Shift-left security risk analysis

1. A method for security risk analysis, the method comprising:

collecting operational data from different sources;

building pipelines based on the collected operational data;

localizing security issues by applying an unsupervised model to the operational data and extracting representative information from the operational data;

identifying shift-left data based on the localized security issues;

constructing a semantic graph using the shift-left data;

constructing a mapping between the operational data and the shift-left data;

clustering collected datasets; and

based on determined patterns of security issues, creating an active learning cycle using ground truth, wherein the active learning cycle automatically maps the operational data and the shift-left data to identify a source of a security issue.

2. The method of claim 1 , wherein the pipelines are built as an automated process that builds, tests and deploys computing data.

3. The method of claim 1 , wherein the unsupervised model identifies a top list of representative information for each software application.

4. The method of claim 1 , wherein the shift-left data includes source code, deployment configurations, deployment specifications and environmental variables.

5. The method of claim 1 , wherein the semantic graphs are constructed using the shift-left data.

6. The method of claim 1 , wherein the collected datasets are clustered, automatically, by the security issues.

7. The method of claim 1 , wherein the active learning cycle allows feedback from subject matter experts to improve precision of security risk identification by a model over time.

8. A computer system for security risk analysis, comprising:

one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage media, and program instructions stored on at least one of the one or more computer-readable tangible storage media for execution by at least one of the one or more processors via at least one of the one or more computer-readable memories, wherein the computer system is capable of performing a method comprising:

collecting operational data from different sources;

building pipelines based on the collected operational data;

localizing security issues by applying an unsupervised model to the operational data and extracting representative information from the operational data;

identifying shift-left data based on the localized security issues;

constructing a semantic graph using the shift-left data;

constructing a mapping between the operational data and the shift-left data;

clustering collected datasets; and

based on determined patterns of security issues, creating an active learning cycle using ground truth, wherein the active learning cycle automatically maps the operational data and the shift-left data to identify a source of a security issue.

9. The computer system of claim 8 , wherein the pipelines are built as an automated process that builds, tests and deploys computing data.

10. The computer system of claim 8 , wherein the unsupervised model identifies a top list of representative information for each software application.

11. The computer system of claim 8 , wherein the shift-left data includes source code, deployment configurations, deployment specifications and environmental variables.

12. The computer system of claim 8 , wherein the semantic graphs are constructed using the shift-left data.

13. The computer system of claim 8 , wherein the collected datasets are clustered, automatically, by the security issues.

14. The computer system of claim 8 , wherein the active learning cycle allows feedback from subject matter experts to improve precision of security risk identification by a model over time.

15. A computer program product for security risk analysis, comprising:

one or more computer-readable tangible storage media and program instructions stored on at least one of the one or more computer-readable tangible storage media, the program instructions executable by a processor to cause the processor to perform a method comprising:

collecting operational data from different sources;

building pipelines based on the collected operational data;

localizing security issues by applying an unsupervised model to the operational data and extracting representative information from the operational data;

identifying shift-left data based on the localized security issues;

constructing a semantic graph using the shift-left data;

constructing a mapping between the operational data and the shift-left data;

clustering collected datasets; and

based on determined patterns of security issues, creating an active learning cycle using ground truth, wherein the active learning cycle automatically maps the operational data and the shift-left data to identify a source of a security issue.

16. The computer program product of claim 15 , wherein the pipelines are built as an automated process that builds, tests and deploys computing data.

17. The computer program product of claim 15 , wherein the unsupervised model identifies a top list of representative information for each software application.

18. The computer program product of claim 15 , wherein the shift-left data includes source code, deployment configurations, deployment specifications and environmental variables.

19. The computer program product of claim 15 , wherein the semantic graphs are constructed using the shift-left data.

20. The computer program product of claim 15 , wherein the collected datasets are clustered, automatically, by the security issues.