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Achieving efficiency

The impact of ML and AI in security testing

Erna Clayton
© Shutterstock / Sombat Muycheen

Manual efforts to gather such a huge amount of information could eat up a lot of time. Hence, AI is leveraged to automate the stage and deliver flawless results while saving a lot of time and resources. Embedded AI and ML can help security testing teams in delivering greater value through automation of audit processes that are more secure and reliable.

Artificial Intelligence (AI) has come a long way from just being a dream to becoming an integral part of our lives. From self-driving cars to smart assistants including Alexa, every industry vertical is leveraging the capabilities of AI. The software testing industry is also leveraging AI to enhance security testing efforts while automating human testing efforts.

AI and ML-based security testing efforts are helping test engineers to save a lot of time while ensuring the delivery of robust security solutions for apps and enterprises.

#1 Reconnaissance stage

During security testing, it is essential to gather as much information as you can to increase the odds of your success. Hence, it is crucial to analyze the target carefully to gather the maximum amount of information.

Manual efforts to gather such a huge amount of information could eat up a lot of time. Hence, AI is leveraged to automate the stage and deliver flawless results while saving a lot of time and resources. Security experts can use the combination of AI and ML to identify a massive variety of details including the software and hardware component of computers and the network they are deployed on.

SEE ALSO: Amazon’s new ML service Amazon CodeGuru: Let machine learning optimize your Java code

#2 ML-backend scanning

Applying machine learning to the application scan results can help in a significant reduction of manual labor that is used in identifying whether the issue is exploitable or not. However, findings should always be reviewed by test engineers to decide whether the findings are accurate.

The key benefit that ML offers is its capability to filter out huge chunks of information during the scanning phase. It helps focus on a smaller block of actionable data, which offers reliable results while significantly reducing scan audit times.

An ML-based security scan results audit can significantly reduce the time required for security testing services. Machine learning classifiers can be trained through knowledge and data generated through previous tests for automation of new scan results processing. It can help enterprises triage static code results. Organizations can benefit from a large pool of data collated through multiple scans ongoing on a regular basis to get more contextual results.

#3 Gaining and maintaining access stage

This stage includes controlling multiple network devices to churn out data from the target or leverage the devices to launch attacks on multiple targets. After scanning the vulnerabilities, test engineers are required to ensure that the system is free of flaws that be used by attackers to affect the system.

AI-based algorithms can help ensure the protection of network devices by suggesting multiple combinations of strong passwords. Machine learning can be programmed to identify the vulnerability of the system though observation of user data while identifying patterns to make possible suggestions about used passwords.

AI can also be used to access the network on a regular basis to ensure that any security loophole is not building up. The algorithm’s capability should include identification of new admin accounts, new network access channels, encrypted channels and backdoors among others.

SEE ALSO: Artificial intelligence & machine learning: The brain of a smart city

#4 More efficiency

ML-backed security testing services can significantly reduce triage pain because triage takes a lot of time if organizations rely on manual efforts. Manual security testing efforts would require a large workforce to go through all the scan results only and will take a lot of time to develop efficient triage. Hence, manual security testing is neither feasible nor scalable to meet the security needs of enterprises.

Aside, application inventory numbers used to be in the hundreds before, but now enterprises are dealing with thousands of apps. With organizations scanning their apps every month, the challenges are only increasing for security testing teams. Test engineers are constantly trying to reduce the odds of potential attacks while enhancing efficiency to keep pace with agile and continuous development environment.

Embedded AI and ML can help security testing teams in delivering greater value through automation of audit processes that are more secure and reliable.

Author

Erna Clayton

Erna is a technology enthusiast with expertise in software testing. She has been reading and writing extensively over current software testing trends. She also keeps a keen interest in futuristic technologies. She also writes for QA Infotech.


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