AI Governance: Frameworks and Best Practices

Dates: 23rd – 27th November 2026

COURSE DURATION 5 DAYS

Frameworks, Policies, Ethics, and Governance Controls for Responsible and Shadow AI

Course Introduction

Artificial Intelligence (AI) is reshaping industries, economies, and governance at a scale unmatched by any previous technological revolution. As AI becomes increasingly central to decision-making and automation, organizations face growing pressure to implement systems that are not only effective but also ethical, transparent, and compliant. A strong governance structure is essential to ensure that AI deployment aligns with corporate values, public trust, and evolving global regulations.

This AI Governance Course that highlights Shadow AI, provides a practical foundation for developing, implementing, and managing responsible AI governance systems. Participants will explore the frameworks, standards, and best practices required to mitigate risks, ensure accountability, and promote ethical innovation. The AI Governance Training Course focuses on balancing innovation with oversight — helping organizations maintain compliance while leveraging AI’s transformative potential.

By combining real-world case studies, international standards, and structured frameworks, this AI Governance Frameworks Training enables professionals to build governance models that are scalable, auditable, and future-ready. Participants will learn how to establish robust policies, assess AI risks, and foster responsible innovation that supports long-term business objectives and public confidence.

Key Highlights:

• Understand the principles and pillars of responsible AI governance
• Develop AI frameworks that ensure transparency, fairness, and compliance
• Apply leading global standards such as the EU AI Act, NIST, and ISO/IEC 42001
• Manage AI-related risks, including bias, privacy, and ethical implications
• Design organizational structures for sustainable and accountable AI adoption

Objectives of the Course:

By completing this AI Governance Training Course, participants will be able to:

• Understand the foundations of AI governance, ethics, and responsible AI practices
• Develop comprehensive AI Governance Frameworks aligned with organizational and regulatory requirements
• Identify and manage AI-related risks — technical, ethical, and operational
• Implement best practices in AI transparency, explainability, and auditability
• Align AI initiatives with international frameworks, including NIST and ISO/IEC 42001
• Establish roles, responsibilities, and oversight mechanisms for AI governance
• Conduct bias detection, impact assessments, and AI risk audits
• Lead ethical and compliant AI deployment strategies across industries
• Integrate AI governance into broader corporate sustainability and ESG objectives

Training Methodology

The AI Governance Course uses a blend of experiential learning, collaborative workshops, and case-based instruction to maximize engagement and practical retention. Facilitated by AI governance experts, the course combines structured presentations, interactive discussions, and applied exercises to illustrate how governance models are implemented in real-world contexts.

Participants will analyze global regulatory frameworks, conduct hands-on risk assessments, and draft AI governance policies tailored to their organizations. Through simulations and group projects, delegates will strengthen their ability to design governance strategies that meet compliance, performance, and ethical expectations simultaneously.

Who Should Attend?

This AI Governance Training Course is designed for professionals and decision-makers responsible for overseeing AI implementation, policy, and ethics. It is ideal for leaders aiming to balance innovation with accountability in technology-driven environments.

• Chief Officers (CTO, CIO, CDO) and Senior Executives
• AI and Data Governance Specialists
• Risk, Compliance, and Internal Audit Professionals
• Policy Makers and Government Regulators
• Legal and Regulatory Affairs Experts
• Digital Transformation Leaders and Strategists
• AI/ML Engineers transitioning to governance and ethics roles
• Consultants involved in AI risk management and responsible innovation

Course Outline

Day One: Foundations of AI Governance & Responsible AI

• Understanding AI governance: definitions, scope, and importance
• Key drivers for AI governance in the public and private sectors
• Overview of AI ethics principles: fairness, accountability, transparency, privacy
• Types of AI systems and associated governance challenges
• Case studies: governance failures (Amazon recruiting AI, COMPAS, etc.)
• Introduction to global AI governance models and frameworks
• Building the business case for responsible AI
• Workshop: Mapping AI governance needs in your organisation

Day Two: Regulatory Landscapes, Standards & Compliance Requirements

• Overview of global regulations
• AI classifications and compliance obligations
• Data protection laws and AI (GDPR, regional regulations)
• Governance requirements for high-risk AI systems
• AI documentation, transparency, and reporting obligations
• Building internal compliance frameworks
• Workshop: Conducting a regulatory impact assessment

Day Three: AI Risk Management, Bias, & Algorithmic Transparency

• Understanding AI risks: technical, operational, ethical, and societal
• Bias detection, fairness assessment, and mitigation strategies
• Explainable AI (XAI) methods and tools
• Governance for generative AI models and large language models
• AI model lifecycle management and monitoring
• Risk registers, AI control checkpoints, and audit trails
• AI system testing and validation frameworks
• Workshop: Conducting an AI risk assessment & bias analysis

Day Four: Designing & Implementing AI Governance Frameworks (Including Shadow AI)

• Governance structures: committees, roles, and oversight responsibilities
• Accountability models for AI ownership and decision-making

• Understanding AI Shadow: causes, organisational blind spots, and governance gaps
• Why Shadow AI emerges despite existing IT and AI policies
• Integrating Shadow AI oversight into governance structures
• AI governance frameworks: NIST, ISO, and organisational models
• Creating AI governance policies, acceptable-use policies, and standard operating procedures

• Controlling employee use of public and generative AI tools
• Procurement governance: evaluating and approving third-party AI vendors
• Managing Shadow AI in SaaS platforms and embedded AI tools
• Human-in-the-loop (HITL) and human-on-the-loop (HOTL) controls
• Incident response and escalation procedures for Shadow AI misuse or failure
• Building governance for generative AI & autonomous systems

Day Five: Strategy, Maturity Models & Future Trends

• Developing an enterprise AI governance strategy
• AI maturity assessments and roadmap development
• Aligning AI governance with organisational values and ESG goals
• Integrating AI governance into digital transformation programs
• Preparing for future trends: autonomous systems, AGI, and next-gen regulations
• Capstone exercise: Designing a complete AI governance blueprint
• Certificate examination/assessment
• Closing session: Action plan for AI governance implementation

Certificate

• On successful completion of this training course, the ALARDI Africa Certificate will be issued to the participants.

Venue:

Kenya-Nairobi                               Cost USD 1800

Egypt-Cairo                                    Cost USD 2100

Singapore City, Singapore           Cost USD 3500

Dates: 23^rd – 27^th November 2026