At Databricks, we all know that knowledge is one in all your Most worthy property. Our product and safety groups work collectively to ship an enterprise-grade Information Intelligence Platform that allows you to defend towards safety dangers and meet your compliance obligations. Over the previous yr, we’re proud to have delivered new capabilities and assets similar to securing knowledge entry with Azure Personal Hyperlink for Databricks SQL Serverless, retaining knowledge non-public with Azure firewall assist for Workspace storage, defending knowledge in-use with Azure confidential computing, attaining FedRAMP Excessive Company ATO on AWS GovCloud, publishing the Databricks AI Safety Framework, and sharing particulars on our strategy to Accountable AI.
Based on the 2024 Verizon Information Breach Investigations Report, the variety of knowledge breaches has elevated by 30% since final yr. We imagine it’s essential so that you can perceive and appropriately make the most of our security measures and undertake really useful safety finest practices to mitigate knowledge breach dangers successfully.
On this weblog, we’ll clarify how one can leverage a few of our platform’s prime controls and just lately launched security measures to determine a sturdy defense-in-depth posture that protects your knowledge and AI property. We may even present an summary of our safety finest practices assets so that you can stand up and working shortly.
Defend your knowledge and AI workloads throughout the Databricks Information Intelligence Platform
The Databricks Platform offers safety guardrails to defend towards account takeover and knowledge exfiltration dangers at every entry level. Within the under picture, we define a typical lakehouse structure on Databricks with 3 surfaces to safe:
- Your purchasers, customers and functions, connecting to Databricks
- Your workloads connecting to Databricks providers (APIs)
- Your knowledge being accessed out of your Databricks workloads
Let’s now stroll via at a excessive stage a few of the prime controls—both enabled by default or out there so that you can activate—and new safety capabilities for every connection level. Our full listing of suggestions primarily based on completely different menace fashions will be present in our safety finest observe guides.
Connecting customers and functions into Databricks (1)
To guard towards access-related dangers, you need to use a number of elements for each authentication and authorization of customers and functions into Databricks. Utilizing solely passwords is insufficient as a result of their susceptibility to theft, phishing, and weak consumer administration. Actually, as of July 10, 2024, Databricks-managed passwords reached the end-of-life and are now not supported within the UI or through API authentication. Past this extra default safety, we advise you to implement the under controls:
- Authenticate through single-sign-on on the account stage for all consumer entry (AWS, SSO is routinely enabled on Azure/GCP)
- Leverage multi-factor authentication provided by your IDP to confirm all customers and functions which can be accessing Databricks (AWS, Azure, GCP)
- Allow unified login for all workspaces utilizing a single account-level SSO and configure SSO Emergency entry with MFA for streamlined and safe entry administration (AWS, Databricks integrates with built-in identification suppliers on Azure/GCP)
- Use front-end non-public hyperlink on workspaces to limit entry to trusted non-public networks (AWS, Azure, GCP)
- Configure IP entry lists on workspaces and on your account to solely permit entry from trusted community areas, similar to your company community (AWS, Azure, GCP)
Connecting your workloads to Databricks providers (2)
To stop workload impersonation, Databricks authenticates workloads with a number of credentials throughout the lifecycle of the cluster. Our suggestions and out there controls rely in your deployment structure. At a excessive stage:
- For Traditional clusters that run in your community, we suggest configuring a back-end non-public hyperlink between the compute airplane and the management airplane. Configuring the back-end non-public hyperlink ensures that your cluster can solely be authenticated over that devoted and personal channel.
- For Serverless, Databricks routinely offers a defense-in-depth safety posture on our platform utilizing a mix of application-level credentials, mTLS shopper certificates and personal hyperlinks to mitigate towards Workspace impersonation dangers.
Connecting from Databricks to your storage and knowledge sources (3)
To make sure that knowledge can solely be accessed by the fitting consumer and workload on the fitting Workspace, and that workloads can solely write to licensed storage areas, we suggest leveraging the next options:
- Utilizing Unity Catalog to control entry to knowledge: Unity Catalog offers a number of layers of safety, together with fine-grained entry controls and time-bound down-scoped credentials which can be solely accessible to trusted code by default.
- Leverage Mosaic AI Gateway: Now in Public Preview, Mosaic AI Gateway lets you monitor and management the utilization of each exterior fashions and fashions hosted on Databricks throughout your enterprise.
- Configuring entry from licensed networks: You’ll be able to configure entry insurance policies utilizing S3 bucket insurance policies on AWS, Azure storage firewall and VPC Service Controls on GCP.
- With Traditional clusters, you may lock down entry to your community through the above-listed controls.
- With Serverless, you may lock down entry to the Serverless community (AWS, Azure) or to a devoted non-public endpoint on Azure. On Azure, now you can allow the storage firewall on your Workspace storage (DBFS root) account.
- Assets exterior to Databricks, similar to exterior fashions or storage accounts, will be configured with devoted and personal connectivity. Here’s a deployment information for accessing Azure OpenAI, one in all our most requested eventualities.
- Configuring egress controls to forestall entry to unauthorized storage areas: With Traditional clusters, you may configure egress controls in your community. With SQL Serverless, Databricks doesn’t permit web entry from untrusted code similar to Python UDFs. To find out how we’re enhancing egress controls as you undertake extra Serverless merchandise, please this manner to affix our previews.
The diagram under outlines how one can configure a personal and safe surroundings for processing your knowledge as you undertake Databricks Serverless merchandise. As described above, a number of layers of safety can defend all entry to and from this surroundings.
Outline, deploy and monitor your knowledge and AI workloads with industry-leading safety finest practices
Now that now we have outlined a set of key controls out there to you, you most likely are questioning how one can shortly operationalize them for your corporation. Our Databricks Safety workforce recommends taking a “outline, deploy, and monitor” strategy utilizing the assets they’ve developed from their expertise working with a whole bunch of shoppers.
- Outline: You need to configure your Databricks surroundings by reviewing our greatest practices together with the dangers particular to your group. We have crafted complete finest observe guides for Databricks deployments on all three main clouds. These paperwork provide a guidelines of safety practices, menace fashions, and patterns distilled from our enterprise engagements.
- Deploy: Terraform templates make deploying safe Databricks workspaces simple. You’ll be able to programmatically deploy workspaces and the required cloud infrastructure utilizing the official Databricks Terraform supplier. These unified Terraform templates are preconfigured with hardened safety settings much like these utilized by our most security-conscious prospects. View our GitHub to get began on AWS, Azure, and GCP.
- Monitor: The Safety Evaluation Software (SAT) can be utilized to watch adherence to safety finest practices in Databricks workspaces on an ongoing foundation. We just lately upgraded the SAT to streamline setup and improve checks, aligning them with the Databricks AI Safety Framework (DASF) for improved protection of AI safety dangers.
Keep forward in knowledge and AI safety
The Databricks Information Intelligence Platform offers an enterprise-grade defense-in-depth strategy for shielding knowledge and AI property. For suggestions on mitigating safety dangers, please seek advice from our safety finest practices guides on your chosen cloud(s). For a summarized guidelines of controls associated to unauthorized entry, please seek advice from this doc.
We constantly improve our platform primarily based in your suggestions, evolving {industry} requirements, and rising safety threats to higher meet your wants and keep forward of potential dangers. To remain knowledgeable, bookmark our Safety and Belief weblog, head over to our YouTube channel, and go to the Databricks Safety and Belief Heart.
