AI-generated dashboards need a trust model

A prompt can become code

AI-generated dashboards are powerful because a question can become an artifact: filters, charts, tables, and interaction. That also means the artifact needs a trust model. It is not enough to say "the model generated it."

Perelan Views separates prompt, scoped data, generated artifact, sandbox, and sharing checks.

Scope the source data

The View should begin from data the creator is allowed to access. Restricted areas such as compensation, hiring, reviews, or private fields need explicit classification. A general visitor demo should not casually display sensitive values.

Isolate generated execution

Generated code should not be able to reach account cookies or the surrounding application. The artifact can render the dashboard, but it should not become a script with access to the rest of the product.

Separate sharing from permission granting

Sharing a dashboard should not grant the underlying data. A viewer still needs to be eligible for the source records. This can feel stricter than a spreadsheet, but it is the difference between collaboration and accidental disclosure.

Recheck access over time

The creator can lose access. A shared viewer can move teams. A company-wide view can later be classified as sensitive. The product needs lifecycle behavior that re-evaluates access rather than treating the generated artifact as permanently safe.

Design useful warnings

Warnings should be calm and specific: what is generated, why it is isolated, what data category it touches, and who can share it. A scary generic warning trains people to ignore it.