Rowan's AI data integration exposes semantic errors that access controls alone cannot prevent

Engage institutional research, the registrar, finance and enterprise IT around inconsistent answers to routine reporting questions. Ask which definitions already cause reconciliation work and who can approve the official answer. Offer a bounded comparison of a few high-frequency queries against existing signed-off reports. The value hypothesis is reduced reconciliation effort with equal or better correctness, to be tested locally. Rowan supplies concrete failure modes for discovery, not a savings benchmark. Do not promise autonomous decision-making or imply that curated data alone makes an assistant secure.

Assemble a test set with ambiguous terms, conflicting field definitions, unauthorized requests and computationally costly joins. Require approved datasets, role mappings and reference answers before connecting a model. Enforce read-only execution and resource limits in trusted code; prevent the model from expanding its own privileges. A proposed proof should compare answer correctness and total response effort against current reports and demonstrate refusal or clarification where meaning is unclear. Test after each model or schema change. An on-premises database with a cloud interface still requires end-to-end data-flow review.

Name a reporting-service owner and data stewards with time to resolve conflicting definitions. Implement a controlled release process, user feedback triage and rollback for changed metrics. Dependencies include business-owner sign-off, security testing and support training. Invite hesitant staff into usability exercises and preserve an accessible escalation route. Proposed acceptance requires every agreed critical test to match an approved definition, all unauthorized test requests to be denied, and unresolved ambiguities to reach a human. These are proposed gates, not Rowan results. Risks include silent definition drift and human reviewers trusting fluent output.