At Databricks, we all know that information is one among your most beneficial belongings. Our product and safety groups work collectively to ship an enterprise-grade Information Intelligence Platform that lets you defend towards safety dangers and meet your compliance obligations. Over the previous 12 months, we’re proud to have delivered new capabilities and assets comparable to securing information entry with Azure Personal Hyperlink for Databricks SQL Serverless, retaining information personal with Azure firewall help for Workspace storage, defending information 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 method to Accountable AI.
Based on the 2024 Verizon Information Breach Investigations Report, the variety of information breaches has elevated by 30% since final 12 months. We imagine it’s essential so that you can perceive and appropriately make the most of our security measures and undertake really useful safety greatest practices to mitigate information breach dangers successfully.
On this weblog, we’ll clarify how one can leverage a few of our platform’s high controls and just lately launched security measures to ascertain a strong defense-in-depth posture that protects your information and AI belongings. We may even present an outline of our safety greatest practices assets so that you can stand up and operating rapidly.
Defend your information and AI workloads throughout the Databricks Information Intelligence Platform
The Databricks Platform gives safety guardrails to defend towards account takeover and information exfiltration dangers at every entry level. Within the under picture, we define a typical lakehouse structure on Databricks with 3 surfaces to safe:
- Your shoppers, customers and purposes, connecting to Databricks
- Your workloads connecting to Databricks companies (APIs)
- Your information being accessed out of your Databricks workloads
Let’s now stroll by means of at a excessive degree among the high controls—both enabled by default or accessible 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 might be present in our safety greatest follow guides.
Connecting customers and purposes 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 purposes into Databricks. Utilizing solely passwords is insufficient because of their susceptibility to theft, phishing, and weak person 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 degree for all person entry (AWS, SSO is routinely enabled on Azure/GCP)
- Leverage multi-factor authentication supplied by your IDP to confirm all customers and purposes which are 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 id suppliers on Azure/GCP)
- Use front-end personal hyperlink on workspaces to limit entry to trusted personal networks (AWS, Azure, GCP)
- Configure IP entry lists on workspaces and to your account to solely enable entry from trusted community places, comparable to your company community (AWS, Azure, GCP)
Connecting your workloads to Databricks companies (2)
To stop workload impersonation, Databricks authenticates workloads with a number of credentials throughout the lifecycle of the cluster. Our suggestions and accessible controls rely in your deployment structure. At a excessive degree:
- For Traditional clusters that run in your community, we suggest configuring a back-end personal hyperlink between the compute airplane and the management airplane. Configuring the back-end personal hyperlink ensures that your cluster can solely be authenticated over that devoted and personal channel.
- For Serverless, Databricks routinely gives a defense-in-depth safety posture on our platform utilizing a mix of application-level credentials, mTLS consumer certificates and personal hyperlinks to mitigate towards Workspace impersonation dangers.
Connecting from Databricks to your storage and information sources (3)
To make sure that information can solely be accessed by the fitting person and workload on the fitting Workspace, and that workloads can solely write to approved storage places, we suggest leveraging the next options:
- Utilizing Unity Catalog to control entry to information: Unity Catalog gives a number of layers of safety, together with fine-grained entry controls and time-bound down-scoped credentials which are solely accessible to trusted code by default.
- Leverage Mosaic AI Gateway: Now in Public Preview, Mosaic AI Gateway permits you to monitor and management the utilization of each exterior fashions and fashions hosted on Databricks throughout your enterprise.
- Configuring entry from approved networks: You possibly can 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 personal endpoint on Azure. On Azure, now you can allow the storage firewall to your Workspace storage (DBFS root) account.
- Sources exterior to Databricks, comparable to exterior fashions or storage accounts, might be configured with devoted and personal connectivity. Here’s a deployment information for accessing Azure OpenAI, one among our most requested situations.
- Configuring egress controls to stop entry to unauthorized storage places: With Traditional clusters, you may configure egress controls in your community. With SQL Serverless, Databricks doesn’t enable web entry from untrusted code comparable to Python UDFs. To learn the way we’re enhancing egress controls as you undertake extra Serverless merchandise, please this type to hitch our previews.
The diagram under outlines how one can configure a personal and safe setting for processing your information as you undertake Databricks Serverless merchandise. As described above, a number of layers of safety can defend all entry to and from this setting.
Outline, deploy and monitor your information and AI workloads with industry-leading safety greatest practices
Now that now we have outlined a set of key controls accessible to you, you most likely are questioning how one can rapidly operationalize them for your small business. Our Databricks Safety workforce recommends taking a “outline, deploy, and monitor” method utilizing the assets they’ve developed from their expertise working with a whole lot of consumers.
- Outline: You must configure your Databricks setting by reviewing our greatest practices together with the dangers particular to your group. We have crafted complete greatest follow 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 straightforward. You possibly can programmatically deploy workspaces and the required cloud infrastructure utilizing the official Databricks Terraform supplier. These unified Terraform templates are preconfigured with hardened safety settings just 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 greatest 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 information and AI safety
The Databricks Information Intelligence Platform gives an enterprise-grade defense-in-depth method for safeguarding information and AI belongings. For suggestions on mitigating safety dangers, please consult with our safety greatest practices guides to your chosen cloud(s). For a summarized guidelines of controls associated to unauthorized entry, please consult with 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.
