3 Ways to Improve Pen Testing With Machine Learning

Hackers and cybersecurity professionals are in an arms race. One side creates new methods of attacks, and the other finds ways to defend against them. Now, both sides are seeking to integrate machine learning and artificial intelligence into their arsenals to gain an advantage.

What is vulnerability assessment and penetration testing?

New software undergoes vulnerability assessment and penetration testing (VAPT) to ensure it can defend against known cyberthreats. This is a security testing process to detect any possible vulnerabilities software might have.

VAPT is typically a two-part process. The first is vulnerability assessment, which uses automation programs. Administrators utilize specialized scanning programs to search for weaknesses common in software. Once found, they can patch those openings to prevent cyberattackers from using them.

Penetration testing is a much more complex process. It involves hacking the software application itself to see if attackers can exploit functions and create openings in security protocols.

Learn more: 9 Penetration Testing Tools The Pros Use

Unlike vulnerability assessments, this testing stage is more intense and should only be performed by cybersecurity professionals. They will manipulate the software’s code like a hacker would. This can damage the software if not done correctly, rendering it useless.

While it might seem like a lot of time and money spent, cybersecurity and IT risk assessments are crucial to protecting a business’s data in the 21st century. About 44% of companies said cyber incidents were their top risk in 2022, and many were unprepared to defend against them. Having your computer systems undergo VAPT testing is essential.

3 ways machine learning can improve VAPT Testing

Machine learning is a term used to describe a form of predictive artificial intelligence technology. This type of AI uses a complex algorithm to record data and make predictions based on that data. For example, Google implemented machine learning into its search engines to predict what a user might be searching for based on their search history.

Machine learning AI has numerous applications but can be a game changer in cybersecurity.

1. Automation in testing

One of the reasons VAPT is time-consuming is that it can’t be reliably automated. Current scanning programs can probe software for security vulnerabilities but can only display these as findings. It’s up to cybersecurity professionals to act on them.

Professionals must manually review large data sets to find and fix potential security concerns. This process must be comprehensive, so a full VAPT test can take a lot of time.

Machine learning AI can solve this problem by performing scans and making security corrections and patches. They can analyze and understand what must be done to fix the issue.

This can reduce the human interaction necessary for testing — making the process less time-consuming.

2. Deep exploitation testing

VAPT testing powered by machine learning acts autonomously to fix security vulnerabilities and tests software to a much deeper extent than traditional programs. Because machine learning is so adaptable, it can use data from previous scans to probe for potential vulnerabilities that are less obvious.

Machine learning can also be used in the penetration testing phase, which is challenging because it requires human coders to test the software for possible exploits. ML can accomplish this task by checking specific areas — something conventional programs can’t do. Current testing programs can probe and look for vulnerabilities that have already been documented but can’t search beyond those parameters.

Machine learning algorithms can do a more intensive scan, probing for vulnerabilities in places that might not be obvious based on records of recent attacks. AI can infiltrate software like a real hacker would, pointing out a greater variety of potential weak points.

3. More comprehensive reports and analysis

VAPT conducted by machine learning can also provide much more comprehensive reports than conventional programs. Because of its predictive capabilities, it can inform administrators what cybersecurity steps they might need.

For example, machine learning can predict the probability of cyberattacks and malware attempting to enter software based on the amount of internet traffic handled. This can allow administrators to take additional security measures.

Machine learning is the future of VAPT Testing

Machine learning applications in cybersecurity are numerous — especially in VAPT testing. Learning programs can find and fix vulnerabilities, perform tests based on data collected and report more comprehensive findings. Artificial intelligence will become a powerful tool in cyberattack prevention as machine learning technology improves.