AI Risk Management and Governance Frameworks

Course Category : Risk Management

An advanced programme for establishing integrated frameworks to identify, assess, and address AI risks while strengthening governance, accountability, and compliance throughout the AI system lifecycle.
Duration: 5 Days
Level: Advanced

Introduction

The expanding institutional use of artificial intelligence requires organisations to establish robust controls over risks affecting decisions, data, compliance, accountability, and reputation. AI risk management is therefore no longer an isolated technical responsibility; it is an enterprise governance discipline requiring defined authority, structured risk classification, and traceable oversight mechanisms.
This course examines advanced approaches to AI risk management and governance framework design. It addresses risk identification, assessment and treatment, governance responsibilities, lifecycle controls, monitoring, accountability, and organisational oversight required to support controlled and trustworthy adoption of AI..

Targeted Audience

Executives and decision-makers responsible for AI initiatives
Governance, risk, and compliance managers
AI and digital transformation leaders
IT and data managers
Enterprise risk and internal control professionals
Data protection and privacy officers
Technology audit and compliance teams
Legal and technology advisors involved in AI governance

Targeted Skills

AI risk analysis and classification
AI governance framework design
Definition of roles, responsibilities, and accountability lines
Assessment of model, data, and automated decision risks
Development of AI lifecycle risk controls
Alignment of AI governance with enterprise risk management
Design of monitoring, escalation, and risk indicators
Evaluation of transparency, documentation, and compliance requirements

Expected Outcomes

Explain the nature, sources, and organisational implications of AI risks.
Classify AI risks according to impact, likelihood, and operational context.
Design governance structures defining ownership, authority, and accountability for AI systems.
Apply a structured methodology for identifying, assessing, prioritising, and treating AI risks.
Determine appropriate controls across AI development, acquisition, deployment, and operation.
Use recognised reference frameworks to structure enterprise AI risk and governance practices.
Develop mechanisms for continuous monitoring, reporting, escalation, and risk response.
Support consistent organisational decisions regarding AI risk acceptance and mitigation.

Training Topics Index

Nature of AI risk and its distinction from conventional technology risks
Classification of model, data, privacy, security, bias, and automated decision risks
Organisational context, stakeholder mapping, and risk exposure analysis
Impact, likelihood, risk appetite, and tolerance considerations
Structure of the NIST AI Risk Management Framework (AI RMF) as a risk management reference

Risk identification methodologies, scenario analysis, and potential harm pathways
Inherent and residual risk assessment and prioritisation
Data quality, bias, fairness, reliability, and explainability risks
Risk treatment through avoidance, mitigation, transfer, acceptance, and compensating controls
IBM AI Fairness 360 as an instructional example for understanding bias and fairness risk assessment

Enterprise AI governance principles and accountability models
Ownership, roles, authority, oversight committees, and escalation pathways
AI policies, approval criteria, and use-case classification
Registers, documentation, traceability, and governance evidence
OECD AI Principles as a reference for trustworthy AI governance principles

Integration of risk management from planning and design through deployment and operation
Data, model, access, security, privacy, and human oversight controls
Third-party, vendor, external model, and AI service risks
Documentation, auditability, change management, exceptions, and incident controls
Microsoft Responsible AI Dashboard as an example for fairness, interpretability, and model error analysis

AI key risk indicators, thresholds, and escalation criteria
Model performance, drift, and changing risk exposure monitoring
Management reporting, governance committees, and AI risk dashboards
Control effectiveness reviews and AI governance maturity assessment
Governance roadmaps, continuous improvement, and emerging risk response

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 Risk Management and Governance Frameworks