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Security & Trust Center

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Security Best Practices

For the Databricks Data Intelligence Platform

Secure your deployments

Databricks has worked with thousands of customers to securely deploy the Databricks Data Intelligence Platform with the appropriate features to meet their security, privacy and regulatory requirements. While many organizations deploy security differently, there are patterns and features that are commonly used by most organizations. Here are the most relevant controls to Define, Deploy and Monitor your security posture according to our best practices.

Security Best Practices for Databricks on AWS

Define

Our Best Practice guides provide a detailed overview of the main security controls we recommend for typical and highly secure environments — based on what we see working in partnership with our most security-conscious customers.

Review these documents to get:

  • A simple checklist for the key security controls that are most prevalent among our customers
  • Threat models with the top security risks faced by our customers along with the specific controls to address them 
  • An appendix with descriptions, practitioner context and links for these key controls

Check out our security best practices for AWS, Azure and GCP

Security Reference Architecture

Deploy

Our Security Reference Architecture (SRA) Terraform templates make it easy to automatically create Databricks environments that follow these security best practices.

While securing a single workspace is straightforward with the checklist in our Best Practices guide, aligning an automation that has many workspaces can be very complex. Having terraform templates that include security from the start makes that much easier.

Check out our GitHub repo to get started on AWS, Azure and GCP

Security Analysis Tool (SAT)

Monitor

Our Security Analysis Tool (SAT) continuously monitors the security posture of your Databricks Data Intelligence Platform in accordance with these security best practices.

With these resources, you can easily follow best practices when you deploy your workspace. But how do you make sure it stays that way? And if you’re an existing customer, how easily can you review all your workspaces to see how they compare with our best practices? The Security Analysis Tool is built for exactly these scenarios.

Check out our GitHub repo to get started on AWS, Azure and GCP

Additional resources

Learn more about securing your Databricks Data Intelligence Platform and mitigating the latest wave of AI security risks with the following resources:

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YouTube series

If you’re more of a visual person, check out our Security Best Practices YouTube series.

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AI security

For a comprehensive overview of how to mitigate AI security threats based on real-world attack scenarios, download and review the Databricks AI Security Framework (DASF).

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Well-architected

A robust architecture is the foundation of good security. Check out the well-architected framework for AWS, Azure and GCP.

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Data governance

Another pillar of good security is strong data governance, so be sure to take a look at our Unity Catalog best practices for AWS, Azure and GCP.

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Shared Responsibility Model

To understand the Shared Responsibility Model we operate under, please refer to our documentation for AWS, Azure and GCP.

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Databricks blogs

For more content from the Databricks Product and Security teams, take a look at our Platform and Security blogs.

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Community blogs

For more content from the experts who support the Databricks Security team, check out our Platform SME blogs.

Partners

Obsidian

Obsidian Security combines application posture with identity and data security, safeguarding SaaS.

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EQTY Lab

EQTY Lab builds advanced governance solutions to evolve trust in AI.

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AppSOC

Makes Databricks the most secure AI platform with real-time visibility, guardrails and protection.

Learn more