Secure deployment of AI

Machine learning models are designed to perform well on clean data sets in laboratory settings. When applied in production, however, machine learning models perform poorly on data contaminated by erroneous input, broken data pipelines, or bad actors. Contaminated data causes state-of-the-art machine learning models to fail in production, leading to critical errors in decisions that rely on these models.