withdrawn and is not enacted AI law.
• Control designed in class: Monitor policy developments without presenting draft material as mandatory law.

Learning approach

How you will learn

Our diverse instructional approaches ensure effective learning:

– Lectures & Presentations: Engage with expert-driven, stimulating content.– Course Material: Access well-crafted supporting resources.– Group Work: Collaborate on discussions and case studies for practical insights.– Workshops & Role-Play: Participate in immersive, scenario-based activities.– Practical Application: Focus on applying theoretical knowledge in real situations.– Post-Training Support: Receive extensive support after training for skill implementation.

Discuss your requirements

Dates, duration & arrangements

Contact Ratomac to confirm dates, duration, delivery format, entry requirements and any applicable assessment or certification before booking.