  Adversarial attacks for machine learning models have become a highly studied
topic both in academia and industry. These attacks, along with traditional
security threats, can compromise confidentiality, integrity, and availability
of organization's assets that are dependent on the usage of machine learning
models. While it is not easy to predict the types of new attacks that might be
developed over time, it is possible to evaluate the risks connected to using
machine learning models and design measures that help in minimizing these
risks.
  In this paper, we outline a novel framework to guide the risk management
process for organizations reliant on machine learning models. First, we define
sets of evaluation factors (EFs) in the data domain, model domain, and security
controls domain. We develop a method that takes the asset and task importance,
sets the weights of EFs' contribution to confidentiality, integrity, and
availability, and based on implementation scores of EFs, it determines the
overall security state in the organization. Based on this information, it is
possible to identify weak links in the implemented security measures and find
out which measures might be missing completely. We believe our framework can
help in addressing the security issues related to usage of machine learning
models in organizations and guide them in focusing on the adequate security
measures to protect their assets.
