Systematic distortion in AI training datasets that leads models to over-represent or under-represent certain categories, populations, or scenarios. In cybersecurity, it can compromise the effectiveness of anomaly detection systems, threat classifiers, and predictive models, generating false negatives on attacks poorly represented in training data or false positives on rare but legitimate behaviors. A critical issue for machine learning-based detection and automated threat intelligence.