Risk identification is a difficult subject. Analysts need it to defend mitigation strategies or to suggest investments. Yet risk identification is often a subjective method, especially in the IT industry. How do you give a number on a certain risk? When do you believe that that number exceeds a threshold? Because of the ambiguous definition of risks, it is often overlooked or substituted with impact analysis.
Now impact analysis is not much better, but it is easier to comprehend. Impact analysis describes what happens when something has occurred. It doesn't state how often it can occur, or what the chances are of the event to occur, but gives an estimation on how big the impact would be when it occurs. Most of the time one describes an impact with financial loss. The highest impact a company has is that its survival is threatened. If you're a simple shop and you don't take an insurance on your stock, the impact of a fire or explosion in your shop might be that you're put out of business. If you do have insurance, your impact is more limited.
In the majority of cases, impact analysis is more straightforward.
IT risks are a prime example of difficult exercises. Quantifying the IT risks that a company is taking is difficult. In this post, I'm introducing a more straightforward method - even if it isn't fail-safe, it might still give some interesting sights on the matter. It is of course not something I invented myself (there's enough information on the internet about risk analysis or risk identification), merely a combination of several methods and ideas which I find useful (and decided to write up about).
The method identifies a risk within four levels: low, medium, high and very high. To get inside a level, it uses two metrics: impact analysis which we've discussed before, and chance of occurrence. The latter is the most difficult to identify, but let's first show how to map the two onto the risk level:
chance of occurrence
4 + M M VH VH
3 + M M VH VH
2 + L L H H
1 + L L H H
x--+--+--+--+--> impact analysis
1 2 3 4
As you can see, I give each axis four values, going from low (low impact, or low probability) onto high. The resulting point lays within one of four quadrants - Low, Medium, High or Very High. I identify an event as having a high(er) risk when its probability is low but impact high, whereas an event that has a high probability but low impact is considered to be less of a risk: when something with a somewhat low(er) impact occurs, you should still have some breathing space to make sure it won't happen again (or find a way to reduce the impact) whereas an occurrence of something with a high(er) impact will most likely leave you hurt, fragile and licking your wounds.
So, how to measure the chance of occurrence of an event? Well, let's do this in two stages: do an initial assessment, and then elevate the chance using particular checks. Within IT, an often described threat is the threat of someone trying to achieve personal gain (financial or public) from the system(s). Note that, for every possible threat, one will need to make a risk identification - you can't just say that a system has a risk. It is always a particular threat which is assigned a risk. So how large is the chance of such a "hack" attempt occurring?
First, how "wide" is the access vector towards your system? Can the entire world (read: Internet) access the system (level = 4), is it everyone in the company (level = 3), everyone within an affiliated department (in most cases it means "IT department", level = 2) or limited to a very small set of people (level = 1)?
Second, if one of the people identified earlier would want to perform malicious activity with eye on personal gain (financial or PR), would he succeed by his own (level + 1) or would he need at least one accomplice?
Third, if the activity was performed, would it be traceable to the person or not (if not: level + 1)?
As an example (purely hypothetical): read access to the access logs of a web application server which contains HTTP session information (logging of SESSIONID) as well as username (authentication) and origin (IP address) as well as other information. The threat: using this information to hijack an active session.
First, if a hijack would be successful, the impact would be considered a level-3 (with 1 being low and 4 being very high): the company might suffer huge financial losses or PR would be a difficult beast to tame (because the application is an internal stock application with features to perform financial operations). But how high is the chance of occurrence?
Well, say that the log files themselves can only be read by IT staff (level = 2), but that someone of the IT staff cannot hijack the session easily with this information alone as he would either require firewall changes (for instance because the application can only be reached through trusted middleware components) or have access to the machine the user is working on, and such changes or access require more than a single person in the situation. Also, if he did, audit trails would lead the changes (firewall changes or machine access) to the person. As a result, the chance of this event occurring given the circumstances is considered a level 2. At the quadrant, this would yield a level of "High". The risk of occurrence is relatively low, but the impact is too high to ever consider this a "Medium" or "Low".
Now if we were to reduce the risk, we could focus on lowering the chance of occurrence (only few people access to the given information - say keep SESSIONID information in a separate, inaccessible logfile only to be used for general, automated metric collection - after all, if there's no point in keeping track of SESSIONID's, they wouldn't be logged anyhow). This is a lot easier to accomplish than to try and lower the impact analysis. On the other hand, if the impact analysis could be lowered (say by requesting a stronger authentication method for validating particular steps within the application, such as approvals) a session hijack would give less impact - say level 2 or even 1. In that case, the current risk would be lowered from "High" to "Low".