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A Few Good Metrics

A Few Good Metrics

Mention metrics to a CIO or infosecurity executive and immediately their thoughts may well turn to sigmas, standard deviations and, probably, probability. To many, metrics equals statistics.

METRIC 1 Baseline Defences Coverage (Antivirus, Antispyware, Firewall, and so on)

This is a measurement of how well you are protecting your enterprise against the most basic information security threats. Your coverage of devices by these security tools should be in the range of 94 percent to 98 percent. Less than 90 percent coverage may be cause for concern. You can repeat the network scan at regular intervals to see if coverage is slipping or holding steady. If in one quarter you've got 96 percent antivirus coverage, and it's 91 percent two quarters later, you may need more formalized protocols for introducing devices to the network or a better way to introduce defences to devices. In some cases, a drop may stir you to think about working with IT to centralize and unify the process by which devices and security software are introduced to the network. An added benefit: By looking at security coverage, you're also auditing your network and most likely discovering devices the network doesn't know about. "At any given time, your network management software doesn't know about 30 percent of the IP addresses on your network," says Jaquith, because either they were brought online ad hoc or they're transient.

How to get it: Run network scans and canvass departments to find as many devices and their network IP addresses as you can. Then check those devices' IP addresses against the IP addresses in the log files of your antivirus, antispyware, IDS, firewall and other security products to find out how many IP addresses aren't covered by your basic defences.

Expressed as: Usually a percentage. (For example, 88 percent coverage of devices by antivirus software, 71 percent coverage of devices by antispyware and so forth.)

Not good for: Shouldn't be used for answering the question "How secure am I?" Maximum coverage, while an important baseline, is too narrow in scope to give any sort of overall idea of your security profile. Also, probably not yet ready to include mobile phones, BlackBerrys and other personal devices, because those devices are often transient and not always the property of the company, even if they connect to the company.

Try these advanced versions: You can parse coverage percentages according to several secondary variables. For example, percentage coverage by class of device (for instance, 98 percent antivirus coverage of desktops, 87 percent of servers) or by business unit or geography (for instance, 92 percent antispyware coverage of desktops in operations, 83 percent of desktops in marketing) will help uncover tendencies of certain types of infrastructure, people or offices to miss security coverage. In addition, it's a good idea to add a time variable: Average age of antivirus definitions (or antispyware or firewall rules and so on). That is, 98 percent antivirus coverage of manufacturing servers is useless if the average age of the virus definitions on manufacturing's servers is 335 days. A star company, Jaquith says, will have 95 percent of its desktops covered by antivirus software with virus definitions less than three days old.

One possible visualization: Baseline defences can be effectively presented with a "you are here" (YAH) graphic. A YAH needs a benchmark - in this case it's the company's overall coverage. After that, a business unit, geography or other variable can be plotted against the benchmark. This creates an easy-to-see graph of who or what is close to "normal" and will suggest where most attention needs to go. YAHs are an essential benchmarking tool. The word "you" should appear many times on one graphic. Remember, executives aren't scared of complexity as long as it's clear. Here's an example: plotting the percentages of five business units' antivirus and antispyware coverage and the time of their last update against a companywide benchmark.

METRIC 2 Patch Latency

Patch latency is the time between a patch's release and your successful deployment of that patch. This is an indicator of a company's patching discipline and ability to react to exploits, "especially in widely distributed companies with many business units", according to Jaquith. As with basic coverage metrics, patch latency stats may show machines with lots of missing patches or machines with outdated patches, which might point to the need for centralized patch management or process improvements. At any rate, through accurate patch latency mapping, you can discover the proverbial low-hanging fruit by identifying the machines that might be the most vulnerable to attack.

How to get it: Run a patch management scan on all devices to discover which patches are missing from each machine. Cross-reference those missing patches with a patch clearinghouse service and obtain data on 1. the criticality of each missing patch and 2. when the patches were introduced, to determine how long each missing patch has been available.

Expressed as: Averages. (For example, servers averaged four missing patches per machine. Missing patches on desktops were on average 25 days old.)

Not good for: Companies in the middle of regression testing of patch packages, such as the ones Microsoft releases one Tuesday every month. You should wait to measure patch latency until after regression testing is done and take into account the time testing requires when plotting the information. The metrics might also get skewed by mission-critical systems that have low exposure to the outside world and run so well that you don't patch them for fear of disrupting ops. "There are lots of systems not really open to attack where you say: 'It runs, don't touch it'," says Jaquith. "You'll have to make a value judgement [on patch latency] in those cases."

Try these advanced metrics: As with baseline coverage, you can analyze patch latency by business unit, geography or class of device. Another interesting way to look at patch latency statistics is to match your average latency to the average latency of exploits. Say your production servers average 36 days on missing patches' latency, but similar exploits were launched an average of 22 days after a patch was made available. Well, then you have a problem. One other potentially useful way to approach patch latency is to map a patch to its percent coverage over time. Take any important patch and determine its coverage across your network after one day, three days, five days, 10 days and so on.

One possible visualization: For data where you can sum up the results, such as total number of missing patches, a "small multiples" graphic works well. With small multiples you present the overall findings (the whole) as a bar to the left. To the right, you place bars that are pieces making up the whole bar on the left. This presentation will downplay the overall findings in favour of the individual pieces. One key in small multiples graphing is to keep the scale consistent between the whole and the parts. This example plots total number of missing patches for the top and bottom quartiles of devices (the best and worst performers). Then it breaks down by business unit who's contributing to the missing patches.

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