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AI Governance, Ethics & Risk Management Training Course

The AI Governance, Ethics & Risk Management Training Course provides a strategic and practical framework for enabling organizations to develop, deploy, and manage artificial intelligence systems in a responsible,…

AIGO · Artificial Intelligence in GovernmentAll LevelsClassroomEnglish , Arabic
Duration
5 Days
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Sectors:Government & Public SectorCategories:Artificial Intelligence in Government

Course Overview

The AI Governance, Ethics & Risk Management Training Course provides a strategic and practical framework for enabling organizations to develop, deploy, and manage artificial intelligence systems in a responsible, secure, transparent, and accountable manner. The course is designed for government entities, ministries, public-sector organizations, banks, financial institutions, oil and gas organizations, and large corporations seeking to balance the opportunities of AI adoption with the ethical, operational, technological, regulatory, and organizational risks it can introduce. Responsible AI adoption requires more than technical implementation. Organizations need clear governance structures, defined accountability, effective risk management processes, appropriate policies, human oversight, and mechanisms for monitoring AI systems throughout their lifecycle. This course explores how to establish an integrated governance approach covering AI strategy, use-case approval, system development and procurement, deployment, monitoring, review, and continuous improvement. The program covers the fundamental principles of AI governance and ethics, including transparency, explainability, fairness, non-discrimination, privacy, information security, accountability, human oversight, and responsible use. It also addresses AI risk management, including pre-deployment risk assessment, risk classification, model evaluation, data governance, testing, performance monitoring, and ongoing risk control. Particular emphasis is placed on practical challenges organizations face when implementing AI, including data quality and bias, limited explainability, inappropriate model use, Generative AI risks, over-reliance on automated decisions, operational risks, cybersecurity concerns, and regulatory considerations. Participants will explore how governance and risk management can be embedded into the full AI lifecycle rather than treated as separate compliance activities. Through case studies, risk assessment exercises, governance framework development, policy design, AI use-case analysis, control mapping, and executive workshops, participants will develop the capabilities required to establish an effective AI Governance, Ethics & Risk Management framework that supports responsible AI adoption, organizational trust, informed decision-making, and sustainable digital transformation.

Learning Objectives

  • Analyze the fundamental principles of AI governance and responsible artificial intelligence.
  • Assess ethical, technological, operational, organizational, and regulatory risks associated with AI systems.
  • Develop an integrated organizational framework for AI governance.
  • Apply principles of transparency, explainability, fairness, and accountability to AI use cases.
  • Evaluate data quality, bias, privacy, cybersecurity, and information security risks associated with AI.
  • Design clear governance roles, responsibilities, decision rights, and accountability mechanisms.
  • Apply methodologies for identifying, assessing, classifying, and treating AI risks throughout the AI lifecycle.
  • Develop appropriate governance controls for Generative AI and high-impact AI applications.
  • Design continuous monitoring mechanisms for detecting model performance issues, emerging risks, and unintended outcomes.
  • Develop organizational policies and procedures for safe and responsible AI use.
  • Strengthen organizational capabilities for managing AI-related risks and governance decisions.
  • Establish performance and risk indicators for measuring the effectiveness of AI governance and risk management.

Who Should Attend

This training course is designed for executives, AI and digital transformation leaders, information technology managers, data and analytics leaders, governance and risk professionals, compliance specialists, cybersecurity professionals, privacy professionals, and managers responsible for developing, procuring, implementing, or overseeing artificial intelligence systems. It is particularly relevant to government entities, ministries, public-sector organizations, banks, financial institutions, oil and gas organizations, and large corporations that use or plan to use AI for public services, business operations, decision support, data analytics, automation, customer services, or organizational transformation. The program will also benefit members of risk, governance, compliance, and AI committees, digital product and project managers, data scientists, internal auditors, legal and regulatory professionals, information security specialists, and decision makers involved in assessing, approving, monitoring, or controlling AI applications.

Learning Outcomes

  • Explain the core principles of AI governance, ethics, and responsible AI.
  • Assess and classify risks associated with AI use cases according to their potential impact.
  • Design an organizational AI governance framework with clear roles, responsibilities, and accountability.
  • Apply transparency, explainability, fairness, and non-discrimination principles when evaluating AI systems.
  • Identify data quality, privacy, security, bias, and model integrity risks.
  • Conduct structured assessments of AI use cases before approval, procurement, development, or deployment.
  • Develop policies and controls for the responsible and secure use of Generative AI.
  • Design human oversight and review mechanisms for AI-supported decision-making.
  • Apply approaches for monitoring AI model performance, risks, and outcomes throughout the system lifecycle.
  • Develop AI risk registers and control matrices to document risks, mitigation measures, and governance decisions.
  • Develop response approaches for AI incidents, failures, and emerging risks.
  • Establish KPIs and risk indicators for monitoring AI governance effectiveness.
  • Integrate governance, ethics, and risk management into the AI system lifecycle.

Course Outline

Course Outline:

Day 1

AI Governance Foundations & Responsible AI

  • The concept and strategic role of AI governance
  • Principles of responsible artificial intelligence
  • Governance roles, responsibilities, accountability, and decision rights
  • AI system lifecycle and governance checkpoints
  • Transparency, explainability, and accountability
  • Fairness, non-discrimination, and responsible AI practices
  • Practical application: Assess an AI use case and identify its governance requirements
Day 2

AI Ethics, Data Governance & Privacy

  • Ethical principles in AI design, development, procurement, and use
  • Bias, discrimination, and fairness risks in AI data and models
  • Data quality, integrity, ownership, and governance
  • Data privacy and protection considerations
  • Transparency and disclosure of AI use
  • Human oversight and responsible decision-making
  • Practical application: Conduct an ethical, data, and privacy risk assessment for an AI use case
Day 3

AI Risk Management & Control Frameworks

  • AI risk categories, sources, and organizational exposure
  • AI risk identification, assessment, classification, and prioritization
  • Pre-deployment AI risk assessment
  • Model risk, performance risk, data drift, and changing operating conditions
  • Operational, cybersecurity, regulatory, and organizational risks
  • Designing controls, mitigation measures, and risk treatment plans
  • Practical application: Develop an AI risk register and comprehensive control matrix
Day 4

Generative AI Governance & High-Impact AI Systems

  • Governance principles for Generative AI
  • Accuracy, reliability, hallucination, and misinformation risks
  • Protecting sensitive and confidential information when using Generative AI
  • Risks of over-reliance on AI-generated outputs
  • Assessing high-impact and sensitive AI use cases
  • AI testing, validation, monitoring, and ongoing review
  • Practical application: Design responsible-use policies and governance controls for Generative AI
Day 5

AI Governance Framework Implementation & Continuous Improvement

  • Designing an integrated organizational AI governance framework
  • AI policies, standards, procedures, and approval mechanisms
  • AI governance committees, system owners, developers, and users
  • AI governance KPIs and risk indicators
  • AI incident management and risk response
  • Periodic review, assurance, audit, and continuous improvement
  • Final workshop: Develop an integrated AI Governance, Ethics & Risk Management framework covering governance structures, roles and responsibilities, risk assessment, data governance, privacy, transparency, fairness, human oversight, Generative AI governance, monitoring, incident management, performance and risk indicators, assurance, review, and continuous improvement.

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