1. A method for predicting the likelihood future security threats in distributed computing environment comprising the steps of: a main server entity communicating with a plurality of decision engines and a plurality of correlation engines; a communication engine communicating with the main server; the communication engine communicating with one or more third parties, wherein the one or more third parties comprises one or more data points, wherein the one or more data points comprise a plurality of user data and event data: a deep learning engine receiving outputs from multiple simulation runs using sequential game theory against the one or more data points, wherein the event data is selected from the group consisting of temporal, geographic, social, financial and linguistic data; the deep learning engine predicting a first occurrence of a security event, wherein predicting the first occurrence of a security event is based on the receiving the outputs from the multiple simulation runs; building a plurality of semantic graphs based on the communicating with correlation engines; a plurality of distributed networked agents collecting event and attribute data for the main server entity, the plurality of correlation server engines, and the plurality of decision engines, wherein the plurality of distributed networked agents are maintained on local servers; and a defined protocol initiating and maintaining secure communication between the main server, the plurality of distributed networked agents, the plurality of correlation engines, the plurality of decision engines and the communication engine; forecasting an arrival time of the impending security threat by incorporating temporal data in a prediction process, wherein the prediction process comprises: discovering said agent servers; determining an available processing bandwidth of the main server, agents, decision engines, and correlation engines; registering said main server and available agent server with registration entity; correlating event and attribute data from unstructured data sources; collecting log data; normalizing and tokenize the log data; generating semantic graphs of the log and the attribute data; and deciding on the likelihood of an event using the sequential game theory.