  Air Traffic Management data systems today are inefficient and not scalable to
enable future unmanned systems. Current data is fragmented, siloed, and not
easily accessible. There is data conflict, misuse, and eroding levels of trust
in provenance and accuracy. With increased autonomy in aviation, Artificially
Intelligent (AI) enabled unmanned traffic management (UTM) will be more reliant
on secure data from diverse stakeholders. There is an urgent need to develop a
secure network that has trustworthy data chains and works with the requirements
generated by UTM. Here, we review existing research in 3 key interconnected
areas: (1) blockchain development for secure data transfer between competing
aviation stakeholders, (2) self-learning networking architectures that
distribute consensus to achieve secure air traffic control, (3) explainable AI
to build trust with human stakeholders and backpropagate requirements for
blockchain and network optimisation. When connected together, this new digital
ecosystem blueprint is tailored for safety critical UTM sectors. We motivate
the readers with a case study, where a federated learning UTM uses real air
traffic and weather data is secured and explained to human operators. This
emerging area still requires significant research and development by the
community to ensure it can enable future autonomous air mobility.
