How can machine learning complement your existing security solutions?

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.