Corporations, governments, and academic institutions all understand the immense opportunity artificial intelligence (AI) and machine learning (ML) bring to their constituents and are increasing their investments. PwC expects the AI market to grow to just under $16 trillion by 2030, or about 12% of global GDP. Given the size of the market and the intellectual property involved, one would think appropriate investments have been made to secure these assets. AI and ML has become the largest cybersecurity attack vector. The Adversarial AI Incident Database provides thousands of examples of AI attacks across multiple industries and corporations, including Tesla, Facebook, and Microsoft.

"Any AI smart enough to pass a Turing test is smart enough to know to fail it." Suppose you are working on a high-impact yet challenging problem of malware classification. You have a large dataset at your disposal and are able to train a machine learning classifier with an accuracy of 98%. While suppressing your excitement, you convince the team to deploy the model, as who would resist a model with such an amazing performance? Quite disappointingly, the model fails to detect threats in the real world!?