When it comes to training AI/ML models, a popular debate is whether "supervised" or "unsupervised" learning should be used. Supervised learning is based on labeled data and features extracted to derive a prediction model. For malware, this means human experts classify each sample in the data set as good or bad, and feature-engineering is performed to determine what attributes of the malware are relevant to the prediction model prior to training. Unsupervised learning gleans patterns and determines structure from data that is not labeled or categorized. Unsupervised learning proponents claim that it is not limited by the boundaries of human classification and remains free from feature-selection bias.

Artificial Intelligence (AI) is a term that is readily recognisable but often misunderstood. It is frequently misused by the media, the hype around potential and expectations is huge, and as a result, rarely enables business owners the ability to see how true AI can be applied. Business expectations are not aligned to the adoption of AI and this means that many firms are not benefitting from the exceptional possibilities that technological advancements can offer right now. It is important not to be drowned in the promise of the future by sacrificing the potential of the present day. AI is often viewed as the tool that will revolutionise a business's entire operations, or that it will take over and render human workers unnecessary.