Continual learning is a training approach where a model keeps updating as new data arrives instead of being rebuilt from scratch. It is designed for long-lived systems that must adapt to changing patterns while preserving useful older knowledge. In security, this matters because threats evolve quickly: malware families mutate, phishing lures change, and normal user behavior drifts over time. A model that cannot learn continuously becomes stale; one that learns carelessly can suffer catastrophic forgetting, where new training overwrites prior skills.
In practice, continual learning appears in anomaly detection, spam filtering, endpoint defense, and fraud detection. Defenders may use replay buffers, regularization, or consolidation methods to retain earlier patterns while adding new ones. Attackers can target the same weakness by feeding poisoned or misleading samples over time, steering the model toward bad decisions or causing it to forget important alerts. For that reason, continual learning is both a capability and a security risk: it improves adaptability, but it also requires strong data governance, validation, and rollback controls.