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Merge branch 'develop' into default_meta_updates
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.github/CODEOWNERS

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* @patel-bhavin @ljstella @nasbench
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/response_templates/ @kbouchardherjavecgroup @ccl0utier
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/response_templates/ @kbouchardherjavecgroup @ccl0utier @henryy-splunk
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/.github/workflows/response_templates/mcopenapi_public.yml @kbouchardherjavecgroup @ccl0utier @henryy-splunk

.github/workflows/build-response-templates.yml

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runs-on: ubuntu-latest
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steps:
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- name: Check out the repository code
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uses: actions/checkout@v5
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uses: actions/checkout@v6
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- uses: actions/setup-python@v6
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with:
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cp response_templates/merged_response_templates/* dist/api/response_templates/
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- name: store_artifacts
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uses: actions/upload-artifact@v5
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uses: actions/upload-artifact@v6
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with:
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name: response-templates
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path: |

.github/workflows/response_templates/mcopenapi_public.yml

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type: number
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format: double
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example: 1690743671.0881049633
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example: { "version": 2, "update_time": 1690743671.0881049633 }
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description:
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type: string
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description: Description of the response template version.
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example: "This is version 2 of the response template."
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example: { "version": 2, "update_time": 1690743671.0881049633, "description": "This is version 2 of the response template." }
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ResponseTemplateManifest:
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type: object
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description: Response template manifest.

.github/workflows/response_templates/template_script.py

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@@ -32,21 +32,25 @@ def generate_manifest(directory, prefix, output_dir):
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template_mapping = _get_template_mapping(directory)
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for template_name, template_list in sorted(template_mapping.items(), key=lambda x: x[0]):
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out_template_name = f"{template_name}.json"
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curr_template_name = template_name
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templates_version= []
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for _, file in template_list:
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with open(file, 'r') as in_file:
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content = in_file.read()
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curr_template = json.loads(content)
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version = curr_template.get("version", "1.0")
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version = curr_template.get("version")
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update_time = curr_template.get("update_time")
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description = curr_template.get("description")
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curr_template_name = curr_template.get("name", template_name)
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curr_metadata = {
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"version": version,
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"update_time": update_time,
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"description": description,
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}
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templates_version.append(curr_metadata)
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response_templates.append({
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"name": template_name,
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"name": curr_template_name,
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"versions": templates_version,
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"link": f"{prefix}{out_template_name}"
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})

baselines/baseline_of_blocked_outbound_traffic_from_aws.yml

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name: Baseline of blocked outbound traffic from AWS
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id: fc0edd96-ff2b-48b0-9f1f-63da3782fd63
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version: 1
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date: '2018-05-07'
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version: 2
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date: '2026-01-14'
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author: Bhavin Patel, Splunk
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type: Baseline
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status: production
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how_to_implement: You must install the AWS App for Splunk (version 5.1.0 or later)
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and Splunk Add-on for AWS version (4.4.0 or later), then configure your `VPC flow
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logs.`.
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known_false_positives: none
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known_false_positives: No false positives have been identified at this time.
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references: []
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tags:
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analytic_story:

baselines/baseline_of_cloud_infrastructure_api_calls_per_user.yml

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name: Baseline Of Cloud Infrastructure API Calls Per User
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id: 1da5d5ea-4382-447d-98a9-87c358c95fcb
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version: 1
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date: '2020-09-07'
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version: 2
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date: '2026-01-14'
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author: David Dorsey, Splunk
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type: Baseline
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status: production
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90 days of data. You can modify the search window to build the model over a longer
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period of time, which may give you better results. You may also want to periodically
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re-run this search to rebuild the model with the latest data.
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known_false_positives: none
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known_false_positives: No false positives have been identified at this time.
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references: []
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tags:
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analytic_story:

baselines/baseline_of_cloud_instances_destroyed.yml

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name: Baseline Of Cloud Instances Destroyed
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id: a2f701f8-5296-4d74-829c-0b7eb346d549
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version: 1
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date: '2020-08-25'
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version: 2
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date: '2026-01-14'
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author: David Dorsey, Splunk
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type: Baseline
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status: production
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period of time, which may give you better results. You may also want to periodically
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re-run this search to rebuild the model with the latest data.\nMore information
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on the algorithm used in the search can be found at `https://help.splunk.com/en/splunk-enterprise/apply-machine-learning/use-splunk-machine-learning-toolkit/5.5.0/algorithms-and-scoring-metrics-in-mltk/algorithms-in-the-splunk-machine-learning-toolkit#densityfunction-0`."
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known_false_positives: none
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known_false_positives: No false positives have been identified at this time.
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references: []
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tags:
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analytic_story:

baselines/baseline_of_cloud_instances_launched.yml

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name: Baseline Of Cloud Instances Launched
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id: b01bd274-f661-4f9c-bd9f-cf23ff6ae0bc
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version: 1
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date: '2020-08-14'
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version: 2
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date: '2026-01-14'
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author: David Dorsey, Splunk
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type: Baseline
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status: production
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period of time, which may give you better results. You may also want to periodically
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re-run this search to rebuild the model with the latest data.\nMore information
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on the algorithm used in the search can be found at `https://help.splunk.com/en/splunk-enterprise/apply-machine-learning/use-splunk-machine-learning-toolkit/5.5.0/algorithms-and-scoring-metrics-in-mltk/algorithms-in-the-splunk-machine-learning-toolkit#densityfunction-0`."
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known_false_positives: none
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known_false_positives: No false positives have been identified at this time.
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references: []
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tags:
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analytic_story:

baselines/baseline_of_cloud_security_group_api_calls_per_user.yml

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name: Baseline Of Cloud Security Group API Calls Per User
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id: 67b84d51-8329-4909-849f-8d38ce54260a
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version: 1
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date: '2020-09-07'
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version: 2
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date: '2026-01-14'
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author: David Dorsey, Splunk
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type: Baseline
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status: production
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90 days of data. You can modify the search window to build the model over a longer
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period of time, which may give you better results. You may also want to periodically
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re-run this search to rebuild the model with the latest data.
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known_false_positives: none
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known_false_positives: No false positives have been identified at this time.
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references: []
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tags:
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analytic_story:

baselines/baseline_of_command_line_length___mltk.yml

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name: Baseline of Command Line Length - MLTK
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id: d2a4d85b-fc6a-47a0-82f6-bc1ec2ebc459
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version: 1
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date: '2019-05-08'
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version: 2
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date: '2026-01-14'
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author: Rico Valdez, Splunk
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type: Baseline
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status: production
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periodically re-run this search to rebuild the model with the latest data. More
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information on the algorithm used in the search can be found at
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`https://help.splunk.com/en/splunk-enterprise/apply-machine-learning/use-splunk-machine-learning-toolkit/5.5.0/algorithms-and-scoring-metrics-in-mltk/algorithms-in-the-splunk-machine-learning-toolkit#densityfunction-0`.
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known_false_positives: none
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known_false_positives: No false positives have been identified at this time.
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references: []
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tags:
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analytic_story:

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