A dataset-processing pipeline is the sequence of steps that loads, validates, transforms, and prepares data before it is used by a model, analytics job, or application. In practice, it may fetch files from storage or remote sources, run preprocessing code, normalize fields, merge records, and write outputs for later training or inference.
In cyber security, this pipeline matters because it is often more than passive data handling: it can execute scripts, pull dependencies, and access credentials. That makes it part of the software supply chain and a possible attack path for malicious data, poisoned datasets, or unsafe loaders. Defenders reduce risk with strict input validation, signed or trusted sources, sandboxed execution, least-privilege access, and approval gates for code that runs during ingestion or transformation. A weak pipeline can turn ordinary data preparation into a route into production systems.