A replay buffer is a stored set of examples, feature vectors, or model states that a system revisits during later training. Instead of learning only from the newest data, the model mixes old and new samples so earlier knowledge is not overwritten as quickly. This is especially important in continual learning, where a system must adapt over time without suffering catastrophic forgetting.
In cyber security, replay buffers are useful for models that detect malware, phishing, fraud, or intrusion patterns from changing telemetry. New traffic may reflect fresh attacker tactics, but older attack signatures still matter. By replaying representative past examples, a defender can keep a classifier stable and improve long-term detection. The same mechanism can create risk if the stored data is poisoned, too small, or poorly selected, because the model will relearn whatever the buffer contains. Good buffer design therefore becomes part of both model quality and security hygiene.