Build practical expertise to manage AI risks, ensure quality, apply ISO/IEC standards, and lead responsible, compliant AI initiatives with confidence using real-world cases and the AIQURIS platform.
Artificial Intelligence is transforming industries and redefining accountability. Yet many organisations still rely on governance models built for traditional IT systems, which cannot fully address AI’s unique risks, standards, and lifecycle demands. This two-day training, delivered by Singapore’s leading AI experts who actively shape international standards, equips executives and teams with the frameworks, hands-on tools, and practical techniques needed to lead AI initiatives confidently, responsibly, and at scale.
This two-day AI training programme gives executives and teams the tools to manage AI risks, ensure quality, and meet regulatory expectations across the AI lifecycle. Available both online and in person, the training bridges the gap between traditional governance methods and the specific challenges of Artificial Intelligence.
The course combines theory, real-world use cases, and practical application using the AIQURIS platform. Participants learn how to apply leading standards such as ISO 42001 and ISO 23894 to their own AI use cases and governance structures.
Gain hands-on experience with real-world AI quality and risk management techniques. Learn how to apply ISO and IEC standards to governance, data, and supplier evaluation.
| Topic | Relevant Standards |
|---|---|
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AI System Life Cycle Management
Understand roles, responsibilities and essential processes throughout the AI system lifecycle |
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AI System Risk and Quality Management
Perform an AI risk assessment and develop a use-case risk profile. Plan, implement and continuously improve the effectiveness of an AI Quality Management System. |
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Data Governance
Plan data governance processes throughout the data life cycle. Select relevant quality measures for a use case. |
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Testing, Qualification and Supplier Management
Develop requirements and assessment to qualify and accept an AI system. Set up essential processes to work with vendors, throughout procurement and contract monitoring. |
|
| Topic | Relevant Standards |
|---|---|
|
AI System Life Cycle Management
Understand roles, responsibilities and essential processes throughout the AI system lifecycle |
|
|
AI System Risk and Quality Management
Perform an AI risk assessment and develop a use-case risk profile. Plan, implement and continuously improve the effectiveness of an AI Quality Management System. |
|
|
Data Governance
Plan data governance processes throughout the data life cycle. Select relevant quality measures for a use case. |
|
|
Testing, Qualification and Supplier Management
Develop requirements and assessment to qualify and accept an AI system. Set up essential processes to work with vendors, throughout procurement and contract monitoring. |
|
All participants receive a Certificate of Attendance validating their completion of the course.
This programme is designed for professionals ready to take AI from policy to practice:
Mode of delivery
Instructor-Led Online Sessions: 2 sessions of 4 hours each, interactive and practical
Self-Directed Learning: 8 hours of guided online materials to reinforce concepts
What You’ll Need
Computer with high-speed internet and a webcam
Microsoft Edge or Google Chrome
Optional: Microsoft Teams for live collaboration
This course is taught by specialists who work with AI governance, quality, and risk every day. They contribute to international standards, assess high-risk systems, and support organisations that need quality, reliant, and compliant AI initiatives.
CTO and Co-Founder of AIQURIS with long experience in AI system safety, quality, and regulatory compliance. He contributes to ISO and IEC committees and has led assurance work within TÜV SÜD. His work supports sectors that rely on strict requirements for audit readiness, data quality, and accountable AI deployment.
Principal AI Expert with a background in cybersecurity and trustworthy AI. She is a qualified TÜV SÜD AI Quality Trainer and an IEEE CertifAIEd Lead Assessor. She evaluates AI systems for ethical and technical criteria and participates in Singapore’s national AI standards committees.
