> ## Documentation Index
> Fetch the complete documentation index at: https://docs.usetero.com/llms.txt
> Use this file to discover all available pages before exploring further.

# AI Data Controls

> How AI provider paths are configured, what data is sent, and deployment-dependent control options.

export const securityEmail = "security@usetero.com";

<Badge>Last reviewed: March 5, 2026</Badge>
<Badge>Owner: Security + Engineering</Badge>
<Badge>Review cadence: Quarterly</Badge>
<Badge color="green">Status: Implemented</Badge>

This page covers how Tero configures AI provider integrations and which controls apply to AI-related data paths.

## Reviewer focus

* What data is sent to AI providers for classification and analysis workflows
* Which provider and key-ownership modes are supported
* How retention and training controls differ by provider and configuration

## Implementation status (March 5, 2026)

Tero supports three AI deployment modes: hosted default provider path, bring-your-own provider credentials, and customer-controlled self-hosted provider routing.

## AI workflow data scope

Tero sends AI providers only the data each classification or analysis task needs. By default, that means control-plane-relevant context and the telemetry samples the task requires. Tero does not ingest your full telemetry stream.

| Topic                 | Baseline approach                                                              |
| --------------------- | ------------------------------------------------------------------------------ |
| Prompt scope          | Task-scoped inputs for classification and analysis workflows                   |
| Data minimization     | Inputs are bounded to workflow requirements                                    |
| Model output handling | Outputs are used for product classification and recommendation workflows       |
| Governance            | Provider path and key ownership can be customer-controlled by deployment model |

## Provider and key ownership modes

| Mode                                | Description                                                     | Typical use case                                                  |
| ----------------------------------- | --------------------------------------------------------------- | ----------------------------------------------------------------- |
| Tero-managed provider path          | Tero-hosted default provider configuration                      | Fastest onboarding                                                |
| Bring-your-own provider credentials | Customer API credentials used for provider calls                | Customer-controlled commercial relationship with OpenAI/Anthropic |
| Self-hosted provider path           | Runtime and provider routing controlled in customer environment | Maximum infrastructure and network control                        |

## Provider data controls (external policy alignment)

| Provider path | Training usage baseline                                                                   | Retention baseline                                                                                          | Zero-retention option                                                     |
| ------------- | ----------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------- |
| OpenAI API    | API data is not used for model training by default (unless customer opts in)              | Abuse-monitoring and application-state retention depends on endpoint/configuration (default policies apply) | Available for approved organizations on eligible configurations/endpoints |
| Anthropic API | Commercial/API data is not used for model training by default                             | Standard API retention baseline applies (commercial policy default)                                         | Available by agreement for eligible enterprise API use cases              |
| AWS Bedrock   | Prompts/outputs are not used to train base models and are not shared with model providers | Bedrock service data-protection model applies                                                               | Private connectivity and customer-managed encryption controls available   |

## Deployment boundary

| Control area                  | Tero-hosted                                   | Self-hosted                                       |
| ----------------------------- | --------------------------------------------- | ------------------------------------------------- |
| Provider account boundary     | Tero-managed or customer-provided credentials | Customer-controlled                               |
| Runtime/network boundary      | Tero-hosted boundary                          | Customer infrastructure boundary                  |
| Provider policy configuration | Configured per provider path                  | Customer-configured                               |
| Compliance posture            | Tero controls + selected provider controls    | Customer environment + selected provider controls |

## Model and provider coverage

* Recommended frontier providers for production quality are Anthropic and OpenAI.
* Bedrock and additional provider paths are supported as deployment-dependent integration options.
* Additional open-source/local model paths (for example, Ollama-compatible) are possible, with quality validated case-by-case.

## Recommended strict-control profile

| Control                   | Recommended setting                                                                                   |
| ------------------------- | ----------------------------------------------------------------------------------------------------- |
| Provider account boundary | Use customer-provided credentials or self-hosted provider routing for strict boundary control         |
| Data retention mode       | Use provider zero-retention mode where eligible and contractually enabled                             |
| Training controls         | Keep provider training opt-in disabled for API data                                                   |
| Prompt scope control      | Limit prompts to minimum task-relevant context and avoid unrestricted raw payload submission          |
| Secret management         | Store provider credentials in managed secret stores with scoped runtime access                        |
| Key lifecycle             | Rotate provider credentials on a defined cadence and on any incident trigger                          |
| Network controls          | Restrict outbound destinations to approved AI provider endpoints                                      |
| Auditability              | Log AI request metadata and outcome events without exposing sensitive prompt text in operational logs |

## External references

* [OpenAI data controls](https://platform.openai.com/docs/guides/your-data)
* [Anthropic retention and zero data retention FAQs](https://privacy.anthropic.com/en/articles/7996866-how-long-do-you-store-my-organization-s-data)
* [Anthropic zero data retention scope](https://privacy.anthropic.com/en/articles/8956058-i-have-a-zero-data-retention-agreement-with-anthropic-what-products-does-it-apply-to)
* [Amazon Bedrock data protection](https://docs.aws.amazon.com/bedrock/latest/userguide/data-protection.html)
* [Amazon Bedrock security/privacy overview](https://aws.amazon.com/bedrock/security-privacy-responsible-ai/)

## Evidence you can request

| Topic                            | Primary evidence                                                            |
| -------------------------------- | --------------------------------------------------------------------------- |
| High-level data scope            | [Overview](/trust/overview), [Data Handling](/trust/controls/data-handling) |
| Ownership split                  | [Shared Responsibility](/trust/shared-responsibility)                       |
| Identity and credential controls | [Identity and Access](/trust/controls/identity-access)                      |
| Encryption and key controls      | [Encryption and Key Management](/trust/controls/encryption-key-management)  |

## Exceptions and governance

Any AI data-path exception requires documented approval, compensating controls, and a time-bound remediation plan.

Evidence requests: [{securityEmail}](mailto:\{securityEmail})