Model training is the stage where an AI system learns patterns from data before it is deployed. During training, the model adjusts its internal parameters to reduce errors and improve later predictions or generated outputs. This process can require large datasets, specialized hardware, and carefully managed pipelines.
In cyber security, training is a high-value target because weaknesses at this stage can affect every downstream use of the model. Attackers may try data poisoning, inserting malicious or biased examples to influence behavior, or model theft, copying trained weights and tuning work. Defenders protect training data, restrict access to compute, verify datasets, monitor for tampering, and track provenance so the model can be trusted. Secure training is essential because a compromised model may look normal during deployment while quietly producing unsafe, inaccurate, or exploitable results.