Data Theorem scanner reference for STO
You can extract and ingest scan results for your code repositories from Data Theorem.
Workflow descriptions
Ingestion workflows
This workflow applies to scanner integrations that support Ingestion mode.
-
Add a Build or Security stage to your pipeline.
-
Add a Run step and set it up to save your scan results to a shared folder.
For more information, go to Run an ingestion scan in an STO Pipeline.
-
Add a Custom Scan step.
-
Review the Important notes for Custom Scan steps for additional requirements and relevant information.
-
Add the relevant
key:value
pairs to Settings.
Orchestration/extraction workflows
This workflow applies to scanner integrations that support orchestratedScan
or dataLoad
scan modes.
-
Add a Build or Security stage to your pipeline.
-
If you're scanning a code repository, set up your codebase.
-
Add a Custom Scan step.
-
Review the Important notes for Custom Scan steps for additional requirements and relevant information.
If you're setting up a scan on a Kubernetes or Docker build infrastructure, you need to add a Docker-in-Docker background step to the stage.
-
Add the relevant
key:value
pairs to Settings.
Custom Scan step settings for Data Theorem scans
Scanner settings
These settings are required for most scanners. For more information, go to the reference for the scanner integration you're setting up.
Product name
The scanner name. This is required for all Custom Scan steps.
Key
product_name
Value
data-theorem
Scan type
The target type to scan.
Key
scan_type
Value
repository
Policy type
The scan mode to use.
Key
policy_type
Value
Must be one of the following. For supported values, go to the relevant scanner reference.
ingestionOnly
dataLoad
Product config name
Required for most scanner integrations.
Key
product_config_name
Value
default
Target and variant
Every Custom Scan step needs a target and baseline.
Target name
Key
target_name
Value
A user-defined label for the code repository, container, application, or configuration to scan. Specify a unique, descriptive name. This makes it much easier to navigate your scan results in the STO UI.
Target variant
Key
target_variant
Value
A user-defined label for the branch, tag, or other target variant to scan.
Code repository
These settings apply to Custom Scan steps when both of these conditions are true:
- The
policy_type
isorchestratedScan
ordataLoad
. - The
scan_type
isrepository
.
Repository project
Key
repository_project
Value
The name of the repo to scan. To specify the repo URL, edit the Codebase Config object in the Harness pipeline.
In most cases, this should match the repo name used in your Git provider.
Repository branch
Key
repository_branch
Value
The branch that gets reported in STO for the ingested results. In most cases, this field should match the name of the Git branch that is getting scanned.
You can specify a hardcoded string or use a variable such as <+codebase.branch>
to specify the branch at runtime. For more information, go to:
Ingestion file
This setting applies to Custom Scan steps when the policy_type
is ingestionOnly
.
Key
ingestion_file
Value
The path to your scan results when running an Ingestion scan, for example /shared/scan_results/myscan.latest.sarif
.
-
The data file must be in a supported format for the scanner.
-
The data file must be accessible to the scan step. It's good practice to save your scan results to a shared path in your stage. In the visual editor, go to the stage where you're running the scan. Then go to Overview > Shared Paths. You can also add the path to the YAML stage definition like this:
- stage:
spec:
sharedPaths:
- /shared/scan_results
Product access
These settings are available to access your Data Theorem SaaS instance when policy_type
is dataLoad
.
You should create Harness text secrets for your encrypted passwords and tokens and reference them using the format <+secrets.getValue("my-access-token")>
.
Product access keys
product_app_id
product_access_token
Fail on Severity
If the scan finds any vulnerability with the specified severity level or higher, the pipeline fails automatically. NONE
means do not fail on severity.
For more information, go to Stop builds based on scan results.
Key
fail_on_severity
Value
CRITICAL
MEDIUM
LOW
INFO
NONE