In this talk, a general overview of AI methods and computational models to solve design problems in cryptography is given. These include the use of bio-inspired optimization techniques (particularly evolutionary algorithms) to design symmetric primitives with good cryptographic properties, like Boolean functions and S-boxes. The approach levereages also on the use of AI computational models like Cellular Automata (CA) as an efficient representation technique for such primitives. In the second part of the talk, new directions of research are illustrated based on the experience gained with regard to the above AI methods and models. In particular, we focus on the use of evolutionary algorithms to design algebraic constructions of symmetric primitives, to evolve differential distinguishers for small symmetric ciphers, and to explore the space of adversarial examples in machine learning models. Particular emphasis is given to the inherent interpretability and explainability of the solutions provided by evolutionary algorithms, specifically in the case of Genetic Programming (GP).