Model training data is the collection of text, images, logs, records, or other examples used to build or improve a machine learning model. It teaches the model patterns, language, and decisions. In AI systems, the quality and handling of this data often matter as much as the model itself.
In cyber security, training data is sensitive because it can contain personal information, confidential business content, or security-relevant details. Attackers may try to poison it with malicious examples, causing the model to learn unsafe behavior. They may also attempt data extraction or membership inference to recover private information that influenced training. Defenders reduce this risk by minimizing collected data, filtering secrets, controlling access, encrypting data, and separating customer content from model improvement pipelines. In regulated environments, clear rules about whether conversation data is used for training are a core privacy control.