Implementing a risk management framework within one’s management system turns a reactive organization into a proactive one. AI driven enterprises integrating risk management ideologies/methods will see tangible outputs from reduction in product delays, recalls, and other negative events. Furthermore, it will enable organizations to adhere to risk-related, regulatory requirements imposed by different jurisdictions.

BSI has developed a course that enables participants the tools to build a risk management system based on the content of ISO/IEC 23894:2023.

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The aim of this course is to educate participants of the organizational and regulatory importance risk management for AI Enterprises and how to build a such a system based on ISO/IEC 23894:2023. This standard will most likely become harmonized against the AI Act post publication.

How will I benefit?

This course will help you:

• Gain awareness of the importance and significance of implementing a risk management system within an organization’s structure.
• Understand risk terminology and how they apply in mitigating threats and delays.
• Understand the relevance of ISO/IEC 23894:2023 on the international regulatory stage.
• Learn how to build a risk management system.

What will I learn?

Upon completion of this course, you will be able to:

• Understand why risk management is an important organizational activity
• Recognize ISO/IEC 23894 as part of a larger framework of standards
• Be able to define the key terms within ISO/IEC 23894
• Identify key principles/tenets of effective AI risk management systems
• Possess the knowledge to create a risk management framework/plan
• Identify and apply the scope, contexts, and risk criteria for a given prompt
• Perform a risk analysis based on a given prompt
• Identify the documents needed in a risk management file
• Be familiar with adapting changes into a risk management file
• Understand the important of a risk management audit

Who should attend?

The following persons who are working in an AI-related field:

• Audit Staff
• Data Analysts
• Data Engineers
• Data Governance Managers
• Data Scientists
• Directors
• Machine Learning Engineers
• Post Market Surveillance Staff
• Quality Managers and Staff
• Regulatory Staff
• Research and Development Staff
• System Architects
• Supplier Staff