According to a report from Lloyd's Register Foundation,a at present, cybercrime is one of the biggest concerns of Internet users worldwide, with disinformationb ranking highest among such risks (57% of Internet users across all parts of the world, socioeconomic groups, and all ages). For years, there has been a discussion in the security community about whether disinformation should be considered a cyber threat.10 However, recent worldwide phenomena (for example, an increase in the frequency and sophistication of cyberattacks, the 2016 U.S. election interference, the Russian invasion in Ukraine, the COVID-19 pandemic, and so forth) have made disinformation one of the most potent cybersecurity threats for businesses, governments, the media, and society as a whole. In addition, recent breakthroughs in AI have further enabled the creation of highly realistic fake content at scale. As such, we argue that disinformation should be rightfully considered a cyber threat, and therefore developing effective countermeasures is critically necessary.

Why is AI an emerging cybersecurity threat? Artificial intelligence is a booming industry right now with large corporations, researchers, and startups all scrambling to make the most of the trend. From a cybersecurity perspective, there are a few reasons to be concerned about AI. Your threat assessment models need to be updated based on the following developments. Early cybersecurity AI may create a false sense of security. Most machine-learning methods currently in production require users to provide a training data set.

Despite the prevalence of the technologies, a degree of confusion remains around the difference between machine learning (ML) and artificial intelligence (AI). The distinction lies in the fact that machine learning is the practical implementation of artificial intelligence – the use of algorithms to analyse volumes of quantitative and qualitative data, establishing findings and making statistical inferences based on analyzed data. From a cybersecurity perspective, this process is focussed on accurately and efficiently identifying zero-day, unknown threats at the earliest possible opportunity – and at a stage before that which traditional static or behavioral analysis would permit. But machine learning algorithms are not infallible, and should not be treated as such. That said, they can certainly offer a significant boost to security tools, by enabling them to operate proactively as well as reactively when undertaking functions such as anti-malware, anti-spam, anti-fraud and anti-phishing detection.