  Technology is shaping our lives in a multitude of ways. This is fuelled by a
technology infrastructure, both legacy and state of the art, composed of a
heterogeneous group of hardware, software, services and organisations. Such
infrastructure faces a diverse range of challenges to its operations that
include security, privacy, resilience, and quality of services. Among these,
cybersecurity and privacy are taking the centre-stage, especially since the
General Data Protection Regulation (GDPR) came into effect. Traditional
security and privacy techniques are overstretched and adversarial actors have
evolved to design exploitation techniques that circumvent protection. With the
ever-increasing complexity of technology infrastructure, security and
privacy-preservation specialists have started to look for adaptable and
flexible protection methods that can evolve (potentially autonomously) as the
adversarial actor changes its techniques. For this, Artificial Intelligence
(AI), Machine Learning (ML) and Deep Learning (DL) were put forward as
saviours. In this paper, we look at the promises of AI, ML, and DL stated in
academic and industrial literature and evaluate how realistic they are. We also
put forward potential challenges a DL based security and privacy protection
technique has to overcome. Finally, we conclude the paper with a discussion on
what steps the DL and the security and privacy-preservation community have to
take to ensure that DL is not just going to be hype, but an opportunity to
build a secure, reliable, and trusted technology infrastructure on which we can
rely on for so much in our lives.
