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Cyberattacks are becoming more advanced and are occurring more often, with the cost of cybercrime expected to reach $15.63 trillion by 2029. Older security methods just can’t keep up with evolving threats, which is where Artificial Intelligence (AI) and Machine Learning (ML) come in.
We continue our series of articles focused on testing systems that incorporate Multi-Factor Authentication (MFA or 2FA) security mechanisms. In our previous article about MFA testing, we explored why companies operating in regulated industries must adopt these mechanisms to strengthen their security.
In an industry where financial transactions, personal data, and live betting are integral, the reliability and security of horse racing apps must be airtight.
In this article, Priya Yesare explains why AI driven software testing is faster, more efficient and more reliable. AI addresses the limitations of traditional test automation by incorporating machine learning, large language models (LLM), natural language processing and predictive analysis to automate complex tasks with improved accuracy.
When reviewing thousands of lines of code and testing all the different use cases imaginable, it can be challenging to know where to begin or what to prioritize first.
Welcome to this series of three articles dedicated to an in-depth analysis of testing systems that integrate multi-factor authentication (MFA) mechanisms. If you work in a regulated entity, particularly in the financial or banking sectors, you have likely faced the challenges associated with testing MFA-protected workflows such as authentication and financial transactions.
Telemedicine has revolutionized healthcare by furnishing remote access to medical services and real-time case monitoring. Still, the software that powers these platforms faces unique testing challenges beyond those encountered in standard mobile or web operations.
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