How AI and machine learning are changing vulnerability management

Cybersecurity has become a matter of uttermost concern today, especially in our organizations. With the rate at which vulnerabilities are increasing in our systems, we need to take serious measures. Imagine that in 2022, the number of tracked cybersecurity vulnerabilities worldwide increased to 23,964 from 21,518 in 2021. Check Point also reported a 150% increase in cloud vulnerabilities in the last year alone.

Unfortunately, such weaknesses have opened doors to cyber attackers, who have been exploiting them to launch attacks. That is why you need an effective vulnerability management system to help you reduce your risks of a cyberattack, which, by the way, occurs roughly every 39 seconds. And what better way than to take advantage of technologies like AI and machine learning, which can help by automatically detecting a loophole and managing it in advance? Let’s see how that happens.

Why pick modern technologies over traditional methods

Technology is so widespread today and, interestingly, accessible to both the good and the bad. Sadly, many cybercriminals are spending sleepless nights taking advantage of it to come up with more sophisticated cyber threats. Actually, in 2023, 30 million new malware samples were detected. And the traditional methods cannot really hold the fort against that because most of them rely on manual processes, which are usually a bit slow.

Before the system performs periodic scanning, prioritizes the threats based on severity scores, and starts patching things one by one, the damage would have already been done. Plus, there are way too many vulnerabilities for such systems to handle. Hence, there is a need for modern technology solutions, which are at least faster and smarter.

AI-powered threat detection

46% of organizations now use AI to manage their system vulnerabilities because most of them are confident in its ability to detect and block threats and attacks. To begin with, AI can analyze tons of data from logs, network traffic and user activity in real-time to check for any vulnerabilities. This actually makes threat detection faster than when relying on human security teams, who can only catch so much. Plus, it is able to get even the hidden threats that could have otherwise slipped past traditional scans.

With behavioral analysis, AI can learn what a system’s normal functioning looks like so that it can spot and flag any anomalies immediately. For example, if someone suddenly logs into your account from another country at 3 AM, AI can instantly recognize that’s unusual and block access before things go south. Actually, AI-powered tools have slashed incident detection and response times from 168 hours to mere seconds.

Also, AI models can be trained through machine learning to recognize patterns in data that indicate suspicious activity, such as how malware behaves. So, even if a virus is new, AI can tell that it is fishy based on its behavior and shut it down before it spreads. This helps even with Zero-Day threats, which seek to exploit vulnerabilities that even the developers do not know about, meaning no patches exist yet to defend against them. No wonder AI is said to improve threat detection by up to 60%.

Risk prioritization through machine learning

After identifying vulnerabilities, the next task is usually to fix them, and that can’t be done all at once. Since not all security threats are equally dangerous, it is important to address them from those that pose an immediate risk to those with little chance of being exploited. Machine learning has made that easier by using smart data analysis to list the risks accurately in order of priority. Interestingly, it doesn’t rely on generic severity scores but customizes the risk assessment specific to your organization.

What’s beautiful is that the process is actually more proactive than reactive. Machine learning studies past attacks, looking for patterns in how hackers exploit loopholes. It also scans threat-intelligent sources like dark web forums to see which vulnerabilities are in the spotlight at the moment. ML will predict which risk is most likely to be used in an exploit based on the information it gets and flag it as a high priority.

As new threats arise, ML continually analyses them and updates the risk rankings, ensuring the security team always addresses the most pressing issues first. That way, your organization reduces its exposure to cyberattacks more smartly by patching vulnerabilities before attackers get to them.

Automated remediation

With AI and ML, organizations can swiftly fix their security issues without requiring manual intervention. After scanning the systems, detecting the threats, and risk-ranking them, AI can automatically apply security patches when updates are available. Actually, half of the high-risk vulnerabilities found in 84% of companies could be eliminated with a simple software update.

In the event that an attack is already in progress, AI can isolate the compromised devices to help prevent the malware from spreading or even roll back unauthorized system changes to stop the attack. This is crucial because most attacks usually escalate in minutes, and waiting for human intervention might be too late. Not forgetting machine learning makes the system better and faster at identifying and responding to threats.

It is evident that AI and machine learning have made vulnerability management faster and smarter. Now, organizations can utilize them to proactively stay away from the continually advancing online attacks.