Data analytics may be essential to the success of your business, but they’re not going to mean a thing if you don’t know how to collect, interpret, and apply data properly in your business decision-making. Perhaps the greatest threat you’re going to encounter when it comes to data isn’t the lack of information, but rather the influence of optimism bias in managing that information. But what is optimism bias, what are the warning signs that it’s undermining your cybersecurity analytics, and what can be done about it?
What is Optimism Bias?
When it comes to something we want or need, we all want to hope for the best. Often, though, we don’t just hope for the best, we also see the best. Both consciously and unconsciously, we tend to more readily pick up on and amplify positive indicators while overlooking, downplaying, and disregarding entirely negative ones.
On one level, of course, optimism bias is not necessarily a bad thing. In fact, it has a helpful evolutionary basis, giving us a sense of control and hope even in challenging times. Such an optimistic attitude can help us to keep moving forward, to keep striving, even when times get tough. After all, there’s no better way to fortify your determination than by believing you already see the rays of a brighter tomorrow beginning to shine on you and yours.
The problem, though, is that optimism bias can also easily and quickly lull us into a false sense of security. The complacency that results can lead us not to act on signs of potential trouble ahead, even when they’re obvious and even when there’s still time to avert the disaster. All too often, optimism bias doesn’t just get us into trouble, but it also causes us to be caught entirely by surprises, and, therefore, completely unprepared, when trouble comes.
What that means is that optimism bias isn’t just a danger to individuals and the choices they make in their personal lives, but it’s also a profound threat to your business. If you want to make sound business decisions, in other words, it’s not enough just to have the data you need, you also need to ensure you’re interpreting and applying the data appropriately. That means eliminating the optimism bias from your business’s cybersecurity analytics.
Warning Signs and Prevention Strategies
Because optimism bias is almost always an unconscious act, it can be very difficult to detect, let alone prevent it. There are, however, several warning signs you and your company can be watching for.
The first and perhaps most important sign that your business is at risk for optimism bias is if you don’t have a highly trained data analyst on staff. Data analytics is a complex endeavor. Not only are there myriad ways to interpret data, but there are also diverse ways of collecting it.
Few people who have not been specifically trained in the arena of business analytics have the skills to do it properly, and that means that they will be more likely to fall unwittingly into the trap of optimism bias.
Thus, they may subconsciously choose to mine only the data that is most likely to confirm their optimistic expectations for their company, or they may reject or fail to collect data that may conflict with those positive projections. Similarly, they may fail to interpret data properly, exaggerating positive signs or seeing them where they don’t exist at all, while at the same time misinterpreting or underplaying the warnings.
You can also detect signs of optimism bias in the behavior of your employees. For example, if you notice that your staff are engaging in risky behaviors on campus, even in the face of explicit prohibitions, then that could well be a sign that your workers have fallen into the trap of optimism bias, the covert belief that nothing bad could happen to them, their coworkers, or their company.
Signs of optimism bias among your workers don’t just have to apply to the collection and analysis of data, though. If you find your employees “forgetting” to wear hard hats on a construction site, or you notice they’re using work computers to access risky sites, that can indicate that the phenomenon has taken hold in your workplace.
When that happens, you’re going to need to be proactive, from redoubling your employee training efforts to posting more and better signage to remind workers of the threats to which they may have grown inured.
This should also include intensifying cybersecurity measures organization-wide. No matter what your particular industry, for instance, cyber threats are increasing rapidly, and a complacent workforce stricken by optimism bias may well become careless, even reckless, with your company’s tech, sometimes with devastating consequences.
Particularly for those working in cybersecurity, burnout is a real problem among analysts who experience alert fatigue and are tasked with poring over data day-in and day-out. Burnout is a diagnosable condition that affects nearly two-thirds of people across a variety of industries. In the analytics field, burnout may lead to optimism bias.
Cybersecurity analysts who are burned out may be more likely to take a business-as-usual approach, relying on analytics tools that aren’t up-to-date or processes that limit the scope of their analysis. Simply put, if you’re too tired to be vigilant, you’re more likely to make mistakes. To guard against this, IT leaders must watch for signs of burnout in their employees, including low motivation, fatigue, sleeplessness, and headaches. Encourage burned-out employees to take time off, and improve AI-powered threat detection as well as automated response systems.
Optimism bias is real, it is prolific, and it can pose a profound threat to your business, your employees, and your clients alike. The good news, however, is that optimism bias can be prevented if you know the warning signs and how best to respond to them.