LinkedIn Learning – Understanding and Implementing the NIST AI Risk Management Framework (RMF)
English | Tutorial | Size: 172.42 MB
AI seems to be on everyone’s minds these days. But while it can be helpful, AI poses serious risks, so developing a culture of best practices and guidelines is imperative. Organizations today need to adopt an AI risk management scheme to ensure that the advantages of AI use are preserved but that the risks are mitigated to meet the needs of the business.
In this course, instructor Lyron Andrews provides an overview of the six elements of NIST AI Risk Management Framework (RMF) and demonstrates how they can be applied effectively, efficiently, and ethically within a business. After completing this course, you’ll be ready to implement and maintain a risk management framework at your organization.
Learning Objectives:
• Understand the need for an AI Risk Management Framework (RMF) and describe the key elements and structure of the NIST AI RMF.
• Identify and analyze AI risks, impacts, and harms.
• Explain how the concepts of trustworthiness, effectiveness, and risk tolerance relate to AI risk management.
• Apply the four functions of the AI RMF Core (Govern, Map, Measure, and Manage) to establish context, assess risks, and allocate resources for managing AI risks within an organization.
• Utilize AI RMF profiles, actor tasks, and tools to create a specific context for AI risk management activities and compare AI risks to traditional software-related and human interaction risks.
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