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Sample Measurement Projects for Security Operations
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The following four projects provide some practical examples of how security metrics can be used in the context of SMPs to meet defined measurement goals For each project, I have developed a basic GQM template to define the goal of the project, the questions the project is intended to answer, and the metrics used to provide those answers
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SMP: General Risk Assessment
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The first project is designed to improve upon the annual loss expectancy and risk matrix methods of risk analysis that I critiqued in previous chapters Estimations of annual loss expectancy have been critiqued because the numbers used are often completely made up, based on little or no supportable evidence Risk matrix analysis involves asking IT security stakeholders to assign simple ordinal values to the probabilities and costs of certain security threats These values are usually a variation on high, medium, or low, although they may be expressed in numerical scales (1 3, 1 10, 1 100, and so on) These analyses are problematic because they measure perception of risk rather than actual risk, and they disconnect the risk metric from real numbers and costs in favor of a heat map In both techniques, the assessments often introduce as much uncertainty to the risk question as they remove We continue to perform these risk assessments for many reasons, including familiarity and the fact that they are pretty easy to perform We also perform them because of a
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perception that no viable alternatives exist We need some way of estimating and judging risk even though we are uncertain about what the actual risk is But how do you improve the accuracy of an educated guess Assessing security risks is difficult in part because of a lack of solid, empirical data on which to base estimates Without that data, it may seem hopeless that we can get any closer than experience and gut in our guessing Fortunately, a substantial body of literature is available on judgments in situations of uncertainty and of more rigorously analyzing the opinions of experts in the context of those situations This measurement project used some of these techniques to improve on a company s existing, matrix-based risk assessments to gain insight and reduce existing uncertainties regarding the annual financial costs of several threats The GQM template for the project is listed in Table 7-2
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Using Confidence Intervals (CIs) for Analyzing Expert Judgments
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A full treatment of the methods for analyzing human judgment under uncertainty is beyond the scope of this measurement project, but the implications of these techniques for IT security are interesting because they provide a balance between the estimates of an annualized loss expectancy (ALE) assessment and the construction of a risk matrix, all while focusing on maintaining sound methodological and statistical practices
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Goal Components
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Outcome Improve, understand Element Costs Element Threats (unauthorized access, DOS, data loss) Element Confidence Intervals (CIs) Perspective Internal security experts The goal of this project is to improve the understanding of annual financial costs of unauthorized access, DOS, and data loss by developing formal CIs from the perspective of internal security experts How many incidents of unauthorized access, DOS, and data loss will the organization experience in the coming year CIs based on elicitation of judgment from calibrated internal experts What costs will be incurred from each incident of unauthorized access, DOS, and data loss experienced CIs based on elicitation of judgment from calibrated internal experts
Goal Statement
Question
Metrics Question Metrics
Table 7-2 GQM Template for General Risk Assessment Project
IT Security Metrics
Rather than attempting to develop numbers or scores that can be plugged into an equation or a matrix, these techniques focus on building CIs around the measurements under analysis A CI is a range of values that is predicted to contain the true value sought at some level of assuredness For instance, a 90 percent CI is a range of values that is predicted to contain the actual value you are seeking nine out of ten times CIs allow expert opinion to be articulated in a way that is not absolute, but they eliminate a predefined amount of uncertainty Earlier in the book I described building a CI using the example of estimating the balance of your checking account We each have enough information and expertise about our finances to be more precise than simply saying our balances are low, medium, or high, even if we cannot give an exact amount CI construction leverages expertise and experience in order to give a range that we are reasonably sure is correct The level of reasonableness we need or want may vary in some cases we may want to be 95 percent confident of a result while in others a 70 percent CI may be sufficient for our goals The trick is to combine the proper level of available information with our experience and opinions at an appropriate level of certainty Harnessing informed opinion is the core principle of developing expert CIs and can be effectively employed in IT security as an alternative to traditional ALE or matrix assessments One advantage of CI construction for security is that the practice of articulating risk as an expected interval with a certain probability reduces the tendency to treat the risk numbers as absolutes Forcing yourself to consider the chances that you are wrong in your estimates adds a bit more rigor to your analysis, and thinking in terms of ranges helps you to avoid fixating or anchoring on a particular value Another advantage to CI construction is that the treatment of risk in terms of a range of probabilities can open up further analysis, using techniques to model the various scenarios that you envision within the range Finally, by building CIs in the context of an ongoing Security Improvement Program (SIP), you are able to check estimates against actual occurrence and use these comparisons to refine further estimates Over time, this data can then be used to build more sophisticated risk models for the organization than a series of heat maps or a wildly dispersed set of ALE-to-actual loss figures
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