> For the complete documentation index, see [llms.txt](https://v2.dataos.info/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://v2.dataos.info/references/interfaces/ai-activation/trust-model.md).

# Trust model

AI activation has two trust layers.

## Platform-governed trust

The first layer is governed by the platform.

Data Product MCP tools return structured responses from Data Product APIs. Authorization, filtering, masking, semantic validation, quality retrieval, lineage retrieval, and owner lookup are enforced before the response reaches the assistant.

## Assistant-shaped trust

The second layer is shaped by the assistant.

The assistant chooses Data Product MCP tools, passes arguments, and explains results. Data Product MCP reduces risk by providing tool descriptions, strict parameters, structured response fields, warnings, and citations.

The assistant still controls the final wording.

## What this means in practice

AI activation is governed assistant access. It is not autonomous data authority.

The Data Product remains the source of truth. The assistant is the interface that helps users reach it.

## Boundaries

AI activation does not bypass DataOS controls. It is not a general-purpose database connection.

Data Product MCP does not:

* Expose raw SQL access for arbitrary querying.
* Return data the user is not authorized to see.
* Invent missing measures, owners, quality results, or lineage.
* Modify, retrigger, or remediate runtime jobs through the consumption Data Product MCP tools.
* Remove the need for review when AI assists with building a Data Product.

Generated Data Product artifacts should still be reviewed for semantic correctness, quality rules, data contracts, and environment-specific configuration before deployment.


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