Today IT security teams are finding it hard to figure out what is an actual threat and what isn’t. The complex network environments have massive data coming in, IT and Artificial intelligence (AI) will help ally to find genuine risk in the midst of all the data if applied correctly.
People do things with their computers that are outright dangerous, they do these things in ignorance. They click on interesting links that lead to malicious sites and download malware onto the system. They store sensitive information in unsecured places. Despite all the data breach if you still think that you are able to do something on your computer, it must be fine.
As a result, the network ends up generating glitches, which set off alerts on a daily basis. Security teams have to go through all these alerts and are still not able to figure out the difference between what’s malicious and what’s not.
This is a huge time suck that is also unsafe. Your network’s security depends on its personnel’s ability to distinguish between the malicious and the non-malicious vulnerabilities. AI and machine learning (ML) can be used to help teams identify which glitch they need to be concerned about and which are harmless.
Is AI the perfect solution?
As said earlier AI and ML technologies need to be applied correctly. You need a smart framework to focus on which discrepancies matters the most. It is recommended that your team focus on 7-10 criteria for vulnerability analysis and leave it at that.
You need to look at vulnerability collectively, this is a starting point, and then you go further. To detect trends and coordinate behaviors. This goes a. it takes an antagonist mindset to implement vulnerability detection, actually, it is a step further than focusing on those 7-10 criteria.
This is a whole new way of looking at network defense. Many solutions and security professionals have focused which criteria is important in terms of vulnerability detection.
As mentioned by Jason Kichen on securityboulevard.com. Adversaries have an ever-expanding method to get inside your network, but once inside, their campaigns must contain three elemental behaviors; reconnaissance, collection and exfiltration:
If you look at anomalies to see if they correlate with these behaviors, the true security picture emerges.
Do it right?
AI and ML will help you discover which security alerts are the most important, but it has often not met with reality. It also does not means that AI and ML eliminate the need for humans, which is not true. AI makes it easy for humans who use AI tools, but the tools themselves cannot work on their own with the help of a seasoned human professional.
As Jason Kichen says “Computers were made to serve humans, and keeping their work easy and data safe. All the activity that seems safe sometimes causes confusion in the network. This leads to security issues. However, AI and ML can help your teams look at the activities from the threat perspective that is likely to harm your network. In this way, teams know what an actual threat is. This eliminates confusion and keeps the network safer.