by Steve Schlarman – May 7, 2010
A few weeks ago, I wrote about the convergence of business data and risk management techniques to improve understanding of the “likelihood” part of risk assessments. Those thoughts were fueled by discussions at the Archer GRC Summit that highlighted the improved decision making capabilities of companies that are organizing and gathering data on a more consistent basis and integrating it into the risk management process. I recently read an interesting essay via Bruce Schneier’s blog, exploring the root cause of the airline shutdowns in Europe in response to the volcanic activity in Iceland. The essay explored the impact of “worse case” thinking to managing risks, and I realized there was an interesting connection between the essay and my recent blog post.
The essay focuses on how authorities reacted based on the worst possible scenario when analyzing the impact of the ash cloud over the European continent and the resulting decision to ground all airline traffic. If taken into the world of IT, this would be akin to dropping all Internet connectivity based on the “worst case” scenario of a major breach and resulting costs in reputation or actual losses. In fact, if we would focus on ALL of the worst case scenarios, most companies would go back to pen and paper records and forgo computers altogether. However, the reality is completely opposite; companies are embracing technology faster than ever. So we, as GRC professionals, are challenged to understand “worse cases” and to determine the most effective and efficient methods to protect against these scenarios.
This leads me to continue my exploration of integrating actual data from business operations into GRC processes. Most often, there are two inputs into risk management decisions:
• Anecdotal, qualitative information
• Tangible, quantitative information
Anecdotal, qualitative information flows in via questions, manual assessments and other similar activities. Performing risk assessments via stakeholder interviews is one example of this type of input. These risk assessments can highlight tactical issues that can be tracked and remediated individually. If the risk assessments can be conducted over a solid sampling of targets, such as performing an application risk assessment against a set of applications, consistent failures may indicate a systemic issue with specific controls. For example, if within the application risk assessment, applications consistently fail questions related to change control, then there may be an issue with the change management processes. This may indicate a risk related to managing changes to applications or infrastructure within IT operations.
Tangible, quantitative information is harder to come by but can really improve the picture. In my previous blog entry, I cite system utilization events or data access attempts as examples of actual numbers associated with potential risks. This is the type of data resident in operational systems that can be mined to give insight into potential risks. However, this data also must be planned for—meaning, for instance, systems that generate the data must be configured to log events and the organization must have the capability to gather that data together into a single place for mining. Security and information event management (SIEM) systems such as RSA enVision can be a fantastic source of this type of tangible data.
Combining qualitative and quantitative information can be a challenge, but the value of multi-dimensional inputs into the risk analysis process is significant. I’m sure European authorities would have liked to have more data before grounding flights and dealing with the economic and social impact of their decisions. For GRC professionals, we should look to define anecdotal indicators of risks as well as tangible metrics as inputs into our decision processes. This approach must be paired with studying the capabilities of the organization to generate both types of data. In other words, how well can the organization sample qualitative evidence of potential risks as well as gather empirical event data into one picture?
Tactically speaking, this translates into an organization’s capability to build consistent risk assessment content, such as common questionnaires, and sample a broad set of targets to get enough data to identity systematic issues. Secondly, metrics and associated data (measurable, trackable, identifiable events) must be defined and gathered. Both highlight the need for technologies to support risk management processes and a continued evolution of the risk analysis processes. The end result—having qualitative and quantitative inputs into the risk decision process—is a substantial enhancement to the risk management capability of the organization.
If you’re interested in hearing more on this subject, I invite you to join me for an upcoming webcast that I’ll be co-presenting with Sam Curry and Paul Stamp of RSA. We’ll show the integration of RSA enVision and Archer, and we’ll discuss how this integration provides an organization with a broader view of their risk landscape and a more efficient, cost-effective way to handle compliance challenges.
Here are the details of this event:
When: Tuesday, May 18 at 2 p.m. US Eastern
Registration: http://info.rsasecurity.com/2010Am/webcast/100518_Archer_Compliance/online.html
I hope you’ll plan to join us.