Over the last several years, computing has changed to an almost purely
networked environment, but the technical aspects of information protection
have not kept up. As a result, the success of information security programs
has increasingly become a function of our ability to make prudent management
decisions about organizational activities. Managing Network Security takes
a management view of protection and seeks to reconcile the need for security
with the limitations of technology.
It is almost never feasible to prevent all of the harmful things that can happen to an information system, and it is rarely cost effective to do so. But if we don't prevent harm, we will almost certainly take some damage. When we encounter harmful events, we typically respond to them in one form or another depending on the damage we have and expect to suffer. The question arises of how we decide when to prevent, detect, and react. This is the issue of risk staging.
An understanding of two dimensions of the risk-staging problem has been particularly helpful in my work. For now, I will call them the static and dynamic cases. The static risk-staging problem starts with the assumption that everything is steady state, a condition that never actually exists in information technology today, but that can be used to think about how to balance approaches. The dynamic risk-staging problem addresses when risk mitigation should be completed, or in the more detailed case, how to sequence the implementation of risk mitigation activities.
In 1997, I did a national technical baseline study of intrusion detection and response. In the process, I reviewed more than 350 papers on the subject, talked to scores of researchers and developers as well as many users of these systems, and tried several of these systems out. If there is one thing I gleaned from the past 15 years of scientific research into intrusion detection and response, it is that we canít detect many of the harmful things that happen to information systems.
At a theoretical level the problem is, of course, undecidable. No matter how many things we are able to detect, there will either be an infinite number of false positives (detected incidents that do not correspond to actual incidents), an infinite number of false negatives (actual incidents that are not detected), or both. Interestingly, there is a tradeoff between false positives and false negatives that, in some systems, can be explicitly tuned.
At a practical level, false positives stress the human systems that investigate incidents. At some fairly low level of activity (in comparison with the levels of false positives that can be generated by automated systems) human response systems are unable to pick the signal out from the noise. People adapt by ignoring incidents or by performing poorly against them, they get cognitive overload, and they often get frustrated and refuse to continue their efforts. The cost of handling incidents goes up with the total number of detected indicators as well as the number of accurate indicators.
Since false positives carry a substantial cost, many organizations choose to minimize them. But if you minimize false positives, the inevitable result is a lot of false negatives. And this translates into undetected incidents ... sort of.
Prevention is expensive and almost always unnecessary. When I say unnecessary, I mean it in a specific way. It turns out that there are a large number of ways to attack systems. Unless the number of actual attacks against a system is very large, the vast majority of attack mechanisms will never be used. Thus the vast majority of the prevention is never exercised. This would not be true if we could predict which attacks were going to be used against which systems with what frequency. Probabilistic risk assessment (PRA) is based on the notion that we can do that, but in my experience, the predictive power of PRA in information protection is inadequate to change this situation. Effective prevention almost always carries high cost and unnecessary restriction.
Because detection and reaction delay spending money on protection until it is needed, management often chooses it. Unless you can show that prevention is more cost effective, detection and reaction will normally be the defenses of choice.
If we could detect attacks and react to them quickly enough, we could, in theory at least, eliminate prevention completely. Since we cannot detect and react accurately and quickly enough for all situations, in those situations, protection requires prevention. But this begs the issue of what requires protection? In order to understand this issue, we have to talk about what causes risk.
I will almost certainly repeat what I am about to describe again and again, but since this is the first article in this series, I will be a little bit more elaborate in this exposition than in the future.
I have an equation:
Managing risk requires that we be able to define and understand risk. My equation is intended to describe the notion that risk is produced by a combination of dependencies, vulnerabilities, and threats. To clarify, I usually give three examples.
In order to justify protection, we must have a risk. But the issue runs a bit deeper than just this. The investment in risk mitigation must have a return that, after taking all of the uncertainty into account, meets or exceeds the return on investment I would get from investing in other ways. This return on investment (ROI) perspective on protection requires that we be able to clearly demonstrate the risks in terms of financial impacts. A clear demonstration, at least for most business people I know, requires some data from real incidents.
Fortunately, most organizations can supply that sort of data if they only look for it. An example might help. In one organization I work with, macro viruses had become a substantial issue. In encountering one on a PC, I asked about it and was told by the technician who came to clean it up that most people in the support organization spend an average of a few hours a day cleaning up after computer viruses. I later went to a meeting of the support group and asked for confirmation of that figure, and sure enough, something like 20 percent of the time spent on support ended up spent removing macro viruses from systems. Furthermore, all of these macro viruses were spreading via email attachments.
