Granted Patent B2

US 12,499,240 · App. 18/385,672 · Granted Dec 16, 2025

Inventor: Tom Lambotte (Chardon, OH)

Assignee: Tom Lambotte

G06F21/577G06F21/566G06F2221/034

Abstract

Claims (83)

1 . An apparatus for enhancing cybersecurity of an entity, wherein the apparatus comprises:

at least a processor; and

a memory communicatively connected to the at least a processor, wherein the memory containing instructions configuring the at least a processor to:

receive an entity prompt from an entity;

receive entity data comprising cybersecurity related data from the entity;

generate a cybersecurity enhancement program as a function of the entity prompt, wherein generating the cybersecurity enhancement program comprises:

comparing the entity data to a cybersecurity metric;

simulate a cyber-attack to the entity data as a function of the cybersecurity enhancement program using a cyber-attack simulation module;

monitor at least a change in the entity data as a function of the cyber-attack simulation;

generate a cybersecurity report using a simulation statistical model, wherein generating the cybersecurity report using the simulation statistical model comprises:

collecting a first set of responses to malicious data containing the at least a change in the entity data, wherein the at least a change in entity data comprises removing the malicious data;

collecting a second set of responses to the malicious data containing the at least a change to entity data, wherein the at least a change to the entity data comprises the entity processing the malicious data; and

comparing, in the cybersecurity report, a first count of the first set of responses to a second count of the second set of responses;

generate a prompt response as a function of the at least a change in the entity data, wherein the prompt response comprises the cybersecurity report; and

display the prompt response comprising the cybersecurity report through a visual interface of an entity device of the entity.

2 . The apparatus of claim 1 , wherein the memory contains the instructions further configuring the at least a processor to:

identify a plurality of search patterns from the entity data; and

filter entity identification data from the entity data as a function of the plurality of search patterns using a secure gateway.

3 . The apparatus of claim 1 , wherein the memory contains the instructions further configuring the at least a processor to:

generate a cybersecurity threat classifier using cybersecurity training data, wherein the cybersecurity training data comprises a plurality of entity data as input correlated to a plurality of cybersecurity threat classifications as output;

classify the entity data to at least one cybersecurity threat classification using the cybersecurity threat classifier; and

determine a cybersecurity risk threshold as a function of the at least one cybersecurity threat classification.

4 . The apparatus of claim 3 , wherein the memory contains the instructions further configuring the at least a processor to:

generate a cybersecurity risk score as a function of the entity data using a fuzzy inferencing system; and

compare the cybersecurity risk score to the cybersecurity risk threshold.

5 . The apparatus of claim 3 , wherein the memory contains the instructions further configuring the at least a processor to:

generate a cybersecurity enhancement program classifier using cyber security enhancement program training data, wherein the cyber security enhancement program training data comprises a plurality of entity data and cybersecurity threat classifications as input correlated to a plurality of cybersecurity enhancement programs as output; and

determine the cybersecurity enhancement programs as a function of the entity data and the output of the cybersecurity threat classifier using the trained cybersecurity enhancement program classifier.

6 . The apparatus of claim 1 , wherein the memory contains the instructions further configuring the at least a processor to:

insert a dumb agent in a data transfer between a first entity and a second entity, wherein the dumb agent is configured to analyze network traffic for insecure communication between the first entity and the second entity;

receive an entity data packet from the first entity using the dumb agent; and

insert one or more harmless tokens to the entity data packet using the dumb agent.

7 . The apparatus of claim 1 , wherein the memory contains the instructions further configuring the at least a processor to:

generate malicious data for the entity at a predetermined time period using a language processing module; and

distribute the malicious data to the entity.

8 . The apparatus of claim 6 , wherein the memory contains the instructions further configuring the at least a processor to:

receive a plurality of responses regarding the malicious data from the entity, wherein the at least a change of the entity data comprises the plurality of responses; and

generate the cybersecurity report as a function of the plurality of responses using a simulation statistical model.

9 . The apparatus of claim 1 , wherein the memory contains the instructions further configuring the at least a processor to update the cybersecurity enhancement program as a function of the cybersecurity report.

10 . The apparatus of claim 1 , wherein at least a portion of the prompt response is generated using a generative adversarial network (GAN), wherein the GAN comprises:

a generative network configured to generate candidate data; and

a discriminative network configured to evaluate the candidate data.

