Autonomous generation of GRC programs

1. A computer-implemented method of generating a computer program for a governance, risk, compliance (GRC) system, the method comprising:

detecting, by a processor, at least one change made to a plurality of objects of a database in a GRC system;

generating, by the processor, a plurality of snapshots of the database, wherein a snapshot of the database is generated in response to every change among the at least one change detected by the processor and each snapshot of the database includes a corresponding detected change made to the plurality of objects;

training, by the processor, a machine learning model using the plurality of snapshots of the database, the trained machine model indicating relationships among a set of related objects, wherein the related objects are among the plurality of objects;

applying, by the processor, a set of grammar rules on the relationships among the set of related objects, and fields of the set of related objects, indicated by the machine learning model to generate a computer program, wherein the computer program comprises a set of instructions for performing a set field operation on at least one object among the set of related objects; and

identifying, by the processor, an input field and an operation, based on the applied grammar rules, wherein an application of the identified operation on the input field outputs a result for performing the set field operation on the at least one object among the set of related objects.

2. The computer-implemented method of claim 1 , further comprising enabling, by the processor, the computer program on an application.

3. The computer-implemented method of claim 2 , further comprising: outputting, by the processor, the computer program on a user interface; and

receiving, by the processor, a user input indicating an approval of the computer program, wherein enabling the computer program is performed in response to receiving the user input indicating the approval of the computer program.

4. The computer-implemented method of claim 2 , further comprising:

outputting, by the processor, the computer program on a user interface;

receiving, by the processor, a user input indicating a need to revise the computer program; and

revising, by the processor, the computer program based on the user input indicating the need to revise the computer program.

5. The computer-implemented method of claim 1 , further comprising:

determining, by the processor, a new relationship among the plurality of objects based on the machine learning model; and

updating, by the processor, a memory storing a plurality of relationships among the plurality of objects, and among fields of the plurality of objects, in the GRC system with the new relationship.

6. The computer-implemented method of claim 1 , further comprising:

running, by the processor, the computer program using a default value of an input field of the computer program to perform the set field operation on the at least one object among the set of related objects to a result value;

comparing, by the processor, the result value with a threshold;

adjusting, by the processor, the default value of the input field based on the comparison of the result value with the threshold, wherein the running, comparing, and adjusting are performed until the result value reaches the threshold; and

setting, by the processor, a constraint on the input field of the computer program in response to the result value reaching the threshold.

7. The computer-implemented method of claim 1 , further comprising:

determining, by the processor, an of snapshots stored in a memory; and

training, by the processor, the machine learning model in response to the amount of snapshots stored in the memory reaching a threshold.

8. A system comprising:

a memory;

a processor configured to be in communication with the memory, the processor being configured to:

detect at least one change made to a plurality of objects of a database in a governance, risk, compliance (GRC) system;

generate a plurality of snapshots of the database, wherein a snapshot of the database is generated in response to every change among the at least one change detected by the processor and each snapshot of the database includes a corresponding detected change made to the plurality of objects;

train a machine learning model using the plurality of snapshots of the database, the trained machine model indicating relationships among a set of related objects, wherein the related objects are among the plurality of objects;

apply a set of grammar rules on the relationships among the set of related objects, and fields of the set of related objects, indicated by the machine learning model to generate a computer program, wherein the computer program comprises a set of instructions for performing a set field operation on at least one object among the set of related objects; and

identify an input field and an operation, based on the applied grammar rules, wherein an application of the identified operation on the input field outputs a result for performing the set field operation on the at least one object among the set of related objects.

9. The system of claim 8 , wherein the processor is further configured to enable the computer program on an application.

10. The system of claim 9 wherein the processor is further configured to:

output the computer program on a user interface; and

receive a user input indicating an approval of the computer program, wherein enabling the computer program is performed in response to receiving the user input indicating the approval of the computer program.

11. The system of claim 9 , wherein the processor is further configured to:

output the computer program on a user interface;

receive a user input indicating a need to revise the computer program; and

revise the computer program based on the user input indicating the need to revise the computer program.

12. The system of claim 8 , wherein the memory is configured to store a plurality of relationships among the plurality of objects in the GRC system, and among fields of the plurality of objects, and the processor is further configured to:

determine a new relationship among the plurality of objects based on the machine learning model; and

update the memory with the new relationship.

13. The system of claim 8 , wherein the processor is further configured to:

run the computer program using a default value of an input field of the computer program to perform the set field operation on the at least one object among the set of related objects to a result value;

compare the result value with a threshold;

adjust the default value of the input field based on the comparison of the result value with the threshold, wherein the running, comparing, and adjusting are performed until the result value reaches the threshold; and

set a constraint on the input field of the computer program in response to the result value reaches the threshold.

14. The system of claim 8 , wherein the processor is further configured to:

determine an amount of snapshots stored in the memory; and

train the machine learning model in response to the amount of snapshots stored in the memory reaching a threshold.

15. A computer program product for generating a computer program in a GRC system, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processing element of a device to cause the device to:

detect at least one change made to a plurality of objects of a database in a governance, risk, compliance (GRC) system;

generate plurality of snapshots of the database, wherein a snapshot of the database is generated in response to every change among the at least one change detected by the processor and each snapshot of the database includes a corresponding detected change made to the plurality of objects;

train a machine learning model using the plurality of snapshots of the database, the trained machine model indicating relationships among a set of related objects, wherein the related objects are among the plurality of objects;

apply a set of grammar rules on the relationships among the set of related objects, and fields of the set of related objects, indicated by the machine learning model to generate a computer program, wherein the computer program comprises a set of instructions for performing a set field operation on at least one object among the set of related objects; and

identify an input field and an operation, based on the applied grammar rules, wherein an application of the identified operation on the input field outputs a result for performing the set field operation on the at least one object among the set of related objects.

16. The computer program product of claim 15 , wherein the program instructions are further executable by the processing element of the device to cause the device to enable the computer program on an application.

17. The computer program product of claim 15 , wherein the program instructions are further executable by the processing element of the device to cause the device to:

determine a new relationship among the plurality of objects based on the machine learning model; and

update a memory storing a plurality of relationships among the plurality of objects, and among fields of the plurality of objects, in the GRC system with the new relationship.

18. The computer program product of claim 15 , wherein the program instructions are further executable by the processing element of the device to cause the device to:

run the computer program using a default value of an input field of the computer program to perform the set field operation on the at least one object among the set of related objects to a result value;

compare the result value with a threshold;

adjust the default value of the input field based on the comparison of the result value with the threshold, wherein the running, comparing, and adjusting are performed until the result value reaches the threshold; and

set a constraint on the input field of the computer program in response to the result value reaches the threshold.