AI IN THEFT DETECTION

The shift towards digital platforms has revolutionized financial transactions, but it has also fueled a surge in fraudulent activities, particularly identity theft cases that are driven by cyber-attacks. Cybercriminals, leveraging stolen identity information, have devised sophisticated schemes, complicating fraud mitigation efforts. And with the frequency of cybersecurity incidents on the rise each year, organizations face a mass of threats like ransomware and data theft, posing significant challenges across industries.

Deepfake technology — One of the most concerning developments is the use of deepfake technology, a blend of machine learning and media manipulation that allows cybercriminals to create convincingly realistic synthetic media content. Criminals then use deepfakes to spread misinformation, perpetrate financial fraud, and tarnish reputations, exploiting the trust we place in digital media.

AI-powered password cracking — AI algorithms, including machine learning and deep learning, enable systems to identify patterns and make predictions based on vast datasets. For example, PassGAN, an AI-driven password-cracking tool, harnesses machine learning algorithms that operate within a neural network framework. And the tool seems to work, as a study showcasing the effectiveness of PassGAN in password cracking, published by Home Security Heroes, found that 51% of passwords were cracked in less than a minute, 65% in less than an hour, 71% within a day, and 81% within a month.