In a recent survey by Webroot, nearly 75 percent of IT professionals stated their intentions to incorporate more artificial intelligence (AI) solutions into their cybersecurity initiatives in 2019. Despite these ambitious intentions, the same study revealed that a staggering 58 percent of these same respondents don’t completely understand how the technology works.
It’s not surprising — AI and machine learning are frequently reduced to buzzwords. And as digital disruption becomes more competitive, demand has increased without a full comprehension of what the technology is or how it works. But AI is offering multiple use cases for every business — not least of all, better Cybersecurity for the enterprise. What is machine learning?
For starters, it’s a common misconception that AI and machine learning are interchangeable. While the two are linked, AI is the concept of making systems “smart,” enabling them to complete tasks that humans typically complete. Machine learning is one of many methods used to build AI, leveraging data and patterns to replicate human behavior with limited human direction. Machine learning evolution
Machine learning, believe it or not, was born in the 1950s. Mathematician and computer scientist Alan Turing formed the Turing Test to determine if a computer could operate intelligently. When the computer passed the test, machine learning was born. Over the years, machine learning has evolved in several directions, from movement detection to question-and-answer intelligence to identification capabilities. Machine learning is being applied to various new technologies as businesses grow and automation becomes a crucial component of efficiency and daily operations. Autonomous Cybersecurity
But one of the most important applications of machine learning is in the realm of cybersecurity. As businesses lean into big data and cloud migration, machine learning becomes an important component in detecting anomalies within systems. As businesses continue to grow, the machine learning components “learn” at scale, detecting behaviors that are abnormal or that could lead to security threats. This allows the business to mitigate risk autonomously, without having to sift through petabytes of data — the machine learning components automate security to speed up detection and response. Man and Machine
Machine learning is not a replacement for data analysts — but it does make their jobs significantly easier. The two work more efficiently together to protect systems and data. Machine learning can be applied to routine monitoring detection for insider threats, network security issues, zero-day vulnerabilities and more, in ways that change the game forever. And while it may at first seem intimidating for those that have long detected threats manually, educating employees about the value and time-saving capabilities of automation is imperative. Knowledge is power
Much like human beings, machine learning is not perfect. Machine learning algorithms are only as advanced as the data they are being trained with. Unlike a human being, the machine learning algorithms are not advanced enough to “reason.” However, by contrast, machine learning allows companies to sift through large complex data sets autonomously within seconds, with the intent of finding anomalous behavior that may indicate a potential threat to the organization. This simply goes beyond what humans are capable of doing at scale.
As the technology advances, there is progress in solving some of the challenges that come with using the technology. Fortunately, machine learning improves over time, using the right data and the right amount of training. A combined effort
Machine learning is no silver bullet to be implemented across the business without strategy. Although it has the capability to drastically improve business operations — and especially cybersecurity — machine learning is something that requires time and effort to be configured correctly for the best value.