There are inherent limitations to relying upon traditional Security Information & Event Management Systems or SIEMS, which are often overlooked that every organization must be made aware of. These limitations are: 1) SIEM’s fixed focal point and 2) Dependencies upon structured data sources
Maintaining a fixed focal point (or monitoring just a subset of data) only encourages nefarious opportunists to find vulnerabilities outside of this narrow field of vision. Any experienced security professional will say that all data is security relevant. However, traditional SIEM’s limit their field of vision to just a fixed focal point of data. To understand why this matters, let’s look at an example outside of information technology that’s perhaps easier to follow. Imagine for a moment that three are a string of home break-ins happening in your neighborhood. To safeguard your property you decide to take precautionary measures. You consult a security professional and they make several recommendations – Place deadbolts on the front and back doors, reinforce locks on the first floor windows, set camera’s and alarm systems above the front and back doors and windows. With all this complete, you rest easier feeling far more secure. This is what a traditional SIEM does. It takes known vulnerability points and monitors them.
Building upon this example, let’s imagine that a bad person comes along and is intent on breaking into your home. She cases the house, spots the camera’s, and decides that the windows and doors on the first floor pose too much of a risk of detection. After studying the house for a while, she finds and exploits a blind spot in your defenses. Using a coat hanger, she quickly gains access to the home in just six seconds through the garage without any alarm being tripped. How can this be? Well, your security professional didn’t view a closed garage door as one of your vulnerability points, so no cameras or security measures were installed there. As a result, your home has been breached, and no alarms have been triggered since the breach occurred outside your monitored field of vision. This scenario illustrates the inherent limitation of defining a problem based upon anticipated vulnerabilities. Determined inventive criminals will figure out ways to defeat known defenses that haven’t been considered. That too is the inherent problem of traditional SIEM’s; they are designed to only look at known threats and vulnerabilities, as a result – they do little to no good alerting you to unanticipated threats or vulnerabilities.
Also, the dependence upon structured data sources also creates another serious security limitation. Traditional SIEMS store information in a relational database. The limitation of this approach is that in order to get information from different sources into a database, users first need to define a structure for this information, then force ably make the data adhere to this defined structure. Oftentimes, imposing this structure leads to relevant security information being left out in this process.
To illustrate why this is an issue, let’s imagine that detectives are trained to only look for finger prints when analyzing a crime scene. Their investigations totally ignore any information that isn’t a finger print – they search for finger prints, partial prints, and if they are really advanced, maybe they’ll include hand and foot prints. However, in the course of their investigation they completely ignore collecting blood, hair, saliva or other DNA related evidence. Now, just how effective would a detective be in solving this case if the criminal wore gloves and shoes? I think everyone would agree that the answer to that question is that wouldn’t be a very effective investigator. Well, that’s exactly what happens by limiting the types of data captured by force fit different data types into a standard database schema – running through a schema format process effectively removes lots of relevant information that can be of great help in an investigation.
Instead, since all data is security relevant, to be truly effective, security professionals must have the ability to collect information from all sources of data in its full fidelity. Since traditional SIEM’s strips out this ability, then it follows that no business should solely rely upon a traditional SIEM for security – make sense?
Instead, what is needed is more of a fluid approach to security, one that captures information from multiple sources, evaluates all known exploits, and allows you to correlate different information to uncover new potential exploits before a report-able data breach occurs. Splunk’s real-time machine data platform is extremely well suited to that task.
