The current Risk Reporting Matrix used by the Department of Defense is a useful tool for determining the risk associated with an action, but it’s limited due to the discrete nature of the metric. Currently, the Risk Reporting Matrix is built using two Likert Scales, one for the likelihood of the event, and one for its consequence. This gives the user 25 possibilities for a likelihood-consequence combination. While this gives a concise and easy to understand answer, it does not fully inform the user of the true risk level for the decision.

Continuous Asymmetric Risk Assessment (CARA) alleviates this shortcoming by transforming the discrete risk matrix to a continuous gradient field. It is designed to provide the user with infinite combinations of likelihood and consequence which more accurately describe the risk associated with the decision in question. Furthermore, by leveraging the use of asymmetric Gaussian distributions, CARA creates confidence intervals around nominal risk, displaying likely outcomes and its variation.

This paper will describe how CARA identifies risk and creates asymmetric confidence intervals by evaluating a real risk data.  The data evaluated in this paper was used to demonstrate to the owner of the data how decisions will impact the risk of their decisions. This paper will also demonstrate how the risk level and variability is reduced as mitigation and prevention steps are added, which leads to more accurate and descriptive risk analysis over a continuum.

Keywords
PROBABILITY, RISK ASSESSMENT

Additional Keywords
RISK MATRIX