Google was the first in the industry to publish an [AI/ML Privacy Commitment](https://cloud.google.com/blog/products/ai-machine-learning/google-cloud-unveils-ai-and-ml-privacy-commitment), which outlines our belief that customers should have the highest level of security and control over their data that is stored in the cloud. That commitment extends to Google Cloud's generative AI products. Google ensures that its teams are following these commitments through robust data governance practices, which include reviews of the data that Google Cloud uses in the development of its products. More details about how Google processes data can also be found in Google's [Cloud Data Processing Addendum](https://cloud.google.com/terms/data-processing-addendum) (CDPA). ## Training restriction As outlined in "Training Restriction" in the [Service Terms section of the Service Specific Terms](https://cloud.google.com/terms/service-terms), Google won't use your data to train or fine-tune any AI/ML models without your prior permission or instruction. This applies to all managed models on Gemini Enterprise Agent Platform, including GA and pre-GA models. ## Customer data retention and achieving zero data retention Customer data is retained in Gemini Enterprise Agent Platform for Models as a Service (MaaS) for limited periods of time in the following scenarios and conditions. To achieve zero data retention, customers must take specific actions within each of these areas: - **Prompt logging for abuse monitoring for Google models** : As outlined in Section 4.3 "Generative AI Safety and Abuse" of [Google Cloud Platform Terms of Service](https://cloud.google.com/terms), Google may log prompts to detect potential abuse and violations of its [Acceptable Use Policy](https://cloud.google.com/terms/aup) and [Prohibited Use Policy](https://policies.google.com/terms/generative-ai/use-policy) as part of providing generative AI services to customers. Only customers whose use of Google Cloud is governed by the [Google Cloud Platform Terms of Service](https://cloud.google.com/terms) are subject to prompt logging for abuse monitoring. If you are in scope for prompt logging for abuse monitoring and want zero data retention, you can request an exception for abuse monitoring. See [Abuse monitoring](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/abuse-monitoring). - **Prompt and response logging for abuse monitoring for Advanced AI models** : Certain models or features may also be subject to additional prompt and response logging to prevent abuse, as described in the [Advanced AI Safety Addendum](http://cloud.google.com/terms/advanced-ai-safety-addendum). Zero data retention may not be possible when using some Advanced AI features. Please contact your account team for clarification. See [Abuse monitoring](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/abuse-monitoring) for more information. - **Grounding with Google Search** : As outlined in Section 20 "Generative AI Services: Grounding with Google Search" of the [Service Specific Terms](https://cloud.google.com/terms/service-terms), Google collects and stores logs, which contains the following Customer Data: queries derived from End User prompts and contextual information that Customer may provide along with the prompts that are not associated with any Customer or its End Users for up to three (3) days, this stored information may be used for debugging of systems that support Grounding with Google Search. There is no way to disable the storage of this information if you use Grounding with Google Search. If you require zero data retention, we recommend using [Web Grounding for Enterprise](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/grounding/web-grounding-enterprise). - **Grounding with Google Maps** : As outlined in Section 20 "Generative AI Services: Grounding with Google Maps" of the [Service Specific Terms](https://cloud.google.com/terms/service-terms), Google stores prompts and contextual information that customers may provide, and generated output for thirty (30) days for the purposes of creating grounded results, and this stored information may only be used for reliability engineering, such as debugging in case of service issues, of systems that support grounding with Google Maps. There is no way to disable the storage of this information if you use Grounding with Google Maps. - **Request-response logging** : This feature is disabled by default. It can be enabled using a configuration setting on a per-model, per-project basis. Enabling this logging causes certain requests and responses to the specified model to be written to a designated BigQuery table. To achieve zero data retention, do not enable this feature. For more information about this feature, including how to enable or disable it, or read the current configuration setting, see [Log requests and responses](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/capabilities/request-response-logging). - **Interactions API** : When using the [Interactions API](https://docs.cloud.google.com/gemini-enterprise-agent-platform/reference/models/interactions-api) with `store = true`, Google stores user data (such as prompts, responses, and conversation state) to enable multi-turn conversations and later retrieval. If you do not specify a value for `store`, it defaults to `true` for all models. To achieve zero data retention, explicitly set `store = false` in your API requests. This applies to all managed models on Gemini Enterprise Agent Platform, including GA and pre-GA models. ## Agent products To achieve zero data retention for agent products, customers must adhere to the following conditions and retention schedules for each product: - **CodeMender session data** --- To enable resumption of long-running find and fix scans, [CodeMender](https://docs.cloud.google.com/gemini-enterprise-agent-platform/agents/codemender) stores session state in Spanner. While a scan is in progress, session data --- including target source code snippets, diffs, configuration, and analysis