I did a quick calculation and figured out that the cost of response was now on the order of $200,000 per year for this site - and this didn't include lost time or data by users. I asked if they had considered getting a central virus scanner for their email server. They indicated that such a system carried a cost of $50,000 and required substantial time and effort to maintain (about $50,000 per year). My report indicated that an ROI of 2 to 1 could be attained in the first year and that in subsequent years the return would be on the order of 4 to 1. This wasn't a speculation based on some combination of guesses about statistical phenomena. It was based on clear empirical evidence gathered from their own experience.
Translating this result into the DxVxT equation is neither easy to do nor particularly important once we have an ROI to report. Clearly we have a case where a relatively modest investment bears very good returns with almost no guesswork and in a very short time. This is also a case where detection and response (macro viruses are detected in the server and removed) are automated so that they, in effect, become prevention. It is thus also a case where risk staging favors prevention over detection and response and where analysis can clearly show the rationality of this choice. Nevertheless, it may be useful to point out what the D, V, and T components are in this case.
Dependencies: The systems being infected by viruses in these cases are used for normal business purposes. They facilitate communication between individuals within the organization and individuals in customer and vendor organizations. While the business can exist without these systems, they provide far greater efficiency than can be attained through other means. Timeliness is not usually critical in these systems. The dependencies have been demonstrated by actual harm.
Vulnerabilities: These systems are vulnerable to all sorts of attacks, but the particular vulnerability here is from macro viruses. All of these systems are vulnerable and none of the vulnerabilities can be removed today without destroying the utility of the systems. The vulnerabilities have been demonstrated by exploitations against this organization.
Threats: The threats range over a wide range. Very little skill is required in order to write computer viruses and this organization is targeted and actively attacked by threats with the motivation and capability to use viruses. This has been demonstrated by actual incidents.
To summarize results on the static risk staging challenge, I have the following word equation.
Prevent when [[time and accuracy limitations of detection and reaction mandate it] OR [it is more cost effective than alternatives]] AND the risk reduction (read DxVxT-D'xV'xT') yields adequate return on investment.
Several years ago, Ron Knecht (a very knowledgeable and thoughtful expert in this field) asked me what would seem to be a very simple question. He wanted to know when we should expect to achieve our national infrastructure protection goals. In trying to answer his question, I came to understand some things about dynamic risk staging.
The basic issue I addressed in dynamic risk staging
was the financial effect of trying to achieve a desired level of
protection in different time frames. The plot here shows an analysis
of the cost of improving protection in a particular computing
environment verses the time protection is fully in place. The general
shapes of the curves are valid in many cases. In particular, I found
four factors that were involved in risk staging.
Lifecycle location costs come from the fact that modifications to systems cost more and more as we move from the beginning of the lifecycle toward the end. If we try to retrofit protection into all of our existing systems, the cost will be very high.
Acquisition costs come from both the acquisition cycle and the development of new technologies. Trying to buy the latest and greatest technologies on almost no notice makes the acquisition process far more expensive. Waiting even one year has a very substantial impact on the cost of a technology as well as on the ability to bargain for a better price.
The cost of attacks is cumulative and tends to increase over time. If effective protection is in place at a given time, subsequent losses from attacks will be within the managed level of risk, so delaying implementation tends to increase loss. The times here can be quite short for some situations. In some cases, within a few hours after an attack is announced it is exploited in thousands of systems.
Lost opportunity comes in the form of the inability to use technology for activities that would be very risky without proper protection in place. For example, Internet commerce has very high potential risks if protection is not in place. Thus, for deciding about how to stage Internet protection, lost opportunity may far outweigh acquisition costs over time frames as short as a few months.
In most environments, static and dynamic risk staging are combined to form a mix of current and planned protective measures. Prevention strategies that may work in 3 years might be covered by detection and response capabilities in the short run, while in other cases, short term risk-taking might be used in anticipation of improved technologies for mitigating a particular risk in the coming years.
In order to do a really good job of risk staging, optimization techniques can be used. One technique for accomplishing this is by using a graph analysis to select the best sequence of prevention, detection, and response options implemented in sequence over time. But this sort of optimization is beyond the scope of this article.
Risk staging is an important protection management technique that has not been widely studied in the literature. It can be effectively used to trade off resources and it is implicit in many decisions about protection. The static risk staging process is, in effect, used every time the decision is taken to not use prevention. They dynamic risk staging technique is used every time we decide to delay implementation. In making these explicit and beginning to study them, I hope to accomplish two things. One is to find new ways to address protection. The other is to encourage others to take up the subject and produce the sorts of results we all need to make protection more effective.
About The Author:
Fred Cohen is a Principal Member of Technical Staff at Sandia National Laboratories and a Managing Director of Fred Cohen and Associates in Livermore California, an executive consulting and education group specializing information protection. He can be reached by sending email to fred at all.net or visiting /