Power BI Policy Engine
Best-practice documents do not stop
unsafe Power BI model changes
SemanticOps turns governance rules into enforceable policies that check semantic model operations before they happen — blocking unsafe changes and requiring validation where needed.
A best-practice document cannot block an AI agent from deleting a production measure.
Most Power BI governance lives in documents, review meetings, or tribal knowledge. That does not stop a developer or AI agent from making an unsafe change. SemanticOps enforces rules at the point of change — before the operation executes.
Policy types
Five categories of enforceable policy
Policies are configured in a bundle and evaluated before every relevant operation. Teams can start with a standard pack and extend it for their environment.
Destructive-change policies
- —Block deleting measures with dependencies
- —Block deleting columns used by measures or reports
- —Require impact analysis before renaming objects
- —Prevent relationship deletion without review
Semantic-quality policies
- —Require descriptions on public measures
- —Enforce naming conventions
- —Block exposed technical columns
- —Flag bidirectional relationships
- —Require display folders
Security policies
- —Prevent RLS / OLS changes without tests
- —Require role validation before deployment
- —Block exposing sensitive columns
- —Require masking for data-returning queries
AI-safety policies
- —Prevent direct production edits
- —Restrict data-returning queries
- —Block bulk changes unless dry-run passes
- —Require test execution after model changes
Deployment policies
- —Require test suite pass before release
- —Require no high-severity impact findings
- —Require rollback checkpoint before change
- —Require documentation baseline
Custom policies
Extend any policy bundle with rules specific to your team, environment, or model risk profile.
Policy outcomes
Three policy decisions
Each policy check produces one of three outcomes. The outcome determines what happens next — nothing, a hard block, or an approval gate.
Action is permitted. Proceeds without interruption.
Action cannot proceed. No override is available at the user level.
A human must explicitly approve before the operation executes.
AI safety
Policies enforce governance
even when AI is executing the operations
Document-based governance assumes a human reads and follows the rules. Policy enforcement does not. When an AI agent calls a SemanticOps tool, the policy engine evaluates the action — the same rules apply whether the operator is a developer or an autonomous agent.
Document-based governance
- —Requires a human to read and follow the rule
- —Cannot intercept an AI tool call
- —No mechanism to block a destructive operation
- —No record of which rules were checked
SemanticOps policy engine
- ✓Evaluates every tool call before execution
- ✓Works the same for humans and agents
- ✓Can allow, deny, or gate any operation
- ✓Records policy decision in the audit log
Turn governance rules into enforcement.
Define policies that run before every semantic model operation. No document required.