Advanced Data Access Control for the Exabyte Era: Scaling with Purpose

Summary

• Purpose-Based Access Control: Just-in-Time (JIT) PBAC platform addresses data privacy and compliance challenges at enterprise scale with dynamic, purpose-driven access.
• Least Privilege at Scale: Using Unity Catalog, Delta Sharing, and Databricks Apps, the platform delivers real-time, context-aware data governance maintaining security while enabling productivity.
• Future-Proof Compliance: This video shows how advanced access control keeps data secure, engineers productive, legal and security teams satisfied in evolving compliance landscapes.

Advanced Data Access Control for the Exabyte Era: Scaling with Purpose

Talk by Arpan Ghosh, Engineering Manager, Databricks ; Shuting Zhang, software engineer, Databricks
As data-driven companies scale from small startups to global enterprises, managing secure data access becomes increasingly complex. Traditional access control models fall short at enterprise scale, where dynamic, purpose-driven access is essential. In this talk, we explore how our “Just-in-Time” Purpose-Based Access Control (PBAC) platform addresses the evolving challenges of data privacy and compliance, maintaining least privilege while ensuring productivity. Using features like Unity Catalog, Delta Sharing & Databricks Apps, the platform delivers real-time, context-aware data governance. Leveraging JIT PBAC keeps your data secure, your engineers productive, your legal & security teams happy and your organization future-proof in the ever-evolving compliance landscape.
Here’s more to explore:
Unified and open governance for data and AI: https://www.databricks.com/product/unity-catalog
See all the product announcements from Data + AI Summit: https://www.databricks.com/events/dataaisummit-2025-announcements

Chapters

• 0:00 The data access challenge
• 2:35 Understanding the pain points
• 4:41 The bottleneck of manual access
• 9:14 Introducing purpose-based access
• 12:44 Defining purpose and scope
• 17:26 Evaluating data handling
• 20:10 Scaling through a platform
• 24:10 Architecting the PBAC platform
• 30:17 Benefits of the platform
• 32:39 Platform superpowers and future

FAQs

What is purpose-based access control (PBAC)?

This video explains purpose-based access control as a dynamic, context-aware approach to data access that maintains least privilege while ensuring productivity. It addresses traditional access models' limitations at enterprise scale by enabling real-time, context-aware governance through features like Just-in-Time access provisioning.

How does Just-in-Time (JIT) access work in this video?

This video describes JIT as provisioning temporary access when needed rather than maintaining standing permissions. Combined with purpose-based definitions, JIT access reduces risk by limiting exposure windows and automating provisioning/deprovisioning through Unity Catalog and Databricks' governance features.

best platforms for data access control?

This video demonstrates how the Databricks Data and AI platform combines Unity Catalog, Delta Sharing, and Databricks Apps to deliver purpose-based access control at enterprise scale. It addresses the core challenge: as organizations grow from startups to enterprises handling data globally, access must scale from trust-based to policy-driven. The platform automates approval workflows and policy enforcement while maintaining least privilege and audit compliance.

how data platform teams reduce time spent on access requests?

This video reveals how Just-in-Time access provisioning eliminates the weeks-long approval bottleneck. Instead of manual spreadsheets and multi-stage approvals, purpose-based policies define access through context: who needs it, what purpose, and what scope. The Databricks Data and AI platform automates the entire workflow from request to provisioning to deprovisioning, freeing teams to focus on analytics rather than access management.

How do I implement attribute-based access control (ABAC) for data and AI?

This video shows how purpose-based access control implements ABAC principles by defining access through attributes (purpose, scope, data handling) rather than static roles. Using Unity Catalog, the Databricks Data and AI platform enables dynamic, context-aware policies that enforce least privilege at scale. The architecture supports both startup simplicity and enterprise compliance requirements without requiring specialized teams.

Full transcript

0:00 The data access challenge

Good evening and welcome to the last talk standing between you and happy hour. We're going to dive into advanced access control for the exabyte era. And I promise we're going to make it worth your while if this clicker starts working. Ah, there we go. All right. So, before we break that down, I just have to jump on a real quick call with my legal team. Feel free to listen in. Don't worry, nothing's wrong. It's just going to be a minute and we'll be back on track.
Hey, legal team. Hey, CEO. So, this new data and AI regulation, it's going into effect in what, 3 months? Actually, in two months. Plenty of time. We've got logs, access control, red tape. We're in good shape, right? We have policy defined, but no enforcement though. Okay, not a big deal. Maybe a spreadsheet where we track who looked at what and when. Uh, you know, a spreadsheet won't scale. You know, we are looking at a quarter-long effort project involving every team that touches the data.
Hang on. We're supposed to be launching our new AI assistant this quarter. If I pull the team into this, it's going to ruin the entire year's road map. There must be something we can do that's simple. We trust our people after all. You know, we are no longer a 20 person startup anymore. We are a enterprise handling data at global scale. Trust isn't just a strategy and the risk is very real. You're right. What do I do?
Do I call the board? Do I hire consultants? Do I cancel the launch? Please tell me there's a plan. Don't worry. Looks like we are in the right room. I heard those folks speaking at a data and AI summit have spent years scaling platform for data access. So, let's hear how they prepare for the next regulation change in data. Phew, I knew going to the summit this year would pay off. So shooting and I are not really a CEO and legal and there's a bit of exaggeration in that example but only a tiny bit.
We know this kind of regulation is right around the corner but none of us really know what it truly entails. But today we're here to tell you what you can do about it. Hi, I'm an engineering leader in the data platform org at data bicks. I started my career as a one-man data platform team at Data Bricks's first customer. A tiny startup which had no access control, no guardrails, just raw data and a lot of hope. Since then, I've spent the last decade learning how to build scalable data access control.

