Abstract: Scenario generation includes establishing a communicative coupling between a computer-generated business continuity management user interface and a selected large language model (LLM). Scenario generation includes specifying an adverse event in the user interface and querying a database of scenario description records for an organization with the specified adverse event in order to receive a vector of relevant data to the specified adverse event. Scenario generation further includes first tuning a pre-determined LLM prompt with the vector to produce a specific LLM prompt for the specified adverse event and then submitting the specific LLM prompt to the selected LLM from over the communicative coupling. The selected LLM returns to the user interface scenario text referring to a plausible instance of the specified adverse event. Finally, scenario generation includes persisting the scenario text into persistent storage in connection with a specific scenario for use in a scenario simulation engine.

Abstract: Simulation for business continuity includes querying a scenario repository for a specific adverse event and responding to the query by selecting and retrieving from the repository infrastructure element objects that are associated with the specific adverse event. The simulation executes a Monte Carlo scenario test of the infrastructure element objects across a range of the susceptibility values through the computation of an average of the susceptibility values for each infrastructure element object for randomly selected different degrees of severity of the event. The simulation additionally executes a recovery simulation of the infrastructure element objects across a range of recovery times by computing an average recovery time for each infrastructure element object amongst the range of the recovery times.

Abstract: Scenario generation includes establishing a communicative coupling between a computer-generated business continuity management user interface and a selected large language model (LLM). Scenario generation includes specifying an adverse event in the user interface and querying a database of scenario description records for an organization with the specified adverse event in order to receive a vector of relevant data to the specified adverse event. Scenario generation further includes first tuning a pre-determined LLM prompt with the vector to produce a specific LLM prompt for the specified adverse event and then submitting the specific LLM prompt to the selected LLM from over the communicative coupling. The selected LLM returns to the user interface scenario text referring to a plausible instance of the specified adverse event. Finally, scenario generation includes persisting the scenario text into persistent storage in connection with a specific scenario for use in a scenario simulation engine.