Evidence-based enterprise compliance systems and methods thereof

1 . A compliance system, comprising:

a processor; and

a memory operatively coupled with the processor, wherein the memory comprises processor-executable instructions which, when executed by the processor, cause the processor to:

receive, via a network, from a user device associated with a user, a selection of a project in an enterprise and a compliance type corresponding to the selected project;

cause a list of controls associated with the selected compliance type to be displayed on a user interface of the user device;

receive, via the user interface over the network, a set of configurations for each of the list of controls from the user, wherein the set of configurations comprises at least a selection of an orchestrator engine from a plurality of orchestrator engines to execute each control in the list of controls;

dynamically generate, via the selected orchestrator engine, a schema based on the selected compliance type and the list of controls at a server;

receive, via the user interface, user inputs to develop custom APIs;

clone the generated schema in a project repository, wherein the project repository is automatically created using the custom APIs;

automatically trigger, an execution of validation of each of the list of controls for the selected compliance type based on the generated schema,

generate results of the validation, in response to the execution of validation of each of the list of controls, the results comprising a list of non-compliant controls and a list of compliant controls;

wherein the processor is to generate the schema by dynamically connecting respective configuration files with each other in a sequence to trigger the execution of the validation of each of the list of controls;

dynamically generate, via a machine learning engine, a set of remedies for fixing each of the non-compliant controls by:

applying a cosine similarity function, via a deduplication Machine Learning (ML) engine based on a context of issues to perform deduplication to identify unique non-compliant controls from the list of non-compliant controls;

generating and transmitting, via the machine learning engine, real-time remediation recommendations using generative pre-trained transformer models to provide the set of remedies in the form of relevant code for each of the identified unique non-compliant controls to the user, wherein the set of remedies comprises a root cause analysis of each of the identified unique non-compliant controls; and

causing the machine learning engine to self-learn based on the set of remedies provided for each of the identified unique non-compliant controls, wherein the machine learning engine is the transformer model which uses deep learning and neural network trained on a plurality of parameters; and

cause the generated set of remedies to be displayed on the user interface of the user device, wherein the set of remedies is caused to be executed or modified by the user to fix each of the non-compliant controls, and wherein the execution of validation of the list of controls is retriggered until each of the list of controls corresponding to the selected compliance type is compliant.

2 . The system of claim 1 , wherein the results of the validation are version controlled and secured by a blockchain.

3 . The system of claim 1 , wherein the processor is to:

generate and display real-time recommendations for compliance types corresponding to the selected project and controls corresponding to the selected compliance type on the user interface of the user device.

4 . The system of claim 3 , wherein the processor is to generate the real-time recommendations for the compliance types by:

generating a similarity score for each of the compliance types with respect to the selected compliance type; and

generating, via the machine learning engine, the real-time recommendations for the compliance types based on the similarity score.

5 . The system of claim 1 , wherein to cause the list of controls to be displayed on the user interface, the processor is to:

extract the list of controls corresponding to the selected compliance type from a database, wherein the database comprises a mapping of the list of controls with the corresponding compliance type.

6 . The system of claim 1 , wherein the processor is to:

receive at least one of a custom compliance type and a custom control from the user; and

dynamically define a set of controls for the custom compliance type and a set of validations for the custom control.

7 . The system of claim 1 , wherein the set of configurations further comprises at least one of a schedule of a frequency of execution for each control, a selection of a branch or environment for execution of each control, and a method for execution of each control.

8 . The system of claim 1 , wherein the plurality of orchestrator engines comprises at least one of a continuous integration tool, a continuous deployment tool, a custom tool, or an enterprise tool.

9 . The system of claim 1 , wherein the processor is to identify the unique non-compliant controls by:

determining an angle of closeness of a first vector corresponding to a non-compliant control from the list of non-compliant controls with a second vector corresponding to an existing set of non-compliant controls;

determining whether the angle is greater than or equal to a pre-configured threshold;

in response to a positive determination, identifying the non-compliant control as a duplicate non-compliant control; and

in response to a negative determination, identifying the non-compliant control as a unique non-compliant control.

10 . A computer-implemented method, comprising:

receiving, by a processor associated with a compliance system, from a user device associated with a user, via a network, a selection of a project in an enterprise and a compliance type corresponding to the selected project;

causing, by the processor, a list of controls associated with the selected compliance type to be displayed on a user interface of the user device;

receiving, by the processor, via the user interface over the network, a set of configurations for each of the list of controls from the user, wherein the set of configurations comprises at least a selection of an orchestrator engine from a plurality of orchestrator engines to execute each control in the list of controls;

dynamically generating, by the processor, via the selected orchestrator engine, a schema based on the selected compliance type, and the list of controls at a server;

receiving, by the processor, via the user interface, user inputs to develop custom APLs;

cloning, by the processor, the generated schema in a project repository, wherein the project repository is automatically created using the custom APIs:

automatically triggering, by the processor, an execution of validation of each of the list of controls for the selected compliance type based on the generated schema;

