MLOps Blog Series Part 4: Testing security of secure machine learning systems using MLOps

The growing adoption of data-driven and machine learning–based solutions is driving the need for businesses to handle growing workloads, exposing them to extra levels of complexities and vulnerabilities. Cybersecurity is the biggest risk for AI developers and adopters. According to a survey released by Deloitte, in July 2020, 62 percent of adopters saw cybersecurity risks as a significant or extreme threat, but only 39 percent said they felt prepared to address those risks. In Figure 1, we can observe possible attacks on a machine learning system (in the training and inference stages). To know more about how these attacks are carried out, check out the Engineering MLOps book.