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compute the throughput X and other interesting performance measures using the load-dependent MVA (see Section 8.2). As an example we use the parameters from Table 13.3 and determine the throughput as a function of the number of client workstations m (see Fig. 13.7) Problem 13.5 Verify the results shown in Fig. 13.7 by hand computation and using an available modeling package such as SHARPE or PEPSY. Fundamental Paradox of IT Process Improvement FIGURE 28.14
Whether a threat model and security policy evolve or are developed in a one-off project, at their heart lie business decisions about priorities how much to spend on protection against what. This is risk management, and it should be done within the broader framework of managing non-IT risks. A number of firms sell methodologies for this. Some come in the form of do-ityourself PC software, while others are part of a package of consultancy services. Which one you use may be determined by your client s policies; for example, if you re selling anything to the U.K. government, you re likely to have to use a system called CRAMM. The basic purpose of such systems is to prioritize security expenditure, while at the same time provide a financial case for it to senior management. The most common technique is to calculate the annual loss expectancy (ALE) for each possible loss scenario. This is the expected loss multiplied by the number of incidents expected in an average year. A typical ALE analysis for a bank s computer systems might consist of several hundred entries, including items such as those listed in Figure 22.5. Note that accurate figures are likely to be available for common losses (such as Teller takes cash ), while for the uncommon, high-risk losses such as a large funds transfer fraud, the incidence is largely guesswork.
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