Model versioning is the practice of tracking each change to a machine learning model, including its training data, parameters, code, and configuration. A versioned model can be identified, restored, and compared with earlier releases, which makes its behavior reproducible instead of mysterious.
In cyber security, versioning matters because models often influence fraud detection, spam filtering, malware classification, and access decisions. If a model starts producing bad results, defenders need to know exactly which version made the decision, what inputs it used, and whether a rollback is safe. Versioning also supports audit trails and incident investigations by showing when a model changed and whether a new release introduced bias, instability, or a security flaw. Attackers may try to poison training data or tamper with a model update; strong version control, signed artifacts, and change logs make those attacks easier to detect and contain.