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What metrics can be seen from the System Health Display? (select all that apply)
System Health Display is a dashboard that shows the status and performance of the SOAR processes and components, such as the automation service, the playbook daemon, the DECIDED process, and the REST API. Some of the metrics that can be seen from the System Health Display are:
* Memory Usage: The percentage of memory used by the system and the processes.
* Disk Usage: The percentage of disk space used by the system and the processes.
* Load Average: The average number of processes in the run queue or waiting for disk I/O over a period of time.
Therefore, options B, C, and D are the correct answers, as they are the metrics that can be seen from the System Health Display. Option A is incorrect, because Playbook Usage is not a metric that can be seen from the System Health Display, but rather a metric that can be seen from the Playbook Usage dashboard, which shows the number of playbooks and actions run over a period of time.
1: Web search results from search_web(query='Splunk SOAR Automation Developer System Health Display')
The System Health Display in Splunk SOAR provides several metrics to help monitor and manage the health of the system. These typically include:
* B: Memory Usage - This metric shows the amount of memory being used by the SOAR platform, which is important for ensuring that the system does not exceed available resources.
* C: Disk Usage - This metric indicates the amount of storage space being utilized, which is crucial for maintaining adequate storage resources and for planning capacity.
* D: Load Average - This metric provides an indication of the overall load on the system over a period of time, which helps in understanding the system's performance and in identifying potential bottlenecks or issues.
Playbook Usage is generally not a metric displayed on the System Health page; instead, it's more related to the usage analytics of playbooks rather than system health metrics.
What is enabled if the Logging option for a playbook's settings is enabled?
In Splunk SOAR (formerly known as Phantom), enabling the Logging option for a playbook's settings primarily affects how logging information is displayed on the Investigation page. When this option is enabled, more detailed logging information is made available on the Investigation page, which can be crucial for troubleshooting and understanding the execution flow of the playbook. This detailed information can include execution steps, actions taken, and conditional logic paths followed during the playbook run.
It's important to note that enabling logging does not affect the audit logs or the debug window directly, nor does it write execution details to the spawn.log. Instead, it enhances the visibility and granularity of logs displayed on the specific Investigation page related to the playbook's execution.
Splunk Documentation and SOAR User Guides typically outline the impacts of enabling various settings within the playbook configurations, explaining how these settings affect the operation and logging within the system. For specific references, consulting the latest Splunk SOAR documentation would provide the most accurate and detailed guidance.
Enabling the Logging option for a playbook's settings in Splunk SOAR indeed affects the level of detail provided on the Investigation page. Here's a comprehensive explanation of its impact:
Investigation Page Logging:
The Investigation page serves as a centralized location for reviewing all activities related to an incident or event within Splunk SOAR.
When the Logging option is enabled, it enhances the level of detail available on this page, providing a granular view of the playbook's execution.
This includes detailed information about each action's execution, such as parameters used, results obtained, and any conditional logic that was evaluated.
Benefits of Detailed Logging:
Troubleshooting: It becomes easier to diagnose issues within a playbook when you can see a detailed log of its execution.
Incident Analysis: Analysts can better understand the sequence of events and the decisions made by the playbook during an incident.
Playbook Optimization: Developers can use the detailed logs to refine and improve the playbook's logic and performance.
Non-Impacted Areas:
The audit log, which tracks changes to the playbook itself, is not affected by the Logging option.
The debug window, used for real-time debugging during playbook development, also remains unaffected.
The spawn.log file, which contains internal operational logs for the Splunk SOAR platform, does not receive detailed execution information from playbooks.
Best Practices:
Enable detailed logging during the development and testing phases of a playbook to ensure thorough analysis and debugging.
Consider the potential impact on storage and performance when enabling detailed logging in a production environment.
For the most accurate and up-to-date guidance on playbook settings and their effects, I recommend consulting the latest Splunk SOAR documentation and user guides. These resources provide in-depth information on configuring playbooks and understanding the implications of various settings within the Splunk SOAR platform.
In summary, the Logging option is a powerful feature that enhances the visibility of playbook operations on the Investigation page, aiding in incident analysis and ensuring that playbooks are functioning correctly. It is an essential tool for security teams to effectively manage and respond to incidents within their environment.
Which app allows a user to send Splunk Enterprise Security notable events to Phantom?
The Splunk App for Phantom is designed to facilitate the integration between Splunk Enterprise Security and Splunk SOAR (Phantom), enabling the seamless forwarding of notable events from Splunk to Phantom. This app allows users to leverage the analytical and data processing capabilities of Splunk ES and utilize Phantom for automated orchestration and response. The app typically includes mechanisms for specifying which notable events to send to Phantom, formatting the data appropriately, and ensuring secure communication between the two platforms. This integration is crucial for organizations looking to combine the strengths of Splunk's SIEM capabilities with Phantom's automation and orchestration features to enhance their security operations.
When configuring a Splunk asset for Phantom to connect to a SplunkC loud instance, the user discovers that they need to be able to run two different on_poll searches. How is this possible
In scenarios where there's a need to run different on_poll searches for a Splunk Cloud instance from Splunk SOAR, configuring a second Splunk asset for the additional query is a practical solution. Splunk SOAR's architecture allows for multiple assets of the same type to be configured with distinct settings. By setting up a second Splunk asset specifically for the second on_poll search query, users can maintain separate configurations and ensure that each query is executed in its intended context without interference. This approach provides flexibility in managing different data collection or monitoring needs within the same SOAR environment.
How is a Django filter query performed?
Django filter queries in Splunk SOAR are performed by appending filter parameters directly to the REST API URL. This allows users to refine their search and retrieve specific data. For example, to filter containers by tags containing the word 'sumo', the following URL structure would be used: https://<PHANTOM_URL>/rest/container?_filter_tags_contains='sumo'. This format enables users to construct dynamic queries that can filter results based on specified criteria within the Django framework used by Splunk SOAR.
The correct way to perform a Django filter query in Splunk SOAR is to add parameters to the URL similar to the following: phantom/rest/container?_filter_tags_contains=''sumo''. This will return a list of containers that have the tag ''sumo'' in them. You can use various operators and fields to filter the results according to your needs. For more details, see Query for Data and Use filters in your Splunk SOAR (Cloud) playbook to specify a subset of artifacts before further processing. The other options are either incorrect or irrelevant for this question. For example:
* phantom/rest/search/app/contains/''sumo'' is not a valid URL for a Django filter query. It will return an error message saying ''Invalid endpoint''.
* There is no Django Filter Query Editor in the Administration panel of Splunk SOAR. You can use the REST API Tester to test your queries, but not to edit them.
* There is no SOAR Django App that needs to be installed or configured for performing Django filter queries. Splunk SOAR uses the Django framework internally, but you do not need to install or use any additional apps for this purpose.