11 . A method for enhancing cybersecurity of an entity, wherein the method comprises:

receiving, using at least a processor, an entity prompt from an entity;

receiving, using the at least a processor, entity data comprising cybersecurity related data from the entity;

generating, using the at least a processor, a cybersecurity enhancement program as a function of the entity prompt, wherein generating the cybersecurity enhancement program comprises:

comparing the entity data to a cybersecurity metric;

simulating, using the at least a processor, a cyber-attack to the entity data as a function of the cybersecurity enhancement program using a cyber-attack simulation module;

monitoring, using the at least a processor, at least a change in the entity data as a function of the cyber-attack simulation;

generating, using the at least a processor, a cybersecurity report using a simulation statistical model, wherein generating the cybersecurity report using the simulation statistical model comprises:

collecting a first set of responses to malicious data containing the at least a change in the entity data, wherein the at least a change in entity data comprises removing the malicious data;

collecting a second set of responses to the malicious data containing the at least a change to entity data, wherein the at least a change to the entity data comprises the entity processing the malicious data; and

comparing, in the cybersecurity report, a first count of the first set of responses to a second count of the second set of responses;

generating, using the at least a processor, a prompt response as a function of the at least a change in the entity data, wherein the prompt response comprises the cybersecurity report; and

displaying, using the at least a processor, the prompt response comprising the cybersecurity report through a visual interface of an entity device of the entity.

12 . The method of claim 11 , further comprising:

identifying, using the at least a processor, a plurality of search patterns from the entity data; and

filtering, using the at least a processor, entity identification data from the entity data as a function of the plurality of search patterns using a secure gateway.

13 . The method of claim 11 , further comprising:

generating, using the at least a processor, a cybersecurity threat classifier using cybersecurity training data, wherein the cybersecurity training data comprises a plurality of entity data as input correlated to a plurality of cybersecurity threat classifications as output;

classifying, using the at least a processor, the entity data to at least one cybersecurity threat classification using the cybersecurity threat classifier; and

determining, using the at least a processor, a cybersecurity risk threshold as a function of the at least one cybersecurity threat classification.

14 . The method of claim 13 , further comprising:

generating, using the at least a processor, a cybersecurity risk score as a function of the entity data using a fuzzy inferencing system; and

comparing, using the at least a processor, the cybersecurity risk score to the cybersecurity risk threshold.

15 . The method of claim 13 , further comprising:

generating, using the at least a processor, a cybersecurity enhancement program classifier using cyber security enhancement program training data, wherein the cyber security enhancement program training data comprises a plurality of entity data and cybersecurity threat classifications as input correlated to a plurality of cybersecurity enhancement programs as output; and

determining, using the at least a processor, the cybersecurity enhancement programs as a function of the entity data and the output of the cybersecurity threat classifier using the trained cybersecurity enhancement program classifier.

16 . The method of claim 11 , further comprising:

inserting, using the at least a processor, a dumb agent in a data transfer between a first entity and a second entity, wherein the dumb agent is configured to analyze network traffic for insecure communication between the first entity and the second entity;

receiving, using the at least a processor, an entity data packet from the first entity using the dumb agent; and

inserting, using the at least a processor, one or more harmless tokens to the entity data packet using the dumb agent.

17 . The method of claim 11 , further comprising:

generating, using the at least a processor, malicious data for the entity at a predetermined time period using a language processing module; and

distributing, using the at least a processor, the malicious data to the entity.

18 . The method of claim 17 , further comprising:

receiving, using the at least a processor, a plurality of responses regarding the malicious data from the entity, wherein the at least a change of the entity data comprises the plurality of responses; and

generating, using the at least a processor, the cybersecurity report as a function of the plurality of responses using a simulation statistical model.

19 . The method of claim 11 , further comprising:

updating, using the at least a processor, the cybersecurity enhancement program as a function of the cybersecurity report.

20 . The method of claim 11 , wherein generating the prompt response comprises generating at least a portion of the prompt response using a generative adversarial network (GAN), wherein the GAN comprises:

a generative network configured to generate candidate data; and

a discriminative network configured to evaluate the candidate data.

Assignments (2)

Continuity (2)

References Cited (23)