Both fields benefit from rigorous quantitative modeling for tasks such as detecting patterns and anomalies.
Both financial trading and security operate in an adversarial environment. In finance, the adversary is the ‘market’, i.e. the rest of the participants. In security, the adversary is the set of attackers.
Because of the adversarial environments, simple models do not work for very long, because the adversary evolves in response to actions taken by the other party, and it is hard for a model to capture the direction in which the adversary will evolve. Therefore both fields require that models be updated. Models are built using training data . The challenge therefore is to use the right mix of recent data and longer term historical data. How to do this is not obvious in both fields..
Game theory is a useful tool for modeling problems in both fields because of the adversarial nature of the field.
Modern financial instruments, apart from serving as investment vehicles, are designed for mitigating risk. The business of selling information security products or services is also one of mitigating risk — risk of information loss. There is no such thing as perfect security. Instead, organizations allocate an amount of money they consider appropriate to meet their risk appetite.
One difference is that trading in financial markets often has the ‘self fulfilling prophesy’ property, i.e. it can act as a positive feedback system (if a certain fraction of people with opinion that the price of a security is likely to fall, sell their holdings, the rest start thinking its probably a good idea to sell, and the end result is that the price does fall), whereas there doesn’t seem to be an equivalent in cybersecurity.