Your AI Is Only as Good as What It's Allowed to Read.

Grounding lets a model retrieve your material instead of relying only on its training. It does not assess whether the connected sources are authoritative, current, complete, or available to the requester.

One system may use approved, current policies while another searches a shared drive full of outdated drafts and duplicates. Both use retrieval, but their source quality differs.

IBM says models trained on flawed, biased, or incomplete data produce unreliable outputs regardless of the architecture. Retrieval has a related risk: a model reading a stale policy can give a fluent answer based on obsolete material.

Output quality can hide a bad input. Record source status, approval, version, and date so the retrieval system can prefer the current authoritative document.

IBM defines AI data quality as the degree to which data is accurate, complete, reliable, and fit for use across the AI lifecycle. Apply those tests before connecting a source.

Check whether the document is approved, complete for the intended question, current, and written for the relevant context. Mark drafts and superseded versions so the system does not treat them like active policy.

More connected systems do not automatically mean better answers. Connect the approved policy library, not every draft folder. Prefer primary regulations over summaries when the claim is legal. Preserve SharePoint, Drive, Jira, and ticketing permissions instead of flattening access. Rank current approved records above stale exports.

Source governance asks more than whether the AI can read something. It asks whether this source should be allowed to answer this claim for this user right now.

Model choice gets attention, but teams have more direct control over the connected sources. They decide which systems and documents may support an answer, who may retrieve them, and when a version becomes stale.

Keep the set permissioned and current. A small set of relevant, approved sources can be more useful than a large collection of unvetted files.

Source quality at Sorena starts with the connection. Sorena Integrations let you connect selected trusted systems and documents, so the AI reads verified material within the permitted scope.

Those sources flow into Sorena SSOT, our Single Source of Truth, where the curated, permissioned set is governed together. The AI works from that set, and source owners decide what belongs in it.

IBM describes high-quality data as a foundation for trusted and effective AI and recommends managing dimensions such as accuracy, completeness, and consistency as systems grow.

A curated source set reduces the chance that the model retrieves a stale or irrelevant document. It also gives reviewers a clearer trail from each claim to the approved material behind it.

Choosing the model does not replace source governance. Curate the connected set, keep permissions intact, identify approved versions, and remove stale material before the system cites it.

A well-kept source set gives reviewers a better chance of receiving a useful, traceable answer. The people who own the sources remain accountable for their quality.