generating, by the processor, results of the validation, in response to the execution of the validation of the list of controls, the results comprising a list of non-compliant controls and a list of compliant controls

wherein generating, by the processor, the schema comprises dynamically connecting respective configuration files with each other in a sequence to trigger the execution of the validation of each of the list of controls;

dynamically generating, by the processor via a machine learning engine, a set of remedies for fixing each of the non-compliant controls by:

applying, by the processor, a cosine similarity function, via a deduplication Machine Learning (ML) engine based on a context of issues to perform deduplication to identify unique non-compliant controls from the list of non-compliant controls;

generating and transmitting, by the processor via the machine learning engine, real-time remediation recommendations using generative pre-trained transformer models to provide the set of remedies in the form of relevant code for each of the identified unique non-compliant controls to the user, wherein the set of remedies comprises a root cause analysis of each of the identified unique non-compliant controls; and

causing, by the processor, the machine learning engine to self-learn based on the set of remedies provided for each of the identified unique non-compliant controls,

wherein the machine learning engine is the transformer model which uses deep learning and neural network trained on a plurality of parameters; and

causing, by the processor, the generated set of remedies to be displayed on the user interface of the user device, wherein the set of remedies is caused to be executed or modified by the user to fix each of the non-compliant controls, and wherein the execution of validation of each of the list of controls is retriggered until the list of controls corresponding to the selected compliance type are complaint.

11 . The computer-implemented method of claim 10 , further comprising:

receiving, by the processor, at least one of a custom compliance type and a custom control from the user; and

dynamically defining, by the processor, a set of controls for the custom compliance type and a set of validations for the custom control.

12 . The computer-implemented method of claim 10 , wherein the set of configurations further comprises at least one of a schedule of a frequency of execution for each control, a selection of a branch or environment for execution of each control, and a method for execution of each control.

13 . The computer-implemented method of claim 10 , wherein identifying, by the processor, the unique non-compliant controls comprises:

determining, by the processor, an angle of closeness of a first vector corresponding to a non-compliant control from the list of non-compliant controls with a second vector corresponding to an existing set of non-compliant controls;

determining, by the processor, whether the angle is greater than or equal to a pre-configured threshold;

in response to a positive determination, identifying, by the processor, the non-compliant control as a duplicate non-compliant control; and

in response to a negative determination, identifying, by the processor, the non-compliant control as a unique non-compliant control.

14 . The computer-implemented method of claim 10 , wherein the plurality of orchestrator engines comprises at least one of a continuous integration tool, a continuous deployment tool, a custom tool, or an enterprise tool.

15 . The computer-implemented method of claim 10 , wherein causing, by the processor, the list of controls to be displayed on the user interface comprises:

extracting, by the processor, the list of controls corresponding to the selected compliance type from a database, wherein the database comprises a mapping of the list of controls with the corresponding compliance type.

16 . A non-transitory computer-readable medium comprising processor-executable instructions that cause a processor to:

receive, via a network, from a user device associated with a user, a selection of a project in an enterprise and a compliance type corresponding to the selected project;

cause a list of controls associated with the selected compliance type to be displayed on a user interface of the user device;

receive, via the user interface over the network, a set of configurations for each of the list of controls from the user, wherein the set of configurations comprises at least a selection of an orchestrator engine from a plurality of orchestrator engines to execute each control in the list of controls;

dynamically generate, via the selected orchestrator engine, a schema based on the selected compliance type, and the list of controls at a server, and the selected orchestrator engine;

receive, via the user interface, user inputs to develop custom APIs:

clone the generated schema in a project repository, wherein the project repository is automatically created using the custom APIs;

automatically trigger an execution of validation of each of the list of controls for the selected compliance type based on the generated schema;

generate results of the validation, in response to the execution of validation of each of the list of controls, the results comprising a list of non-compliant controls land a list of compliant controls; wherein generating the schema comprises dynamically connecting respective configuration files with each other in a sequence to trigger the execution of the validation of each of the list of controls;

dynamically generate, via a machine learning engine, a set of remedies for fixing each of the non-compliant controls by:

applying a cosine similarity function, via a deduplication Machine Learning (ML) engine based on a context of issues to perform deduplication to identify unique non-compliant controls from the list of non-compliant controls;

generating and transmitting, via the machine learning engine, real-time remediation recommendations using generative pre-trained transformer models to provide the set of remedies in the form of relevant code for each of the identified unique non-compliant controls to the user, wherein the set of remedies comprises a root cause analysis of each of the identified unique non-compliant controls; and

causing the machine learning engine to self-learn based on the set of remedies provided for each of the identified unique non-compliant controls,

wherein the machine learning engine is the transformer model which uses deep learning and neural network trained on a plurality of parameters; and

cause the generated set of remedies to be displayed on the user interface of the user device, wherein the set of remedies is caused to be executed or modified by the user to fix each of the non-compliant controls, and wherein the execution of validation of the list of controls is retriggered until each of the list of controls corresponding to the selected compliance type is compliant.