Active learning is a machine learning workflow where the model does not wait for a fully labeled dataset. Instead, it selects the next most informative examples for an analyst or tool to label, usually by focusing on uncertain, diverse, or high-impact samples. This reduces labeling cost and speeds up model improvement.
In cyber security, active learning is useful when defenders face huge streams of logs, emails, files, or network events but can only review a small subset. It helps prioritize suspicious alerts for phishing detection, malware classification, and anomaly triage. In practice, an active-learning system may ask for labels on borderline cases, then retrain to improve detection. Attackers can try to manipulate this loop with poisoned samples or noisy feedback, hoping to steer the model away from true threats. Good validation, sampling controls, and analyst oversight help keep the system reliable.