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A Traditional Example of a Risk Calculation

One classic method of risk analysis expresses risk as a financial loss, or Annualized Loss Expectancy (ALE), based on the following equation:

ALE = SLE × ARO

where SLE is the Single Loss Expectancy and ARO is the Annualized Rate of Occurrence (or predicted frequency of a loss event happening).

Consider an Internet-based equities trading application possessing a vulnerability that may result in unauthorized access, with the implication that unauthorized stock trades can be made. Assume that a risk analysis determines that middle- and back-office procedures will catch and negate any malicious transaction such that the loss associated with the event is simply the cost of backing out the trade. We’ll assign a cost of $150 for any such event. This yields an SLE = $150. With even an ARO of 100 such events per year, the cost to the company (or ALE) will be $15,000.

The resulting dollar figure provides no more than a rough yardstick, albeit a useful one, for determining whether to invest in fixing the vulnerability. Of course, in the case of our fictional equities trading company, a $15,000 annual loss might not be worth getting out of bed for (typically, a proprietary trading company’s intraday market risk would dwarf such an annual loss figure). [8]

Other methods take a more qualitative route. In the case of a Web server providing a company’s face to the world, a Web site defacement might be difficult to quantify as a financial loss (although some studies indicate a link simply between security events and negative stock price movements [Cavusoglu, Mishra, and Raghunathan 2002]). In cases where intangible assets are involved (e.g., reputation), qualitative risk assessment may be a more appropriate way to capture loss.

Regardless of the technique used, most practitioners advocate a return-on-investment study to determine whether a given countermeasure is a cost-effective method for achieving the desired security goal. For example, adding applied cryptography to an application server, using native APIs (e.g., MS-CAPI) without the aid of dedicated hardware acceleration, may be cheap in the short term; but if this results in a significant loss in transaction volume throughput, a better ROI may be achieved by investing up front in crypto acceleration hardware. (Make sure to be realistic about just what ROI means if you choose to use the term. See the box The Truth about ROI.)

Interested organizations are advised to adopt the risk calculation methodology that best reflects their needs. The techniques described in this chapter provide a starting point.

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