| name | ai-safety-governance-layer |
| description | Use when reviewing Dataverse AI actions for permissions, policy validation, risk assessment, approval routing, auditability, and explainability. Produces governance guidance only; it does not enforce policies in a live tenant. |
AI Safety Governance Layer
Purpose
Review proposed Dataverse and Power Platform AI actions before execution design is approved.
Use this skill for:
- permission and role review
- compliance and DLP risk review
- approval routing design
- explainable action requirements
- audit logging and rollback planning
- bulk update and high-impact action safety
V1 Boundary
This skill provides governance analysis and design recommendations only. It must not claim that a live tenant policy has been checked or enforced.
Workflow
- Identify action impact: data sensitivity, record count, affected processes, integrations, and reversibility.
- Assess actor authorization: role, team membership, business unit scope, field security, and environment access assumptions.
- Define policy checks: DLP, compliance, approval threshold, segregation of duties, data retention, and business rule constraints.
- Require simulation for risky actions: preview selected records, predicted changes, downstream effects, and blocked records.
- Design approval routing for medium and high risk: approver roles, escalation path, timeout behavior, and evidence package.
- Specify explainability: why the action is recommended, which data is used, which rules apply, and what will change.
Output Format
Return:
Risk rating: low, medium, high, or blocked, with rationale.Required checks: permissions, policies, dependencies, and data quality validations.Approval path: who must approve and when.Simulation requirements: preview and impact details.Audit requirements: fields, evidence, and explanation text to store.Blocking issues: items that must be resolved before implementation.
