AI Governance, Risk and Compliance

Course Category : Risk Management

Develop an integrated institutional approach to AI governance, risk management, and regulatory compliance that strengthens accountability, transparency, and the reliability of AI-enabled decision-making.
Duration: 5 Training Days (25 Training Hours)
Level: Advanced

Introduction

AI governance has become a strategic requirement for organisations seeking to maintain effective oversight of intelligent technologies and algorithmic decision-making. As AI adoption expands, institutions face increasingly complex operational, regulatory, ethical, and information security risks that demand structured accountability and compliance mechanisms.
This course examines institutional AI governance frameworks, risk identification and assessment methodologies, regulatory compliance requirements, and assurance mechanisms. It also addresses the alignment of governance policies, organisational responsibilities, and international standards to establish a coordinated approach that balances technological innovation with risk control, transparency, and regulatory accountability..

Targeted Audience

Executive Leaders and Strategic Decision-Makers.
Governance, Risk and Compliance (GRC) Managers.
AI and Digital Transformation Managers.
Regulatory Compliance and Legal Affairs Officers.
Internal Audit and Corporate Control Managers.
Information Security and Data Protection Officers.
Operational and Technology Risk Managers.
Digital Governance and Policy Consultants.

Targeted Skills

Designing and Evaluating Enterprise AI Governance Frameworks.
Analysing AI-Related Risks.
Aligning AI Policies with Regulatory Requirements.
Defining Governance Roles and Accountability Structures.
Assessing Internal Controls and Compliance Procedures.
Evaluating Algorithmic Bias, Privacy, and Security Risks.
Developing AI Governance and Risk Performance Indicators.
Supporting AI Auditing and Continuous Oversight.

Expected Outcomes

Explain the fundamental principles of AI governance and their relationship to corporate management.
Analyse relevant international AI governance frameworks and standards.
Identify operational, technological, legal, and ethical risks associated with AI systems.
Evaluate AI risk management methodologies and classify risks according to impact and likelihood.
Identify regulatory compliance, data protection, and institutional accountability requirements.
Assess internal controls, audit mechanisms, and AI oversight effectiveness.
Recommend policies and procedures supporting responsible and secure AI adoption.
Formulate an integrated approach to improving organisational AI governance, risk management, and compliance.

Training Topics Index

Fundamental AI governance concepts and their relationship to corporate governance.
International principles of responsible governance, transparency, and accountability.
Governance structures, oversight committees, and allocation of authority and responsibilities.
Examination of ISO/IEC 42001 and the NIST AI Risk Management Framework.
Aligning AI strategies with institutional policies and business objectives.

Classification of technological, operational, legal, and ethical AI risks.
Risk identification methodologies, likelihood and impact analysis, and exposure assessment.
Algorithmic bias, data quality deficiencies, and explainability risks.
Risk treatment strategies, risk appetite thresholds, and appropriate control measures.
Risk registers, early warning indicators, and continuous risk monitoring.

Legislative and regulatory frameworks governing AI across industries.
Core requirements of the EU Artificial Intelligence Act and its risk-based regulatory approach.
Data protection, privacy obligations, and relevant General Data Protection Regulation requirements.
Relationships between ISO/IEC 42001, ISO/IEC 23894, and risk and compliance management frameworks.
Compliance documentation, regulatory evidence management, and nonconformity handling.

Designing internal controls and oversight mechanisms across the AI system lifecycle.
Roles of internal audit, compliance, and risk management in governance assurance.
Transparency, explainability, documentation, and traceability requirements.
Third-party, vendor, and AI-enabled service risk assessments.
Performance and compliance indicators, incident reporting, and escalation procedures.

Components of an integrated AI Governance, Risk, and Compliance operating model.
Institutional policy development, approval procedures, and AI oversight arrangements.
Integrating risk management and compliance into AI development, approval, and operational stages.
Governance maturity assessment, compliance gap identification, and improvement priorities.
Developing an institutional roadmap for stronger oversight, accountability, and continuous improvement.

Course Features

Updated and Interactive Content
Hypothetical Examples and Case Studies
Pre- and Post-assessments to Measure Impact
Verified Certificate with a QR Verification Code

AI Governance, Risk and Compliance