  The increased adoption of Artificial Intelligence (AI) presents an
opportunity to solve many socio-economic and environmental challenges; however,
this cannot happen without securing AI-enabled technologies. In recent years,
most AI models are vulnerable to advanced and sophisticated hacking techniques.
This challenge has motivated concerted research efforts into adversarial AI,
with the aim of developing robust machine and deep learning models that are
resilient to different types of adversarial scenarios. In this paper, we
present a holistic cyber security review that demonstrates adversarial attacks
against AI applications, including aspects such as adversarial knowledge and
capabilities, as well as existing methods for generating adversarial examples
and existing cyber defence models. We explain mathematical AI models,
especially new variants of reinforcement and federated learning, to demonstrate
how attack vectors would exploit vulnerabilities of AI models. We also propose
a systematic framework for demonstrating attack techniques against AI
applications and reviewed several cyber defences that would protect AI
applications against those attacks. We also highlight the importance of
understanding the adversarial goals and their capabilities, especially the
recent attacks against industry applications, to develop adaptive defences that
assess to secure AI applications. Finally, we describe the main challenges and
future research directions in the domain of security and privacy of AI
technologies.