checkpoints --- is stored encrypted for up to seven (7) days from session creation. When a session reaches a terminal state, source code and related content are cleared within seconds; the remaining session record is deleted automatically at the seven-day TTL. Customers can delete a session (removing all associated data) before the TTL expires. - **Deep Research agent session data** --- Google stores prompts and session data - including search results and generated images - via the Interactions API for a period of seven (7) days. This session data is encrypted at rest, and is deleted automatically at the end of the seven-day TTL. Customers can delete a session (removing all associated data) before the TTL expires. This feature cannot be disabled when using the Deep Research agent. - **Agent Platform Sandbox Snapshots**: If you use the Snapshot API to persist Sandbox state, these snapshots are stored only for the duration of the Sandbox TTL. To achieve zero-data-retention, don't use the Sandbox API. ## Google models To achieve zero data retention for Google trained models, customers must take additional specific actions within each of these areas: ### Session resumption for Gemini Live API Session resumption for Gemini Live API is disabled by default. It must be enabled by the user every time they call the API by specifying the field in the API request, and project-level privacy is enforced for cached data. Enabling Session Resumption allows the user to reconnect to a previous session within 24 hours by storing cached data, including text, video, and audio prompt data and model outputs, for up to 24 hours. To achieve zero data retention, do not enable this feature. For more information about this feature, including how to enable it, see [Gemini Live API](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/live-api#session-resumption). ### In-memory data caching By default, Google's published Gemini models cache Customer Data (inputs, outputs, and derived data) in-memory to reduce latency and accelerate responses. This data is stored only in-memory (not at-rest), is isolated at the project level, and has a 24-hour TTL. Cached data is used only for improving service performance and adheres to all Data Residency requirements for the selected location and does not violate zero data retention. This feature can be disabled at the project level. #### Enabling and disabling data caching You can use the following curl commands to get caching status, disable caching, or re-enable caching. When you disable or re-enable caching, the change applies to all Google Cloud regions. For more information about using Identity and Access Management to grant permissions required to enable or disable caching, see [Gemini Enterprise Agent Platform access control with IAM](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/general/access-control). Expand the following sections to learn how to get the current cache setting, to disable caching, and to enable caching. #### Get current caching setting Run the following command to determine if caching is enabled or turned off for a project. To run this command, a user must be granted one of the following roles: `roles/aiplatform.viewer`, `roles/aiplatform.user`, or `roles/aiplatform.admin`. ```bash PROJECT_ID=PROJECT_ID # Setup project_id $ gcloud config set project PROJECT_ID # GetCacheConfig $ curl -X GET \ -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \ -H "Content-Type: application/json" \ https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/cacheConfig # Response if caching is enabled (caching is enabled by default). { "name": "projects/PROJECT_ID/cacheConfig" } # Response if caching is turned off. { "name": "projects/PROJECT_ID/cacheConfig" "disableCache": true } ``` #### Turn off caching Run the following curl command to disable caching for a Google Cloud project. To run this command, a user must be granted the Gemini Enterprise Agent Platform administrator role, `roles/aiplatform.admin`. ```bash PROJECT_ID=PROJECT_ID # Setup project_id $ gcloud config set project PROJECT_ID # Setup project_id. $ gcloud config set project ${PROJECT_ID} # Opt-out of caching. $ curl -X PATCH \ -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" \ -H "Content-Type: application/json" \ https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/cacheConfig \ -d '{ "name": "projects/PROJECT_ID/cacheConfig", "disableCache": true }' # Response. { "name": "projects/PROJECT_ID/locations/us-central1/projects/PROJECT_ID/cacheConfig/operations/${OPERATION_ID}", "done": true, "response": { "@type": "type.googleapis.com/google.protobuf.Empty" } } ``` #### Enable caching If you disabled caching for a Google Cloud project and want re-enable it, run the following curl command. To run this command, a user must be granted the Gemini Enterprise Agent Platform administrator role, `roles/aiplatform.admin`. ```bash PROJECT_ID=PROJECT_ID LOCATION_ID="us-central1" # Setup project_id $ gcloud config set project PROJECT_ID # Setup project_id. $ gcloud config set project ${PROJECT_ID} # Opt in to caching. $ curl -X PATCH -H "Authorization: Bearer $(gcloud auth application-default print-access-token)" -H "Content-Type: application/json" https://us-central1-aiplatform.googleapis.com/v1/projects/PROJECT_ID/cacheConfig -d '{ "name": "projects/PROJECT_ID/cacheConfig", "disableCache": false }' # Response. { "name": "projects/PROJECT_ID/locations/us-central1/projects/PROJECT_ID/cacheConfig/operations/${OPERATION_NUMBER}", "done": true, "response": { "@type": "type.googleapis.com/google.protobuf.Empty" } } ``` ## What's next Overview ### [Responsible AI](https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/responsible-ai) Learn about some of the limitations to generative AI and recommended practices for using generative AI. Overview ### [How Gemini for Google Cloud uses your data](https://docs.cloud.google.com/gemini/docs/discover/data-governance) Learn how Gemini for Google Cloud conforms to Google's privacy commitment with generative AI technologies.