Try this: Use benchmarking in hardware procurement or integration services negotiations, demanding configurations that meet some minimum score. Also demand baseline scores from partners or others who connect to your network.
One possible visualization: An overall score here is simple to do: It's a number between 1 and 10. To supplement that, consider a tree map. Tree maps use color and space in a field to show "hot spots" and "cool spots" in your data. They are not meant for precision; rather they're a streamlined way to present complex data. They're "moody." They give you a feel for where your problems are most intense. In the case of platform-compliance scores, for instance, you could map the different elements of your benchmark test and assign each element a color based on how risky it is and a size based on how often it was left exposed. Be warned, tree maps are not easy to do. But when done right, they can have instant visual impact.
Metric 5: Legitimate E-Mail Traffic Analysis
Legitimate e-mail traffic analysis is a family of metrics including incoming and outgoing traffic volume, incoming and outgoing traffic size, and traffic flow between your company and others. There are any number of ways to parse this data; mapping the communication flow between your company and your competitors may alert you to an employee divulging intellectual property, for example. The fascination to this point has been with comparing the amount of good and junk e-mail that companies are receiving (typically it's about 20 percent good and 80 percent junk). Such metrics can be disturbing, but Jaquith argues they're also relatively useless. By monitoring legitimate e-mail flow over time, you can learn where to set alarm points. At least one financial services company has benchmarked its e-mail flow to the point that it knows to flag traffic when e-mail size exceeds several megabytes and when a certain number go out in a certain span of time.
How to get it: First shed all the spam and other junk e-mail from the population of e-mails that you intend to analyze. Then parse the legitimate e-mails every which way you can.
Not good for: Employee monitoring. Content surveillance is a different beast. In certain cases you may flag questionable content or monitor for it, if there's a previous reason to do this, but traffic analysis metrics aren't concerned with content except as it's related to the size of e-mails. A spike in large e-mails leaving the company and flowing to competitors may signal IP theft.
Added benefit: An investigations group can watch e-mail flow during an open investigation, say, when IP theft is suspected.
Try this: Monitor legitimate e-mail flow over time. CISOs can actually begin to predict the size and shape of spikes in traffic flow by correlating them with events such as an earnings conference call. You can also mine data after unexpected events to see how they affect traffic and then alter security plans to best address those changes in e-mail flow.
One possible visualization: Traffic analysis is suited well to a time series graphic. Time series simply means that the X axis delineates some unit of time over which something happens. In this case, you could map the number of e-mails sent and their average size (by varying the thickness of your bar) over, say, three months. As with any time line, explain spikes, dips or other aberrations with events that correlate to them.