Abstract: Embodiments of the present disclosure relate to deep content inspection of API traffic. Initially, messages are received from users of an API at an API gateway. The messages comprise a structure and metadata and are intended for an API server. The API gateway selectively communicates copies of the messages to a traffic sampler. The traffic sampler comprises a database of traffic samples, a machine learning system, and a database comprising one or more models. The traffic sample communicates the models corresponding to usage of the API servers to the API gateway. The models are built by the machine learning system based on the structure and metadata of the traffic samples and may be utilized to perform tests on the API servers.

Abstract: For a comprehensive view of the parties involved in a transaction, a transaction risk assessment system can collect and persist digital credentials of entities' involved in a requested electronic transaction. With the entity credentials, the transaction risk assessment system performs an on-demand risk analysis of the requested electronic transaction based, at least in part, on previously collected historical transaction data of the entities involved in the electronic transaction. The risk assessment system searches the historical transaction data for information about the entities involved in the requested transaction and evaluates discovered information. The transaction risk assessment system can quantify trustworthiness of each entity involved in the requested transaction based on the evaluation of the discovered information. The transaction risk assessment system can then quantify risk of executing the transaction at least using the quantified trustworthiness of the involved entities.