How can the AI Machine Learning module in the Penetrator Benefit?

The Penetrator Vulnerability Scanner Virtual software, SFF/1U Rack Appliance or Cloud version all has integrated AI Machine Learning module.

The benefit of the AI Machine Learning module is to improve the accuracy of the scan outputs.

When performing vulnerability scanning assessment there is risk of generating false positive output.

This can be occurring if the target site reply back with a non standard output code.

An example is a web server that instead of replying a 404 error code to a file that is non existing on the target web server it will instead reply 200 OK to any request even if a file is not present.

This can it some cases appear in the reports as found vulnerabilities but are not really present.

Normally to combat this the human element is important in double checking all reports.

The Penetrator already features an advanced false positive module where it is easy for humans to remove false positives from a single scan or from the system as a whole.

Example if a system has 4000 completed scans and 2 false positives keep occurring via the global false positive module it is possible to easily remove those 2 false positives across all reports.

Now it can be a time consuming task to look through scans manually and specially when scanning 100s of IP addresses across multiple networks.

To improve and resolve this challenge the new version of the SecPoint Penetrator contains the AI Machine Learning Module.

Here is an example of a scan that was completed.

It has a high amount of Medium risk and Low risk vulnerabilities specifically which looks suspicious.

So for a human this would be required to check manually.

Now in this case here the AI Robot engine has automatically detected a problem and via the engine automatically will fix it.

We can see here when clicking on the scan the AI Robot has fixed the scan lowering High Risk from 9 to 7, Medium Risk from 514 to 22, Low Risk from 53 to 11, Info checks from 28 to 20.

Now the scan result looks much more correct. And the best thing is it happened fully automatic without any human intervention required at all.