enterprisesecuritymag

How Does AI and ML Influence Security Testing?

By Enterprise Security Magazine | Wednesday, January 06, 2021

The inclusion of AI will result in the automation of the data collection process and save valuable time for security teams.

FREMONT, CA: As the cybersecurity community embraces various benefits that artificial intelligence and machine learning bring, the tremendous effect that AI may have on the software security testing process as a whole is a somewhat unprecedented advantage coming to light.

Not only can Artificial Intelligence (AI) and Machine Learning (ML) automate multiple menial testing processes save a lot of valuable IT resources—such as more manageable web hosting costs, shorter and quicker DevOps engineering cycles, and overall less technological overhead—but 

their use in corporate security testing can also significantly increase the overall value of the testing process by producing nearly flawless performance.

Collecting Data Made Easy

One of the most critical aspects of ensuring that the safety checks' outcomes are as flawless as possible is information use. Simply put, the bigger your data pool is, the higher the likelihood of a good safety test being carried out. Since the software security testing process uses a broad data collection, collecting all that information could prove to be highly labor-intensive and time-consuming, which is where the advantages of AI can be used.

The inclusion of AI will result in the automation of the data collection process and save valuable time for security teams. Besides, for an even more efficient approach to the security testing process, a company might have its security teams integrating both AI and ML programs, which cover both the software and hardware components, and accounts for every machine and device involved on the network.

Harnessing Machine Learning Strength in Application Scanning

The crucial phase of security testing is application scanning, which exhibits all the smallest and most vital problems inside the application being reviewed to the security teams. Organizations may combine Machine Learning with application scans to reduce the amount of manual labor needed to find vulnerabilities on the network. However, the discoveries made by the ML-powered application scans should regularly be reviewed by the organization's test engineers to assess whether or not the findings are correct. The security team also needs to prioritize the vulnerabilities found and fix them accordingly.

Perhaps the most significant benefit of using machine learning optimized application scanning software is that it encourages more accurate outcomes by filtering out any irrelevant piece of knowledge. In other words, machine learning provides increasingly accurate results by concentrating on a smaller data set rather than an analysis of the entire data set. Additionally, the introduction of machine learning into application scanning also dramatically decreases the time needed for security testing. It allows the automation of new application scans to be carried out.

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