2:35 Understanding the pain points

And today, I'm going to talk to you about what that really takes. Hi, I'm shooting. I'm the technical lead on the data platform team at data bricks. I began my career at data bricks building largecale multigo data pipelines. Ever since then now I'm moving on to the other side of the data equation to build a scalable platform for secure data access. So having been on the both sides today you will hear from us how we scale data access at data bricks.
So now you know who we are. We'd like to get to know you a little bit better. So, quick poll. How many folks in the room have accessed data they were not supposed to? Yeah, quite a lot. Don't worry, there's no cameras in the room. And uh also, I mean, if we're being honest, half of you probably did it without even knowing. Here's another question. How many of you have had trouble accessing data you were supposed to have access to?
Yeah, just as I guessed. Pretty much the entire room. Not surprising at all. Maybe it was a simple request like this one that turned into a weeksl long project, multiple approval cycles, and you know, just getting a small mountain of paperwork together. So, let's take a step back. People are accessing data they shouldn't, which won't make legal insecurity very happy. And data consumers are having trouble getting access to the data they should have access to.
To understand the problem and why no one seems happy, we have to go back to the beginning. Most companies start here. Tight-knit team, shared mission, full trust. Data was free flowing, access was open, and everything moved fast. If you needed data, you got it. No approvals, no paperwork. But then came hyperrowth, and everything changed. The company took off. Teams spread across the globe. Data sprawled across multiple clouds and compliance zones.

4:41 The bottleneck of manual access

In the meanwhile, innovation didn't stop. Every product, every feature, every experiment required data. And naturally, the access requests skyrocketed. This is also the time when legal insecurity joined the chat, but a little too late. Right off the bat, they were behind the eightball, scrambling to do to do two jobs at the same time, auditing the past and protecting the future. They needed to make sure that everything the company had done till this point was compliant, but at the same time field a flood of fresh data access requests.
Every request was urgent. Every request lacked the appropriate context and the backlog kept growing. In this chaos, two things end up happening. Most requests get bogged down in this maze of privacy, security, and compliance hurdles leading to frustrated consumers. And some requests slip through unnoticed with busy producers rubber stamping access that is way too broad. So the company's productivity is dipping and at the same time its risk is exploding.
This is why no one's happy, especially legal and security. It's clear something has to change. To really bring this to life, let's listen in on another call where a data consumer is trying to navigate exactly those hurdles. Hey legal, I'm an AI engineer. I just need access to my logs to debug my AI agent. Of course, we just have eight simple questions to ask you. Whoa, that's a lot, but I'll try my best. Number one, who needs the data?
Uh, me. My manager said it was okay. Question number two, who else will be working on this? Well, probably my whole team. Oh, then there's our partner team. And wait, there's those contractors in India. Next question. Which part of the data do you need access to? Honestly, I'm not sure. I was hoping to look at the data and find out. Does the data contain any PII? Maybe. I didn't really check. Is that bad? We're almost there.
Four more questions. How long do you need access for? Just a sprint, I think. Oh, wait. Maybe next quarter. You know what? Why don't you give me indefinite access? Don't worry. I know what I'm doing. That's pretty bad. Okay, next question. Can you make sure any derived data set are tracked and deleted when you are done with the data? Derived data? Is that like a pivot table? No, I'm talking about data lineage. Never mind.
Just tell me where the data is stored right now and will you move it out of the region? We run our validation locally on our laptops. Is that out of region? Yes, it does. Okay, promise this is the last question. Will you be able to provide a full audit trail who accessed what and when? Not really. We can probably stick some print statements in our code, but that's about it. But hang on, if we have to build all this, it's going to push out our timeline by months.
All I wanted to do was access the logs. Yeah, I understand your frustration, but unfortunately, we have to deny your access request. Come back to us when you figure out all the details and we can re-evaluate. Boy, my manager is not going to be happy with this. So, what went wrong here? Uh, I think we were speaking completely different languages. As an AI engineer, I'm really good at building chat bots, but I have no idea what any of this stuff really means.
And that's what ends up happening. Everyone at the company is doing their part to make the company better. Data consumers want to move fast. Data producers want to land data reliably and quickly and get out of the way. And legal and security are there to protect the company. However, there's no common language between them. There's no shared framework to discuss that kind of an access request. Remember legal's endless follow-up questions about my data access request?

