Data ingestion is the process of collecting data from one or more sources and importing it into a system, database, or processing pipeline. In security-sensitive environments, ingestion is more than a simple copy step: it is where organizations decide what data is allowed in, how it is labeled, and whether it can be trusted.
This matters in cyber security because bad or unverified inputs can poison analytics, training data, or automated decisions. Attackers may try to smuggle malicious content, corrupt logs, or introduce poisoned records during ingestion. Defenders reduce this risk with source validation, access controls, integrity checks, schema enforcement, and provenance tracking. In AI pipelines, ingestion is also a governance point: if teams cannot prove where data came from or that they had permission to use it, the model can inherit legal, operational, and security exposure.