The rise of GPU-driven machine learning over the last ten years1 has brought a new level of interest regarding the potential of artificial intelligence in cyber security. The ability of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to find critical patterns in very large datasets is being marketed as a broadly applicable answer to a new and rapid acceleration2 in the rate and scope of cyber security breaches.
Research indicates that cyber security companies adopting AI are receiving notable increases in detection rates for malicious entities and decreased time for positive detection, compared to legacy systems. Increasing AI use in cyber security has also brought a surprising reduction in the complexity of companies’ security architecture.
However, findings also indicate that enterprises lack the technical resources to realize the advertised benefits of AI. As the cyber security market is set to exceed $300 billion by 20243, and the AI-related cyber security market predicted to reach a value of $38.2 billion by 20264, there's clear motivation in the sector to foster association with AI consulting, even when AI doesn’t necessarily apply.
In this article we'll take a look at what constitutes 'AI' in the context of enterprise security, the current trends and inhibiting factors around adoption, and the cultural background that is driving cyber security toward AI-led solutions. We’ll also touch on three of the key areas where machine learning and artificial intelligence have potential to provide new and innovative solutions.