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An alternative formula for risk determination is provided by [Mei03]: Risk = Probability * Damage Potential in which both the probability and damage potential variables are represented numerically as values from 1 to 10, giving a minimum and maximum risk value of 1 and 100 respectively. To achieve qualitative results, low represents any risk from 1 to 33, medium represents risks from 34 to 66, and high represents risks from 67 to 100. Note that because this method is threat- based, it gives the risk of a particular threat, as opposed to the risk posed to an asset. Appendix E of [ISO13335-3] provides a number of examples of the use of matrices to evaluate risk, in which each example places emphasis differently. One example offers an asset-based evaluation, whereas another assesses the risk of given threats. While these examples recognize the inherent relationship between threats and vulnerabilities, they do not provide a formal way of accounting for them.

[NIST800-30] uses a 3x3 matrix made up of threat likelihood and threat impact. Qualitative values of threat likelihood (high, medium, low) are converted numerically to ratings of 1.0, 0.5, and 0.1 respectively. Qualitative values of threat impact (high, medium, low) are converted numerically to ratings of 100, 50, and 10 respectively. Risk is then computed by multiplying the threat likelihood by threat impact for each identified threat-vulnerability. The resulting value represents the degree or level to which an IT system, facility or procedure might be exposed if a given vulnerability were exercised. Note that while this method is clear and straightforward, it does not provide an overall risk rating to a given asset, but simply the risk of a single threat-vulnerability pair. [Pel01] describes an Annual Loss Exposure (ALE) an equation that provides a quantitative method for calculating loss. The ALE is calculated from the value of an asset (A) multiplied by the likelihood of a threat occurrence (L) as follows: ALE = A * L. The likelihood value used is calculated from a multiplier table in which an occurrence of once a day is 365, once a month is 12, once a year is 1, once every 5 years is 1/5, and so on.

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As you can see, white balance affects not only the colors of the image but also the look and feel. AWB is great in a pinch and in many picture-taking situations, but it is only making an approximate estimation in a millisecond s notice. Free yourself from always shooting in automatic white balance and experiment with the different controls and presets.

This pattern has the following benefits:

depths of the depression in 1932 to the peak of the Internet bubble in January of 2000. There are a variety of astounding things to note in this chart: The point where the two major lines intersect in 1974 predicted the precise bottom of the massive 1973 1974 bear market. The steady climb from 1990 to 1995 was perfectly bounded by two of the fan lines. Most impressive of all, the ultimate market top in 2000 was established by the rst fan (which, remember, began 97 years before). Figure 1.2 is a close-up view of late 1999 and early 2000; as you can see, the almost century-old fan line creates impressive resistance to these prices moving higher on four different occasions. If we were in stocks at this time, this would be a vital warning signal that the top was established. This is an extreme example, but the point is that being able to gain insight into the most likely future of a particular price is a vehicle for real trading pro ts. It is an edge that those not using charts effectively simply will not have.

The enterprise is now able to identify and address the risks posed to its assets, as part of a risk mitigation effort.

The qualitative results provided are much easier to calculate, prioritize and interpret. The results can be archived and used to track the progress of asset risk among consecutive risk assessments.

As well as the following liabilities:

FIGURE 1.2 Highlighted here four times is the Dow bouncing off the fan line established over a century earlier.

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