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You don't have to be a security expert or even a business owner to know that cyber-attacks are some of the biggest threats to our modern, hyper-connected society. We live a big part of our lives online, and with the boom in e-commerce and e-services, most business operations happen online as well.
Plus, we store sensitive data, such as financial, personal, and healthcare information, in digital format. This has massive benefits for everyone involved but also comes with the curse of cybersecurity threats and risks.
This is why organizations and business owners often find themselves talking about cybersecurity strategies. Vulnerability scanning is an essential component of such strategies, but it's not something a regular person can do right.
Still, with the help of cybersecurity specialists, you can make sure your business and its data are safe. Plus, it seems you can use AI technology to improve the process. Moving forward, we'll dig deeper into vulnerability scanning and how AI can help.
At the base, vulnerability scanning can be defined as the process of using software tools to identify security issues in a given environment. These software tools are also called vulnerability scanners and already have a wide range of built-in automated tests (in the number of thousands).
The main purpose of a vulnerability scanner is to probe your systems' defenses and gather useful information, such as outdated or non-patched software that could be exploited. Once the scanning is complete, you get a detailed report on potentially problematic areas that attackers may use to gain unauthorized access to your systems, steal sensitive information, or disrupt business operations.
Using this report, an organization can devise a plan to increase the level of cybersecurity and remediate any serious weaknesses before they become a problem. Since the level and type of cyber threats are always growing and changing, the process of scanning for vulnerabilities and fixing the ones you find (aka Vulnerability Management) is ongoing.
Cyber attacks can (and usually do) impact anyone, regardless of size and organization type. However, more often than not, iron-clad cyber security seems to be something only big guys need to worry about. After all, the cyberattacks we see in the media are all about some major corporations being hit.
However, this doesn't mean small businesses aren't being targeted. In fact, because small businesses tend to brush off the need for a proper-working cybersecurity system, cybercriminals have a better chance of launching a successful attack. And they know this!
So, the issue of Vulnerability Management cannot be taken lightly by anyone who uses online means of communication and business.
The current vulnerability scanners and Vulnerability Management procedures are efficient, but they are not perfect. Also, if you want to understand how standard vulnerability scanning works, check out this comprehensive guide.
For instance, classic solutions are not always efficient with hybrid environments, which include mobile and IoT devices. Also, traditional vulnerability scanners tend to overlook more complex attacks, which include phishing or credential issues.
Furthermore, because classic solutions don't create a list of priorities when it comes to vulnerabilities (from severe to get to it whenever), security professionals have to do extra work when creating the final report that lets organizations understand how to plan their defense.
But, with the use of AI and Machine Learning (ML) systems, vulnerability scanning and management could become much better. Here are a few examples that show how AI can help:
AI and ML can perform data analyses in real-time, which means software tools powered by this technology will be able to prioritize vulnerabilities based on their severity a lot faster than any human specialist could.
Furthermore, AI-powered tools are a lot better at predicting risk factors that are usually indicators of more complex attack vectors, such as phishing or user-related weaknesses.
The weakest link in the chain of any company's security system is the human component. Human users are often the point of entry for many cyber-attacks because they can be tricked into clicking on a link in a phishing email, or they may use insecure methods to store their access credentials (to indicate a few examples).
However, traditional vulnerability scanners don't account for user behavior, which means the weakest link is not even considered. AI-powered tools can fix this by using ML to analyze user behavior and detect anomalies.
Plus, with AI, you can easily create a list of assets that are crucial for the company (servers, cloud storage, tools, resources, and so on) and need better protection.
Classic vulnerability scanning solutions work on the principle of "better safe than sorry," so they'll err on the side of caution when meeting something that could be considered a threat. However, this can (and usually does) result in a long list of false positives that need to be cleared by the security team before handing in the final report to the customer.
When AI technology is used, the system can detect whether or not an identified vulnerability is real (most of the time). This results in an easier-to-sort list of threats, which means less time spent looking for clues and less money spent on vulnerability scanning.
AI and ML tools help improve the classic Vulnerability Management process by making the process of finding, prioritizing, and fixing weaknesses a lot easier and more efficient. It also means that companies will know about their vulnerabilities before they turn into a problem and that security specialists can dedicate more time to other complex issues.
