Cybercriminals are always evolving their efforts and coming up with more advanced ways to target their victims. And while there are many tools available to stop them, there is a lot of space for improvement. Especially if you take automation into account.
Machine learning and artificial intelligence are playing a significant role in cybersecurity. Automation tools can prevent, detect, and deal with tons of cyber threats way more efficiently and faster than humans. And it will continue to expand down the road. To that end, here’s a quick look at the significant differences AI/ML technologies can make to corporate cybersecurity approaches.
Mitigating the risks posed by omnipresent technology
Technology has permeated every facet of personal and work lives. Above anything else, it increased their attack surface. And it has become a massive problem for companies in recent years – they have to account for many applications and devices.
The problem is, there aren’t enough skilled human resources to contend with all those security risks. That’s why it often results in gaping vulnerabilities.
To add to that problem, many companies cannot afford having cybersecurity teams needed to secure their applications and systems. Startups, in particular, are at risk. They lack established security operations and the funds to ensure them.
Companies need to automate at least some of the processes necessary to protect their systems and devices from outside attacks. Otherwise, they stay vulnerable.
Criminals are using every tool at their disposal to make sure they have as many points of entry as possible. For example, not even firewalls can protect a system like they used to before, as criminals keep inventing new ways to get around them.
There’s no way to manually contend with this because they’re using automated methods to test the defenses of every connected device.
Better threat detection and management
The size of attacks and vast amounts of data available to analyze makes keeping up with the latest threats a challenging task. Automated machine learning applications are much more suited to constant vigilance and systematic threat identification.
These systems are learning all the time. They can evolve alongside growing threat vectors to spot unusual behaviors. It allows them to identify and process sophisticated attack methods.
But most companies are not making use of these game-changing technologies. They continue to rely on outdated methods. Yet conventional tools and applications cannot keep up with ill-intentioned actors. They keep leveraging more complicated capabilities in their attacks.
Cases such as the Outlaw cryptojacking attacks prove that hackers know how to use new technology to avoid detection. And they are quite successful in their endeavors. The only way to cope with such an onslaught of threats is through machine learning/artificial intelligence engines. They overlook the systems and alert about any suspicious and unusual behaviour.
Automating mundane cybersecurity processes
Many tools exist to cover the security needs of businesses. For example, most companies ask their employees to use virtual private networks (VPNs). (What is a VPN? It’s a service that encrypts users’ connections to the internet (https://nordvpn.com/what-is-a-vpn/).
A tool like that makes sure outsiders can’t intercept any data user is transferring over the network.) And while that covers the data in transfer, there’s still a risk employees will fall for phishing emails or install ransomware by accident.
Security researchers cannot keep up with the threat alert notification overload. And many of these notifications are usually false. But you can’t ignore them. Criminals know how to hide in all that noise. It makes threat identification a monumental task for security operation teams.
Thus, providing information security specialists with automated tools is essential. It lets them focus their skills in areas where they’re most needed. The mundane everyday tasks take up so much of technicians’ time.
But automation tools are capable of handling them. It frees time for more valuable tasks that need a human touch. For instance, threat hunting and attribution.
Considerable increase in risk
The world has grown to incorporate technology into almost every facet of daily life and with that comes a considerable increase in risk. Therefore, machine learning and artificial intelligence have become an indispensable part of cybersecurity.
They fulfil a vital role that human labor simply can’t. Automation is the answer. It can help cybersecurity specialists to tackle the sheer number of cyberthreats in corporate and personal applications.
