---
type: concept
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  pageDirectory: concepts
  sources:
    - deploy-models-using-model-serving-databricks-on-aws.md
  confidence: 0.9
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - databricks-model-serving-data-protection
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    - file: deploy-models-using-model-serving-databricks-on-aws.md
title: Databricks Model Serving Data Protection
description: Security controls for Model Serving including logical isolation per customer, AES-256 encryption at rest, TLS 1.2+ in transit, no training on user data, and temporary retention of logs and metrics.
tags:
  - security
  - privacy
  - compliance
timestamp: "2026-06-19T15:10:41.040Z"
---

---  
title: Databricks Model Serving Data Protection  
summary: Security controls, data handling practices, and retention policies for Databricks Model Serving, including logical isolation, encryption, and restrictions on training with customer data.  
sources:  
  - deploy-models-using-model-serving-databricks-on-aws.md  
kind: concept  
createdAt: "2026-06-25T12:00:00.000Z"  
updatedAt: "2026-06-25T12:00:00.000Z"  
tags:  
  - databricks  
  - model-serving  
  - security  
  - data-protection  
aliases:  
  - model-serving-data-protection  
  - msdp  
confidence: 0.95  
provenanceState: extracted  
inferredParagraphs: 0  
---

# Databricks Model Serving Data Protection

Databricks Model Serving implements a set of security controls and data handling practices to protect customer data submitted to and generated by the service. These measures cover logical isolation, encryption, data usage restrictions, and retention policies.

## Overview

Databricks takes data security seriously and applies the following protections to all Model Serving workloads:

- Every customer request to Model Serving is logically isolated, authenticated, and authorized. ^[deploy-models-using-model-serving-databricks-on-aws.md]
- All data at rest is encrypted using AES-256, and all data in transit is encrypted using TLS 1.2 or higher. ^[deploy-models-using-model-serving-databricks-on-aws.md]

## Data Usage and Retention

For all paid accounts, Model Serving does **not** use user inputs submitted to the service or outputs from the service to train any models or improve any Databricks services. ^[deploy-models-using-model-serving-databricks-on-aws.md]

Logging and temporary data are retained for limited durations:

- Container build logs are retained for up to thirty (30) days. ^[deploy-models-using-model-serving-databricks-on-aws.md]
- Metrics data is retained for up to fourteen (14) days. ^[deploy-models-using-model-serving-databricks-on-aws.md]

## Foundation Model APIs

The following data protection policies apply specifically to Databricks Foundation Model APIs (a subset of Model Serving):

- As part of providing the service, Databricks may temporarily process and store inputs and outputs for the purposes of preventing, detecting, and mitigating abuse or harmful uses. ^[deploy-models-using-model-serving-databricks-on-aws.md]
- Customer inputs and outputs are isolated from those of other customers, stored in the same region as the workspace for up to thirty (30) days, and are only accessible for detecting and responding to security or abuse concerns. ^[deploy-models-using-model-serving-databricks-on-aws.md]

Partner model providers may retain data for safety purposes. This retention relies on automated scanning prior to any limited human review. Models with safety retention requirements are noted on the supported models page. ^[deploy-models-using-model-serving-databricks-on-aws.md]

Foundation Model APIs are a [Databricks Designated Service](/concepts/databricks-designated-service-with-geos.md) and adhere to data residency boundaries as implemented by Databricks Geos. ^[deploy-models-using-model-serving-databricks-on-aws.md]

## Related Concepts

- [Model Serving](/concepts/model-serving.md) – Overview of the deployment and query interface.
- Data Encryption at Rest and in Transit – Foundation for the encryption controls used.
- [AI Governance](/concepts/ai-governance.md) – Framework for managing permissions, usage limits, and audit.
- [Unity Catalog](/concepts/unity-catalog.md) – Model registry and governance layer.
- Serverless Compute Security – Security model for the underlying serverless infrastructure.

## Sources

- deploy-models-using-model-serving-databricks-on-aws.md

# Citations

1. [deploy-models-using-model-serving-databricks-on-aws.md](/references/deploy-models-using-model-serving-databricks-on-aws-7a559b74.md)
