Data quality is the accuracy, completeness, and consistency of data used to train or run AI systems. In security work, this matters because AI tools are only as reliable as the information they consume. If logs are missing fields, labels are wrong, or records conflict, an AI model may learn bad patterns or produce unstable results.
Poor data quality can weaken threat detection, increase false positives, and hide real attacks behind noisy or incomplete telemetry. It also creates an opening for adversaries: data poisoning, tampered training sets, and manipulated inputs can steer models toward incorrect conclusions. Defenders reduce this risk with validation checks, source trust rules, access controls, data lineage tracking, and monitoring for anomalies in training and operational data. Strong data quality is therefore a core part of both AI governance and cyber defense.