Cybersecurity risk management guides a growing number of IT decisions. Cybersecurity risks continue to have critical impacts on overall IT risk modeling, assessment and mitigation. In this course, you will learn about the general information security risk management framework and its practices and how to identify and model information security risks and apply both qualitative and quantitative risk assessment methods. Understanding this framework will enable you to articulate the business consequences of identified information security risks. These skills are essential for any successful information security professional. The goal of this course is to teach students the risk management framework with both qualitative and quantitative assessment methods that concentrate on the information security (IS) aspect of IT risks. The relationship between the IT risk and business value will be discussed through several industry case studies. 

First, you will learn about the principles of risk management and its three key elements: risk analysis, risk assessment and risk mitigation. You will learn to identify information security related threats, vulnerability, determine the risk level, define controls and safeguards, and conduct cost-benefit analysis or business impact analysis. Second, we will introduce the qualitative and quantitative frameworks and discuss the differences between these two frameworks. You will learn the details of how to apply these frameworks in assessing information security risk. Third, we will extend the quantitative framework with data mining and machine learning approaches that are applicable for data-driven risk analytics. You will explore the intersection of information security, big data and artificial intelligence. Finally, you will analyze a series of extended case studies, which will help you to comprehend and generalize the principles, frameworks and analytical methods in actual examples.