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1. A generative artificial intelligence (AI) system for cybersecurity applications, comprising: a plurality of data sources, wherein at least one of the data sources is sensor data derived from direct observation of activity within an environment; a context-aware AI database; a probationary database; an analytics engine communicatively coupled to the plurality of data sources, the context-aware AI database, and the probationary database; wherein the analytics engine is configured to: (a) generate a hypothesis object comprising independent variables, a dependent variable including a leading indicator of cybersecurity attack activity, a machine learning model trained from available data, and metadata associated therewith based on the data sources, wherein the leading indicator includes a recommendation or action related to at least one of: prevention of a cyberattack, detection of a cyberattack, mitigation of a cyberattack, and remediation of a cyberattack; (b) train the machine learning model associated with the hypothesis object to produce experimental results; (c) store the hypothesis object and the experimental results in the context-aware AI database in response to determining that the performance metric of the machine learning model meets a predetermined performance criterion; (d) store the hypothesis object and the experimental results in the probationary database in response to determining that the performance metric of the machine learning model does not meet a predetermined performance criterion; and a publishing module configured to provide, to one or more subscribers, the leading indicator associated with at least one of the corporate entity and a product associated with the corporate entity, as computed by the trained machine learning model stored within the context-aware AI database while processing contemporaneous information received from the data sources.
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