Middle-ware artificial intelligence (AI) engine for response generation

1 . A system to generate a response (Reverse Antecedent/Fixed) using an artificial intelligence (AI) engine for a plurality of end-user devices using a plurality of services and to provide threat protection in a cloud-based network comprises:

a tenant of a plurality of tenants in the cloud-based network, the tenant includes the plurality of end-user devices;

a plurality of tunnels between the plurality of end-user devices and the plurality of services, wherein the plurality of tunnels is operable to transmit and segregate traffic;

the AI engine is operable to:

intercept traffic within the plurality of tunnels at an application layer of the cloud-based network;

receive a request from a user of a plurality of users of an end-user device of the plurality of end-user devices for the plurality of services;

apply a plurality of functions to manage the request;

monitor the request in real-time based on a plurality of policies;

generate the response to the request based on the real-time monitoring of the request;

determine a plurality of patterns based on interactions of the user with the plurality of services using user entity behavior analyzer (UEBA), wherein the AI engine is operable to use the UEBA to:

collect behavioral information of the user using a set of sensors,

process the plurality of patterns according to the plurality of policies, and

generate a baseline of user activity based on the processed plurality of patterns,

change settings for the user based on the baseline of user activity;

generate the response to the request based on the settings;

send the response to the user of the plurality of users of the end-user device of the plurality of the end-user devices to fulfill the request; and

an AI reporter to transmit information corresponding to the request and the response across the plurality of tenants of the cloud-based network.

2 . The system of claim 1 , wherein the request is first authenticated at a cloud proxy according to the plurality of policies and then forwarded to the AI engine.

3 . The system of claim 1 , wherein the AI engine protects the plurality of tenants within the cloud-based network against a malicious entity.

4 . The system of claim 1 , wherein the AI engine moderates interactions by scaling up/down number of requests across the plurality of users and the plurality of services within the cloud-based network.

5 . The system of claim 1 , wherein the plurality of functions manages the request by:

enforcing rate limits and throttling to prevent abuse, overloading, and excessive costs;

providing access control and authentication to allow access to the plurality of services to authorized users only;

complying with data privacy regulations;

supporting multiple languages to translate the request; and/or

integrating with external services outside of the cloud-based network.

6 . The system of claim 5 , wherein the plurality of functions filters sensitive information from input and output data of the request to comply with the data privacy regulations.

7 . The system of claim 1 , wherein the UEBA uses machine learning models to determine the plurality of patterns based on the interactions of the user with the plurality of services.

8 . A method for generating a response using an artificial intelligence (AI) engine for a plurality of end-user devices using a plurality of services to provide threat protection in a cloud-based network, the method comprises:

transmitting and segregating traffic within a plurality of tunnels between the plurality of end-user devices and the plurality of services;

intercepting traffic within the plurality of tunnels at an application layer of the cloud-based network;

receiving a request from a user of a plurality of users of an end-user device of the plurality of end-user devices for the plurality of services;

applying a plurality of functions to manage the request from the user;

monitoring the request in real-time based on a plurality of policies;

generating the response to the request based on the real-time monitoring of the request;

determining a plurality of patterns based on interactions of the user with the plurality of services using user entity behavior analyzer (UEBA), wherein the AI engine is operable to use the UEBA to:

collecting behavioral information of the user using a set of sensors,

processing the plurality of patterns according to the plurality of policies, and

generating a baseline of user activity based on the processed plurality of patterns,

changing settings for the user based on the baseline of user activity;

generating the response to the request based on the settings;

sending the response to the user of the plurality of users of the end-user device to fulfill the request; and

transmitting information corresponding to the request and the response to a plurality of tenants of the cloud-based network.

9 . The method of claim 8 , wherein the request is first authenticated at a cloud proxy according to the plurality of policies and then forwarded to the AI engine.

10 . The method of claim 8 , wherein the AI engine protects the plurality of tenants within the cloud-based network against a malicious entity.

11 . The method of claim 8 , wherein the AI engine moderates interactions by scaling up/down number of requests across the plurality of users and the plurality of services within the cloud-based network.

12 . The method of claim 8 , wherein the plurality of functions manages the request by:

enforcing rate limits and throttling to prevent abuse, overloading, and excessive costs;

providing access control and authentication to allow access to the plurality of services to authorized users only;

complying with data privacy regulations;

supporting multiple languages to translate the request; and/or

integrating with external services outside of the cloud-based network.

13 . The method of claim 12 , wherein the plurality of functions filters sensitive information from input and output data of the request to comply with the data privacy regulations.

14 . The method of claim 8 , wherein the UEBA uses machine learning models to determine the plurality of patterns based on the interactions of the user with the plurality of services.

15 . A non-transitory machine-readable media having machine-executable instructions embodied thereon that when executed by one or more processors, cause the one or more processors to perform a method for generating a response using an artificial intelligence (AI) engine for a plurality of end-user devices using a plurality of services for providing threat protection in a cloud-based network, the method comprising:

transmitting and segregating traffic within a plurality of tunnels between the plurality of end-user devices and the plurality of services;

intercepting traffic within the plurality of tunnels at an application layer of the cloud-based network;

receiving a request from a user of a plurality of users of an end-user device of the plurality of end-user devices for the plurality of services;

applying a plurality of functions to manage the request from the user;

monitoring the request in real-time based on a plurality of policies;

generating the response to the request based on the real-time monitoring of the request;

determining a plurality of patterns based on interactions of the user with the plurality of services using user entity behavior analyzer (UEBA), wherein the AI engine is operable to use the UEBA to:

collecting behavioral information of the user using a set of sensors,

processing the plurality of patterns according to the plurality of policies, and

generating a baseline of user activity based on the processed plurality of patterns,

changing settings for the user based on the baseline of user activity;

generating the response to the request based on the settings;

sending the response to the user of the plurality of users of the end-user device to fulfill the request; and

transmitting information corresponding to the request and the response to a plurality of tenants of the cloud-based network.

16 . The non-transitory machine-readable media of claim 15 , wherein the AI engine protects the plurality of tenants within the cloud-based network against a malicious entity.

17 . The non-transitory machine-readable media of claim 15 , wherein the AI engine moderates interactions by scaling up/down number of requests across the plurality of users and the plurality of services within the cloud-based network.

18 . The non-transitory machine-readable media of claim 15 , wherein the plurality of functions manages the request by:

enforcing rate limits and throttling to prevent abuse, overloading, and excessive costs;

providing access control and authentication to allow access to the plurality of services to authorized users only;

complying with data privacy regulations;

supporting multiple languages to translate the request; and/or

integrating with external services outside of the cloud-based network.

19 . The non-transitory machine-readable media of claim 18 , the plurality of functions filters sensitive information from input and output data of the request to comply with the data privacy regulations.

20 . The non-transitory machine-readable media of claim 15 , wherein the UEBA uses machine learning models to determine the plurality of patterns based on the interactions of the user with the plurality of services.