Machine learning and AI are two close terms. Sometimes, people use them interchangeably. In fact, machine learning is actually the building block for AI and in many cases, there is a pretty clear line between these terms.
In the cybersecurity industry, for example, businesses have been pretty open to machine learning but are skeptical of AI. Enterprises have made various investments in machine learning to bolster their cybersecurity. But when it comes to AI, experts are reluctant to fully employ the technology.
A 2018 report by Enterprise Management Associates revealed that 64 percent of security alerts were not investigated. More than 75 percent of those surveyed admitted to not investigate all of the critical alerts they received. The figures show that corporate security teams are drowned in the mammoth number of security warnings the systems are sending to them.
Machine learning can provide augmented analytics that enables security staff to determine what to investigate, where the security fails to prevent attacks, etc. Machine learning algorithms can do it faster and more thoroughly than humans when they are trained properly. Security teams then have more time and insights to address and resolve the issues and dig deeper into more serious problems.
As there is a critical shortage of skilled security analysts, machine learning tools can, to an extent, bolster the security operations. While we are trying to fill the skills gap, machine learning initiatives are great alternatives for combatting cyberattacks.
If we use machine learning initiatives on authoritative data sets, they can put the issues in the order of priority. That allows security teams to resolve the most urgent and serious ones first to minimize the damages.
The best thing about machine learning models is that they get better as they observe and operate. The more data the model receives, the more cases it handles, the more accurate it becomes. However, the bottom line and hard works are still the staff’s responsibility.
If AI enters the picture, it gives the tool the ability to suggest actions or even take actions if allowed. That sounds pretty amazing and fascinating to get things done without doing anything. Nonetheless, it’s not the case in reality.
The fear of disrupting business has driven security teams away from adopting automated responses to security alerts. They follow practices like the 2-man rule, playbooks, privileged access, and surprise audit to reduce the risk of errors arising from haste, poor judgment, or ignorance.
Of course, the cybersecurity community sees the advantages of automation and starts to open themselves to the application of automation in cybersecurity. However, as AI is currently at the artificial stage which is not advanced enough to ensure the security of the system. It is too risky to put their businesses, reputation, and careers at risk and adopt AI. Cybersecurity experts will adopt AI into their operations when it enters the intelligent era.
For the time being, they are standing at the side, observing other domains deploy, fail, then succeed with AI initiatives. When the technology is mature enough to join the defense of cybersecurity, we will see it blossoming in the market of cybersecurity. For now, machine learning is a good helper to security teams.