Category Archives: Investigations

Teaching Good Investigation Habits Through Reinforcement

Press_for_food-fullThe biggest responsibility that leaders and senior analysts in a SOC have is to ensure that they are providing an appropriate level of training and mentoring to younger and inexperienced analysts. This is how we better our SOC’s, our profession, and ourselves. One problem that I’ve written about previously relates to the prevalence of tacit knowledge in our industry. The analysts who are really good at performing investigations often can’t describe what makes them so good at it, or what processes they use to achieve their goals. This lack of clarity and repeatability makes it exceedingly difficult to use any teaching method other than having inexperienced analysts learning through direct observation of those who are more experienced. While observation is useful, a training program that relies on it too much is flawed.

In this blog post I want to share some thoughts related to recent research I’ve done on learning methods as part of my study in cognitive psychology. More specifically, I want to talk a bit about one specific way that humans learn and how we might be able to better frame our investigative processes to better the investigation skills of our fellow analysts and ourselves.

Operant Conditioning

When most people think of conditioning they think of Pavlov and how he trained his dogs to learn to salivate at the sound of a tone. That is what is referred to as learning by classical conditioning, but that isn’t what I want to talk about here. In this post, I want to instead focus on a different form of learning called operant conditioning. While classical conditioning is learning that is focused on a stimulus that occurs prior to a response and is associated with involuntary response, operant conditioning is learning that is related to voluntary responses and is achieved through reinforcement or punishment.

An easy example of operant conditioning would be to picture a rat in a box. This box contains a button the rat can push with its body weight, and doing so releases a treat. This is an example of positive reinforcement that allows to rat to learn the associated that pressing the button results in a treat. The relationship is positively reinforced because a positive stimulus is used.

Another type of operant conditioning reinforcement is negative reinforcement. Consider the same rat in a different box with a button. In this box, a mild electrical charge is passed to the rat through the floor of the box. When the rat presses the button, the electrical charge stops for several minutes. In this case, negative reinforcement is being used because it teaches the rat a behavior by removing a negative stimulus. The key takeaway here is that negative reinforcement is still reinforcing a behavior, but in a different way. Some people confuse negative reinforcement with punishment.

Punishment is the opposite of reinforcement because it reduces the probability of a behavior being expressed. Consider the previous scenario with the rat in the electrified room, but instead, the room is only electrified when the rat presses the button. This is an example of a punishment that decreases the likelihood of the rat pressing the button.

Application to Security Investigation

I promise that all of this talk about electrifying rats is going somewhere other than the BBQ pit (I live in the deep south, what did you expect?). Earlier I spoke about the challenge we have because of tacit knowledge. This is made worse in many environments where you have access a mountain of data but have an ambiguous workflow that can allow an input (alert) to be taken down hundreds of potential paths. I believe that you can take advantage of a fundamental construct like operant conditioning to help better your analysts. In order to make this happen, I believe there are three key tasks that must occur.

Identify Unique Investigative Domains

First, you must designate domains that lend themselves to specific cognitive functions and specializations. For instance, triage requires different skills sets and cognitive processes than hunting. Thus, those are two separate domains with different workflows. Furthermore, incident response requires yet another set of skills and cognitive processes, making it a third domain of investigation. Some organizations don’t really distinguish between these domains, but they certainly should. I think there is work to be done to fully establish investigative domains (I expect lots of continued research here on my part), and more importantly, criteria for defining these domains. But at a minimum you can easily pick out a few domains relevant to your SOC, like I’ve mentioned above.

Define Key Workflow Characteristics and Approaches

Once you’ve established domains you can attempt to define their characteristics. This isn’t something you do in an afternoon, but there are a few clear wins. For instance, triage is heavily suited to divergent thinking and differential diagnosis techniques. On the other hand, hunting is equally reliant on convergent and divergent thinking and is well suited to relational (link) analysis. These are characteristics you can key on in your workflows moving on to the next step.

Apply Positive and Negative Reinforcement in Tools and Processes

Once you know what paths you want analysts to take, how do you reinforce their learning so that they are compelled to do so? While some of us would like to consider a mechanism that provides punishment via electrified keyboards, positive and negative reinforcement are a bit more appropriate. Of course, you can’t give an analyst a treat when they make good decisions, but you can provide reinforcement in other ways.

For an investigation, there is no better positive stimulus than providing easy and immediate access to relevant data. When training analysts, you want to ensure they are smart about what data they gather to support their questioning. Ideally, an analyst only gathers the amount of information the need to get the answer they want. More skilled analysts are able to do this quickly without spending too much time re-querying data sources for more data or whittling excess away from data sets that are too large. Whenever an analyst has a questions and your tool or process helps them answer it in a timely manner, you are positively reinforcing the use of that tool or process. Furthermore, when the answer to that question helps them solve an investigation, you are reinforcing the questions the analyst is putting forth, which helps that analyst learn what questions are most likely to help them achieve results.

Negative reinforcement can be used advantageous here as well. In many cases analysts arrive at points in an investigation where they simply don’t know what questions to ask next. With no questions to ask, the investigation can stall or prematurely end. When chasing a hot lead, this can result in frustration, despair, and hopelessness. If the tools and processes used in your SOC can help facilitate the investigation by helping the analysts determine their next logical set of questions, then that can serve as negative reinforcement by removing the negative stimuli of frustration, despair, and hopelessness. At this point you aren’t only help the analyst further a single investigation, you are once again reinforcing questions that help them learn how to further every subsequent investigation they will conduct.

Other Thoughts

While the previous sections identified some structured approaches you can take towards bettering your analysts, I had a few less structured thoughts I wanted to share in bullet points. These are ways that I think SOC’s can help achieve teaching goals in every day decisions:

  • How can you continually provide positive reinforcement to help analysts learn to make good decisions?
  • If you are making a decision for analysts, let them know. Little things like data normalization and timestamp assumptions can make a difference. Analyst knowledge of these things further help them understand their own data and how we manipulate it for their (hopeful) betterment. Less abstraction from data is critical to understanding the intricacies of complex systems.
  • You must be aware of when you punish your analysts. This occurs when a tool or process prevents the user from getting data they need, takes liberties with data, fails to produce consistent results, etc. If a process or tool is frustrating for a user, then that punishment decreases the likelihood that they will use it, even if it represents a good step in the investigation. You want to at all costs avoid tools and processes that steer your analysts away from good analytic practices.

Conclusion

This is another post that is pretty heavy in theory, but it isn’t so far away from reality that it doesn’t’ have the potential for real impact in the way you make decisions about the processes and tools used in your SOC, and how you train your analysts. As our industry continues to work on developing workflows and technologies we have to think beyond what looks good and what feels right and grasp the underlying cognitive processes that are occurring and the mental challenges we want to help solve. One method for doing this is a thoughtful use of operating condition as a teaching tool.