Risks and Attacks on ML Models

This chapter gives a detailed overview of defining and evaluating a Machine Learning (ML) risk framework from the instant an organization plans to embark on AI digital transformation. Risks may come in different stages, such as when the strategic or financial planning kicks in or during several of the execution phases. Risks start surfacing with the onset of technical implementations and continue up to testing phases when the AI use case is served to customers. Risk quantification can be attained through different metrics, which can certify the system behavior (amount of robustness and resiliency) against risks. In the process of understanding risk evaluation techniques, you will also get a thorough understanding of attacks and threats to ML models. In this context, you will discover different components of the system having security or privacy bottlenecks that pose external threats and make the model open to vulnerabilities. You will get to know the financial losses and business impacts when models deployed in production are not risk and threat resilient.

In this chapter, these topics will be covered in the following sections:

• Discovering risk elements
• Exploring risk mitigation strategies with vision, strategy, planning, and metrics
• Assessing potential impact and loss due to attacks
• Discovering different types of attacks

Further, with the use of Adversarial Robustness Toolbox (ART) and AIJack, we will see how to design attacks for ML models.