  Neuro-Symbolic Artificial Intelligence (AI) is an emerging and quickly
advancing field that combines the subsymbolic strengths of (deep) neural
networks and explicit, symbolic knowledge contained in knowledge graphs to
enhance explainability and safety in AI systems. This approach addresses a key
criticism of current generation systems, namely their inability to generate
human-understandable explanations for their outcomes and ensure safe behaviors,
especially in scenarios with \textit{unknown unknowns} (e.g. cybersecurity,
privacy). The integration of neural networks, which excel at exploring complex
data spaces, and symbolic knowledge graphs, which represent domain knowledge,
allows AI systems to reason, learn, and generalize in a manner understandable
to experts. This article describes how applications in cybersecurity and
privacy, two most demanding domains in terms of the need for AI to be
explainable while being highly accurate in complex environments, can benefit
from Neuro-Symbolic AI.
