Musings of a Cognitive Risk Manager
To drive change, you need buy-in, and to achieve buy-in, your people need to know the “why” behind the change. This is the premise behind cognitive risk governance, the “designer” of human-centered risk management. James Bone, author of Cognitive Hack: The New Battleground in Cybersecurity…the Human Mind, further explains the cogrisk framework.
In my last article, I explained the difference between traditional risk management and human-centered risk management and began building the case for why we must re-imagine risk management for the 21st century. I purposely did not get into the details right away, because it is really important to understand why a thing must change before change can really happen. In fact, change is almost impossible without understanding why.
Why put on sunscreen if you don’t know that skin cancer is caused by too much exposure to ultraviolet rays from the sun? We know that drinking and driving is one of the deadly causes of highway fatalities, but we still do it! Knowing the risk of a thing doesn’t prevent us from taking the chance anyway. This is why diets are so hard to maintain and habits are so hard to change. We humans do irrational things for reasons we don’t fully understand. That is precisely why we need cognitive risk governance.
Cognitive risk governance is the “designer” of human-centered risk management! The sunscreen is effective (if you use it properly!) because the ingredients were formulated to protect our skin from ultraviolet rays. Diets are designed to help us lose weight. Therefore, cognitive risk governance must also design the outcomes we seek.
This is radically different from any other risk framework. If you take the time to study any framework, 99 percent of the guidance is focused on the details of the activity you must do first. Do risk assessments, develop internal controls and create policies and procedures, blah blah blah … The details are important, but what if your focus is on the wrong stuff? This is often the case. If you have ever heard the term, “shoot first, then aim,” you fully understand why most risk frameworks don’t work.
The fallacy of action is the root cause of failure in risk management programs.
It is really important to understand this concept, so let me provide an illustration: If you want to create a fuel-efficient car, you must first design the car to get more mileage with the same amount of fuel.
In order to achieve better efficiency, you must understand why cars are not fuel efficient. In order to fully understand why cars are not fuel efficient, manufacturers must reimagine the car.
However, before you start changing the car, you must decide how efficient you want the car to become.
Design starts with imaging the end state, then determining what steps to take to achieve the goal. This is how cognitive risk governance works in human-centered risk management.
The role of cognitive risk governance is to design new ways to reduce risks across the organization. In order to reduce risks, we must understand why certain risks exist and determine the right reduction in risk we want to achieve. This is why cognitive risk governance is a radical departure from traditional risk management.
In contrast, traditional risk management advocates a Top 10 list of risks or a risk repository that inventories events. Unfortunately, the goal seems to be focused on monitoring risks, as opposed to risk reduction. Risks cannot be completely eliminated, therefore any “activity-focused” risk program will always find new risks to add to the list. A human-centered risk management program is focused on reducing risks to acceptable levels through design. But not all risks! The focus is on complex risks.
Cognitive risk governance is the process of designing human-centered risk management to address the most complex risks. Any distribution of risk data will tell you that 75 to 80 percent of risks are high-frequency/low-impact risks, yet traditional risk programs focus 90 percent of their energy on dealing with the least important risks. The opportunity presented by a cogrisk governance model is to separate risks into appropriate levels of importance. Risks represent a range (distribution) of outcomes; therefore, one-dimensional approaches to address risks will inevitably not address the full range of complex risks.
Developing a Cognitive Risk Governance Toolkit
The toolkit for designing cognitive risk governance involves an understanding of a few concepts that any organization can implement.
Cognitive risk governance starts with a clear understanding of the difference between “uncertainty” and “risk.” Uncertainty is simply what you do not know or don’t have clear insight into the impact of. Risks are known, but knowing of a risk doesn’t mean you fully understand the nature of the risk. I do not subscribe to the semantic exercise of Known-Knowns, Known-Unknowns and Unknown-Unknowns. There is no rigor in this exercise, nor does in provide new insights into solving problems of importance.
The next concept in a cogrisk governance program involves developing risk intelligence and active defense. Risk intelligence consists of quantitative and qualitative data from which analysts are better able to develop insights into complex risks. The processes of data management, data analysis and the formulation of risk intelligence may require a multidisciplinary team of experts, depending on the complexity of the organization and its risk profile.
Active defense, on the other hand, is the process of implementing targeted solutions driven by risk intelligence to capture new opportunities and reduce risk exposures that impede growth. Risk Intelligence and active defense will require solutions and new tools that may not be in use in traditional risk programs. Organizations are generating petabytes of data that are seldom leveraged strategically to manage risk. A cogrisk governance program is responsible for designing risk intelligence and active defense in ways that leverage these stores of data, as well as external sources of intelligence.
In traditional risk, the “three lines of defense” model is a common approach used to defend the organization, yet to understand why a change is needed, one need only look at how the military is re-engineering its workforce to a 21st century model to address the new battleground being fought with technology and cognition. It is no longer a reasonable assumption to expect an army of people with limited tools to be able to analyze the movement of petabytes of data into, across and outside of an organization with confidence.
The transformation in the military is being led by the Joint Chiefs of Command, which is a corollary for risk, compliance, audit and IT professionals. Risk professionals must lead the change from 19th century risk practice to 21st century human-centered risk management. Existing risk frameworks such as COSO, ISO and Basel have laid a good foundation from which to build, but more needs to be done.
I will address these opportunities in more detail in subsequent articles, but for now, let’s move to the next concept in a cogrisk governance model. The intersection of human-machine interactions has been identified as a critical vulnerability in cybersecurity. However, poorly designed workstations that require employees to cobble together disparate data and systems to complete work tasks represent inefficiencies that create unanticipated risks in the form of human error.
The intersection of the human-machine interaction represents two significant opportunities in a human-centered risk management program. The first opportunity is an improvement in cybersecurity vulnerability, and the second is the capture of more efficient processes in productivity gains and reductions in high-frequency/low-impact risks. I will postpone the discussion on the opportunity to improve cybersecurity to subsequent articles because of the scope of the discussion. However, I do want to mention that a focus on reducing human error risks is unappreciated.
The equation is a simple one, but very few organizations ever take the time to calculate the cost of inefficiency, even in firms with advanced Six Sigma programs. Here is an oversimplified model: Human error (75 percent) + uncontrollable risks (25 percent) = operational inefficiency (100 percent). From here, it is easy to see the benefit of human-centered risk management. This is obviously a simplified model, including the statistical data, but not far from reality, if you look at empirical cross-industry analysis.
Human-centered risk management focuses on redesigning the causes of human error, providing real payback in efficiency and business objectives. A risk program designed to facilitate safe and efficient interactions with technology improves risk management and helps grow business. More on that topic later!
In the next article, I will discuss intentional control design and practical use cases for machine learning and artificial intelligence in risk management.
As I have done in previous articles, I invite others to become active participants in helping design a human-centered risk management program and contribute to this effort. If you are a risk professional, auditor, compliance officer, technology vendor or simply an interested party, I hope that you see the benefit of these writings and contribute if you have real-life examples.