When it comes to combating an ever-increasing number of cyberattacks and vulnerabilities in what can only be termed as an ‘ever-evolving’ threat landscape, enterprises need to realize the importance of frequent security tests, and the role that they play in fostering cybersecurity within organizations.
On paper, the prospect of routinely testing the deployed software and programs in an organization seems to be a feat almost too easy to accomplish. Quite on the contrary, the reality of the security testing phenomenon could not be any more different. In order to ensure that the software being tested is completely secure, most organizations rely on a lot of context clues, which include all the relevant bits of information and data. A disadvantage of this, however, is that companies are expected to bear the costs of the actual tests, along with all the manual labor that goes into the gathering of such a large amount of data.
Fortunately, as the cybersecurity world welcomes with open arms the multiple benefits that artificial intelligence and machine learning have to offer, a rather unprecedented advantage that comes into light is the enormous (positive) impact that AI could have in the software security testing process as a whole.
Not only can AI and ML automate several menial testing processes and therefore, save a lot of valuable resources, their inclusion in an organization’s security testing can also immensely improve the overall value of the testing process by delivering near-flawless results.
Long gone are the days where organizations could only dream about harnessing the power of artificial intelligence and machine learning since more and more industries are integrating AI. Some ways through which the capabilities of AI and ML can be steered to bring about a more effective and reliable security testing process include the following:
As we’ve already discussed above, one of the most critical aspects of ensuring that the results of the security tests are as flawless as possible is through the use of information. Simply put, the larger that your data pool is, the higher are the chances of the security test being conducted successfully.
Since a software security testing process makes use of a large data set, gathering all that information could prove to be extremely labor and time consuming, which is where the advantages of AI can be made use of.
The inclusion of AI can result in the automation of the data collection process, and end up saving the valuable time of security teams as well. Furthermore, for an even more effective approach to the security testing process, an organization could have it’s security teams combine both AI and ML systems, which covers both the software and hardware component, and accounts for every computer and device active on the network.
One of the key aspects of security testing is application scanning, which reveals to the security teams all the smallest and biggest issues present within the application being tested. Having said that, organizations can amalgamate Machine Learning to application scans in order to minimize the amount of manual labor required to identify the vulnerabilities present on a network.
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Having said that, however, the discoveries made by the ML-enabled application scans should always be second-checked by the organization’s test engineers to determine whether the findings are accurate or not. Moreover, the security team also needs to prioritize the discovered vulnerabilities, and remedy them accordingly.
Perhaps the biggest advantage in using machine learning optimized application scanning tools is that it fosters more reliable results by filtering out any chunks of information that are irrelevant. In other, simpler words, machine learning offers increasingly reliable results by focusing on a smaller data set, rather than analyzing the whole data set.
And if that wasn’t enough, the integration of machine learning into application scanning also significantly reduces the time required for security testing, since it enables the automation of the new application scans being conducted.
The access stage refers to the multiple devices, or “identities” on the network that are controlled by the security team to discover the vulnerabilities present on the network. It is the same as that of a VPN that protects the user from spying eyes, find out more about it.
After the security testing process is complete, the organization’s test engineers are tasked with the tedious process of ensuring that the network is free from any flaws that might be utilized by hackers, and cybercriminals to gain entry into the system.
The inclusion of AI can greatly benefit the access stage, since the deployment of AI-based algorithms can ensure that the multiple devices and computers on the network are protected, by using several sophisticated and complex passwords.
Additionally, organizations may also rely on machine learning to help them diagnose the vulnerabilities that threaten to expose confidential data, by identifying user patterns, habits, and suggesting steps that could help fix those flawed trends.
Last but certainly not least, one of the most profound ways through which AI and ML can help strengthen the security testing processes in an organization is by providing to organizations more efficient operations, which consequently result in a more efficient result.
Some ways through which AI streamlines the testing process is by the automation of several menial processes, which reduces the need for manual labor investments. Moreover, AI and ML also enable the security teams to deliver a greater level of value, by enabling them to provide more accurate results.
At the end of the article, we can only hope that we’ve brought into light some of the more unprecedented advantages that AI and ML have to offer, particularly as far as the security testing phenomenon is concerned, which in turn fosters an environment that promotes cybersecurity amidst the ever-evolving threat landscape of today.