Governed AI data discovery helps users find suitable data through business language while preserving the context and permissions needed for responsible use. It should connect a question to relevant assets, explain their meaning and limits, and support an appropriate next step.
A natural language interface does not make a dataset authoritative or a query correct. It needs approved business context, suitable asset coverage, and a verified authorization path.
Start with a question that contains ambiguity
Consider an illustrative question: “Show revenue by region for last month.” The system needs to determine which revenue definition applies, which month and timezone are intended, which assets support the metric, and which regions the user is authorized to see.
If several approved definitions are available, the experience should clarify the choice or state the interpretation. Quietly selecting a plausible table can produce an answer that looks useful while addressing the wrong question.
Connect discovery to approved context
| Context | What the user needs |
|---|---|
| Business definition | Which revenue concept is being used |
| Asset purpose and grain | Whether the data fits the question |
| Owner | Who can resolve an ambiguity |
| Quality and freshness | Which relevant checks and time boundaries apply |
| Access scope | Which population and fields are permitted |
| Source reference | Which asset or definition supports the result |
Keep these explanations concise and inspectable. A confident narrative should not conceal unresolved ambiguity or missing metadata.
Test the same question with different roles
Use two illustrative roles. The West analyst is permitted to use West-region data. The enterprise finance role has an approved cross-region scope. Both ask the same revenue question.
The permitted populations should differ according to the implemented policies, while the approved metric meaning remains consistent. The interface should show the applied scope so users can interpret the result. If the environment permits only metadata discovery, it should not imply that a query has executed against underlying data.
Test previews, query results, exports, follow-up questions, and revoked access where they are supported. Include attempts to rephrase a question to obtain a restricted population. Authorization should not depend on a particular wording.
Verify generated descriptions and queries
An AI-generated description can misinterpret a field or omit a material exclusion. An AI-generated query can join at the wrong grain or apply the wrong period. Treat both as results that require appropriate evaluation.
Build a small reference set of business questions with approved interpretations and expected permitted results. Include ambiguous terms, similar asset names, missing data, and a question that the available assets cannot answer. A useful system should acknowledge insufficient evidence instead of inventing an offering or result.
Build a reference test set for natural language discovery
Start with questions consumers actually ask and have the domain owner approve their interpretation. For each question, record the intended metric, period, population, suitable asset, and permitted next action. Include a question with several plausible answers and one that the approved data cannot support. These cases test whether the experience handles uncertainty as carefully as familiar requests.
For the revenue example, add a follow-up asking whether returns are included and another asking to show all regions. The first tests whether the approved definition remains consistent across the conversation. The second tests whether the user's access scope is preserved. A fluent response to either question is insufficient unless the interpretation and permitted population remain correct.
Repeat the reference questions after changes to definitions, sources, or permissions. Retain the evaluated configuration and expected answers so the team can distinguish a deliberate policy change from a regression. Review samples of failed and successful tasks with the people responsible for the underlying business meaning.
Measure successful discovery
Review whether users find an appropriate asset, understand its scope, and complete a permitted task. Record failed searches, clarification needs, incorrect asset selection, and authorization failures separately.
Search speed and answer fluency are useful experience measures, but they do not establish correctness. Evaluate source relevance, definition fidelity, appropriate access, and the ability to verify the result.
Keep retrieval context separate from instructions to the assistant
Discovered descriptions and source documents can contain misleading or malicious instructions. Treat retrieved material as evidence to interpret rather than authority to override the user's permissions or the application's controls. A sentence inside a document telling an assistant to ignore access restrictions should not change what the user is allowed to retrieve.
Test the implemented workflow with an approved adversarial example and a document containing an obsolete definition. Confirm that source references, approval state, and effective period remain visible where relevant. If unstructured material is part of discovery, review its indexing and retrieval permissions as well as access to the original file.
The control design should cover follow-up questions and retained context. A user whose access has changed should not regain a restricted result by asking the assistant to repeat an earlier answer. Confirm how the application handles conversation history, cached results, and exports under the supported authorization model.
Demonstrate the DataMarket workflow
DataMarket supports natural language exploration and authorized consumption of governed data products. RightSight provides metadata and business context that can support those tasks. Demonstrate the supported behavior for a representative question, asset population, and set of user roles.
Avoid assuming that every source is searchable, every permission propagates automatically, or every generated query is ready to execute without review. Confirm the configured source coverage and consumption path.
Read the semantic layer guide for consistent definitions and catalog versus marketplace for consumer workflow design.
Track the difference between finding a plausible asset and completing the intended task successfully. Ask reviewers to assess relevance, definition fidelity, and permitted scope separately. When a result fails, preserve the question and supporting references under appropriate access controls. This makes evaluation actionable: the team can correct missing metadata, an ambiguous definition, or a retrieval problem instead of treating every failure as poor wording by the user.
Give users a result they can verify
Explain which asset was found, why it fits the question, and which definition and scope were applied. If the system generated a query, distinguish that proposal from an executed result. If it only found metadata, say so. Clear action boundaries prevent users from treating an asset suggestion as a completed analysis.
Offer a useful next step when evidence is insufficient. That might be a clarification question, an owner contact, or a request for a missing product. Avoid answering from a similarly named dataset merely because it is available. Reliable discovery includes knowing when the approved collection cannot support the requested decision.
Frequently asked questions
Can AI discovery expose restricted data
An inadequately designed or configured workflow can create disclosure risks. Verify permissions across metadata, snippets, previews, results, and exports, including revoked access and follow-up questions.
Is natural language discovery the same as generated query execution
No. Finding an asset, explaining it, and executing a query are separate capabilities. The interface should make clear which action occurred.
What should happen when no suitable asset exists
State that the available approved assets do not support the request and route the user to a relevant owner or request process. Do not invent a dataset or an unsupported answer.
Request a Demo using the same business question for two roles with different approved access scopes.
