Which of the following is a best practice for data sharing across playbooks?

A customer wants to design a modular and reusable set of playbooks that all communicate with each other.

Which of the following is a best practice for data sharing across playbooks?
A . Use the py-postgresq1 module to directly save the data in the Postgres database.
B . Cal the child playbooks getter function.
C . Create artifacts using one playbook and collect those artifacts in another playbook.
D . Use the Handle method to pass data directly between playbooks.

View Answer

Answer: C

Explanation:

The correct answer is C because creating artifacts using one playbook and collecting those artifacts in another playbook is a best practice for data sharing across playbooks. Artifacts are data objects that are associated with a container and can be used to store information such as IP addresses, URLs, file hashes, etc. Artifacts can be created using the add artifact action in any playbook block and can be collected using the get artifacts action in the filter block. Artifacts can also be used to trigger active playbooks based on their label or type. See Splunk SOAR Documentation for more details.

In the context of Splunk SOAR, one of the best practices for data sharing across playbooks is to create artifacts in one playbook and use another playbook to collect and utilize those artifacts. Artifacts in Splunk SOAR are structured data related to security incidents (containers) that playbooks can act upon. By creating artifacts in one playbook, you can effectively pass data and context to subsequent playbooks, allowing for modular, reusable, and interconnected playbook designs. This approach promotes efficiency, reduces redundancy, and enhances the playbook’s ability to handle complex workflows.