Quick answer
A Dataverse agent user should receive a purpose-built security role for one bounded workflow. Scope data and operations before creating access, validate both success and denial paths in non-production, make every change attributable to the agent, and define suspension and retirement from day one.
The integration is a public preview. Use it for evaluation, not as an implied production recommendation, and recheck availability, licensing, region, restrictions, and supplemental terms.
Role design worksheet
| Dimension | Define before assignment |
|---|---|
| Environment | Exact non-production environment and business unit |
| Data | Tables, rows, ownership, columns, sensitivity, and related-record traversal |
| Operations | Read, create, update, append, assign, share, export, delete, and bulk behavior |
| Purpose | Business process and prohibited uses |
| Time | Evaluation dates, access-review cadence, and expiry |
| Dependencies | Connectors, flows, plugins, APIs, downstream systems, and credentials |
| Approval | Business owner, data owner, security owner, and exception approver |
Model roles around tasks, not departments. Separate research or read access from mutations. Gate exports, bulk changes, deletes, sharing, assignments, and privilege changes independently. Review aggregate access inherited through teams, business units, related records, and downstream integrations.
Prove expected and denied behavior
Create a test matrix for allowed records and operations, then mirror it with out-of-scope tables, sensitive columns, another business unit, another environment, unexpected relationship traversal, stale records, and excessive volume. Include prompt injection and malformed tool arguments when an AI agent decides which operation to invoke.
Test what happens when the role is removed, identity is disabled, token is still active, owner leaves, connector fails, workflow retries, or the agent partially updates a multi-step process. Confirm the stop path works without cooperation from the agent.
Audit the action chain
Log the Entra agent identity, Dataverse agent user, effective role, environment, record, operation, before/after state or change reference, originating workflow, on-behalf-of context, timestamp, and correlation ID. Verify that administrators can distinguish the agent from a human user and a conventional application integration.
Define alerts for unusual tables, fields, volume, time, export, repeated denial, role changes, and attempts after suspension. Retain evidence according to privacy, security, compliance, and incident requirements.
Lifecycle and retirement
Review access whenever purpose, data, tools, environment, owner, or implementation changes. At retirement, stop workloads, disable identity access, invalidate tokens, remove Dataverse roles and environment membership, preserve required audit evidence, and delete the agent user when appropriate.
Use the identity comparison to select constructs and the readiness checker before starting the preview.
Frequently asked questions
Which role should a Dataverse agent user receive?
Use a dedicated, least-privileged Dataverse security role aligned to the agent’s defined tables, records, columns, and operations rather than a broad human or application role.
What should preview testing include?
Test expected and denied reads, writes, creates, deletes, exports, assignments, bulk actions, environment access, role changes, revocation, and audit attribution.
Who owns an agent user’s lifecycle?
Document business and security owners, purpose, approval, access reviews, changes, incident response, suspension, token revocation, role removal, evidence retention, and retirement.
Official sources
Source check: August 12, 2026. Recheck preview status, Dataverse role semantics, audit support, lifecycle behavior, regions, licensing, and terms.