  Security challenges for Cloud or Fog-based machine learning services pose
several concerns. Securing the underlying Cloud or Fog services is essential,
as successful attacks against these services, on which machine learning
applications rely, can lead to significant impairments of these applications.
Because the requirements for AI applications can also be different, we
differentiate according to whether they are used in the Cloud or in a Fog
Computing network. This then also results in different threats or attack
possibilities. For Cloud platforms, the responsibility for security can be
divided between different parties. Security deficiencies at a lower level can
have a direct impact on the higher level where user data is stored. While
responsibilities are simpler for Fog Computing networks, by moving services to
the edge of the network, we have to secure them against physical access to the
devices. We conclude by outlining specific information security requirements
for AI applications.
