A classification task is a machine learning job where a system assigns an input to a label, category, or score. In security, that can mean marking an email as phishing, a file as malware, a login as suspicious, or a message as spam. The model may return a binary answer, a multi-class label, or a probability that supports another rule or workflow.
This matters because many defenses depend on classification to filter traffic, prioritize alerts, and automate response. If the classifier is weak, attackers can exploit it with evasion tricks, adversarial content, or poisoned training data. False positives can block legitimate activity, while false negatives let malicious activity pass. Good security practice includes testing with realistic attack samples, monitoring error rates, keeping human review for high-impact decisions, and limiting how much trust any single model receives.