System and method for enriching a generative model for cybersecurity incident management
AssigneeRadware, Ltd.
InventorsAvi CHESLA, Sergei EDELSTEIN
Life of this patent
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Abstract
A system and method for enriching a generative model for managing a cybersecurity is presented. The method includes generating textual data from reasoning data of a reasoning model, wherein the reasoning model represents a probabilistic causal relationship amongst a plurality of nodes, and wherein the textual data describes an incident case of the reasoning model in relation to at least one cause; embedding the generated textual data at the generative model in order to create a semantic embedding space; and training the generative model with the embeddings of the textual data.
Claims19
A method for enriching a generative model for managing a cybersecurity incident, comprising:
- generating textual data from reasoning data of a reasoning model, wherein the reasoning model represents a probabilistic causal relationship amongst a plurality of nodes, and wherein the textual data describes an incident case of the reasoning model in relation to at least one cause;
- embedding the generated textual data at the generative model in order to create a semantic embedding space;
- and
- training the generative model with the embeddings of the textual data.
A non-transitory computer readable medium having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:
- generating textual data from reasoning data of a reasoning model, wherein the reasoning model represents a probabilistic causal relationship amongst a plurality of nodes, and wherein the textual data describes an incident case of the reasoning model in relation to at least one cause;
- embedding the generated textual data at a generative model in order to create a semantic embedding space;
- and
- training the generative model with the embeddings of the textual data.
A system for enriching a generative model for managing a cybersecurity incident, comprising:
- a processing circuitry;
- and
- a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
- generate textual data from reasoning data of a reasoning model, wherein the reasoning model represents a probabilistic causal relationship amongst a plurality of nodes, and wherein the textual data describes an incident case of the reasoning model in relation to at least one cause;
- embed the generated textual data at the generative model in order to create a semantic embedding space;
- and
- train the generative model with the embeddings of the textual data.
Dependencies are inferred from the claim text.