AI Training Data Poisoning: Protecting Machine Learning Models from Corrupted Data for Singapore Enterprises

Data poisoning attacks introduce malicious data into machine learning training sets — causing models to learn incorrect patterns, misclassify specific inputs, or contain backdoors that attackers can trigger. Security testing for ML pipelines covers: training data integrity verification, data provenance tracking, anomaly detection in training datasets, model behaviour testing against adversarial inputs, and securing the data collection and labelling pipeline from manipulation.