Policies can be managed in the Governance Console.
Once a policy is published, it becomes immutable, ensuring the lineage between a Governed Bundle and the approved policy can be maintained.
To complete these tasks, you’ll need to be assigned the GovernanceAdmin role.
CloudAdmins
and SysAdmins
already have permissions associated with Domino Governance.
This document explains the YAML configuration structure that defines various input fields, policy scripted checks, metadata, and guidance elements.
Evidence and evidence sets
Evidence includes inputs, approvals, and checks that should be gathered as part of the governance process within a given stage. Evidence sets represent logical groupings of evidence and can also be reused in other policies if needed. Local evidence should have the full definition included when appearing for the first time in the policy. It can later be referenced by id when using it in the YAML file.
yaml
evidenceSet:
- id: Local.sample
name: sample local evidence
description: Describe the sample local evidence
definition: Define the evidence
Metrics checks
Model Metrics are sets of policy-defined metrics used for automated pre-approval checks. Each set follows a shared baseline structure and can be extended with additional metrics. Fields include aliases for detection, threshold operators (e.g., >, <), and expected values—helping reduce manual review for governance administrators.
yaml
metrics:
- id: Local.model-quality
name: Model Quality
description: Describe the model quality
definition:
- artifactType: metadata
details:
type: modelmetric
metrics:
- name: Acc
label: Accuracy
aliases:
- acc
- Correct Classification Rate
- Percentage Correct
threshold:
operator: '>='
value: 0.8
Scripted checks
Scripted Checks use centralized, policy-defined scripts to evaluate Governed Bundles, for example, to measure dataset bias. Defined in the policy YAML, these scripts run in a specified environment and attach output files to the evidence notebook. This supports standardized, auditable validation across projects.
yaml
- artifactType: policyScriptedCheck
details:
name: Ethic and Fairness Evaluation
label: Ethic and Fairness Evaluation
command: evaluate_model.py create --model-hub ${model_hub} --model-name ${model_name}
parameters:
- name: model_hub
type: text
default: openai
- name: model_name
type: text
default: gpt-4
outputTypes:
- txt
- png
environmentId: 674f04e2191e8f19a5d12552 # 6.0 default environment on se-demo
hardwareTierId: small-k8s
volumeSizeGiB: 4
- artifactType: metadata
details:
label: Upload model validation report.
type: file
Radio buttons
Defines a set of radio buttons with a list of choices, each with a label (displayed text) and value (submitted data).
yaml
- artifactType: input
details:
type: radio
label: "How would you rate the model risk?"
options:
- label: "High"
value: "High"
- label: "Medium"
value: "Medium"
- label: "Low"
value: "Low"
tooltip: "Guidance text"
Text input
Defines a text field for user input, used to collect written responses or descriptions.
yaml
- artifactType: input
details:
type: textinput
label: "What are the expected business benefits?"
placeholder: "Explain the benefit"
helpText: "The text under the input box to help the user"
Text area
Defines a multi-line text input field.
yaml
- artifactType: input
details:
type: textarea
label: "What are the expected business benefits?"
height: 10
placeholder: "Explain the benefit"
helpText: "The text under the input box to help the user"
Select dropdown
Defines a dropdown selection field.
yaml
- artifactType: input
details:
type: select
label: "Please select the base model template."
options:
- label: "base model1"
value: "baseModel1"
- label: "base model2"
value: "baseModel2"
Multi-select
Defines a multi-select dropdown field and allows selection of multiple options.
yaml
- artifactType: input
details:
type: multiSelect
label: "Please select the data sets used in the model."
options:
- label: "data set1"
value: "dataset1"
- label: "data set2"
value: "dataset2"
- label: "data set3"
value: "dataset3"
Checkbox group
Defines a group of checkboxes and allows selection of multiple options.
yaml
- artifactType: input
details:
type: checkbox
label: "Please select the departments that will use the model?"
options:
- label: "Sales"
value: "DEPT001"
- label: "Customer Success"
value: "DEPT002"
Guidance artifacts
Provide users with informational content using textblock type, which displays Markdown-formatted text.
yaml
- artifactType: guidance
details:
type: textblock
text: >-
[Map 1.4](https://ournistpolicyreferenceurl.com) The business value or
context of business use has been clearly defined or - in the case of
assessing existing AI systems - re-evaluated
Display prominent text banners to provide important notices or key information to users.
yaml
- artifactType: guidance
details:
type: banner
text: >-
[Map 1.4](https://ournistpolicyreferenceurl.com) The business value or
context of business use has been clearly defined or - in the case of
assessing existing AI systems - re-evaluated
Approvals
Approvals are defined under stages. Each approval is defined with a name, a group of specified approvers, and evidence. Approvers must be Domino users or organizations and are specified by the user’s or organization’s name.
yaml
- name: 'Stage 4: validation sign off'
approvers:
- model-gov-org
evidence:
id: Local.validation-approval-body
name: Sign-off
description: The checklist for approvals
definition:
- artifactType: input
details:
label: "Have you read the model validation reports?"
type: radio
options:
- Yes
- No
Classification
A top-level policy variable used to assign risk tiers (e.g., low, medium, high) to a Governed Bundle, with support for tooltips, rules, and artifact-based references.
yaml
classification:
rule:
artifacts:
- model-risk
stages:
- name: classificationExample
- id: Local.model-risk
name: Model Risk
description: Describe the risk of the model
definition:
- artifactType: input
aliasForClassification: model-risk
details:
label: "How would you rate the model risk?"
type: radio
options:
- High
- Low
tooltip: guidance on how to rate the model risk within the organization