9:14 Introducing purpose-based access

They're coming from a world of regulation and contracts that go deep into how data is supposed to use. Risk is not just about who is accessing the data. It's very much about how that data gets used. They're trying to understand the purpose behind the request. It's one core question. Why do you need access to this data? Purpose is that missing link. It's the shared language that turns the intent of a request into policy and further down the line into enforcement and handling.
Once legal knows the why, everything else falls into place. Data access becomes about smoothly enabling a goal, not just opening a door. This is a huge shift in mindset and to show how powerful that is, let's see it in action in the real world. Now, a lot of you flew in to the summit from various parts of the country. All you needed was your ID. You got on your flight and that was it. You were good to go. Everything revolved around your identity.
But for those of you joining us from abroad, you know that wasn't sufficient. You needed a visa and that visa dictated how long you could stay and what you could do while you were here. You also ran into customs who were very interested in what you're bringing in and bringing out of the country. Because it's not just about who you are, it's about the purpose of your visit. That's the very first question you're asked.
That's exactly what purpose-based access control brings to the world of data. It's not just who, it's why, what, and how. Without a visa, you'll just be waving your passport as your flight takes off. And without purpose, your access control model is flying blind. This isn't something you can script around or just bolt on. It's a fundamentally different model, one built for purpose, not just permission. This is peback.
It completely flips the script. You're no longer granting access to data. You're granting access to a goal. Tightly scoped and transparently governed. This is how you achieve least privilege at the enterprise scale across global teams, exabytes of data, and real production systems. So, how did we build this? and more importantly how did we make it scale to walk you through all of that let me welcome back my partner in purpose shooting the technical lead behind the peback platform thanks so let's start with the question what is purpose-based access control so in a traditional access control model we focus on whether a user should have access to a data but in purpose-based access control we flipped the question so instead Instead of asking who should have access to what, we ask why.

12:44 Defining purpose and scope

In peback, every data access decision is driven by the intent of the request. So the purpose of the request becomes the key. It groups the people and data together based on intent. In this model, for each purpose, we will define who is eligible for the data access. what data is appropriate for this purpose and how the data access should be scoped and handled. When we tie a purpose to every data access request, we are able to ensure the data is used and governed consistently.
Purpose becomes the way that we evaluate all data access. So to make the evaluation of purpose-based access control scalable and enforceable we rely on a list of canonical purposes. This list covers the most common reason why people need to access data today. So it has everything from operational analytical customer support marketing R&D security and audit purpose. So let's look at two examples here. So for operational purpose the data is used to keep the system running or to triage any production issues.
So the people in this purpose are typically engineer or sur and the data they need to access to is usually system logs or user event log that's scoped to a specific incident. And let's look at analytical purpose. In this purpose, the people are usually product managers or engineering owners and the data they need are typically product locks or anonymized the user event lock. Now, some of you might notice there could be an overlap on the data across different purposes and you might be wondering if I already get access to the data for one purpose, can I reuse this data for something else?
The answer is no. In peback, a user should only wear one purpose hat at a time. Let's walk through an example to see why that matters. So suppose you are an engineer in investigating why a customer didn't get a password reset email. So to fulfill this operational purpose, we grant you the access to customer email address. And while debugging you realize this customer hasn't logged into our system for more than 30 days and you're wondering can I reuse this email address for our user re-engagement campaign the answer is no because using that email address for outreach this is a marketing purpose and then you this is not allowed under the original operational purpose.
So in feedback access isn't just about what you can see. It's also about what you are allowed to do with it. Every purpose must be reviewed and approved separately. The user can now toggle between operational purpose and marketing purpose even for the same data without undergoing a separate evaluation. So now we have a good understanding of what peback is. Let's revisit the original data access request. So our AI engineer asked, can I get access to the system logs to troubleshoot my AI agent?
So based on this question, we can tell this is the operational purpose. So reframe this question under the peback model. Can we grant engineer access to the system logs of the service they own to troubleshoot? Yes, because this is aligned with the people and data grouping that we defined for the operational purpose. So we are good to approve this data access. However, the evaluation of feedback policy doesn't just stop here.
We still need to figure out some details about the data access here. So when it comes to evaluating the data handling we consider three dimensions data time and place. In a data dimension we look at the following. What data fields are required for this purpose? Do we require any reduction on a data field when accessing data for this purpose? And also can we apply any additional data filters to narrow down the scope of the data.

17:26 Evaluating data handling

By scoping the data pre precisely we ensure that the minimum data are accessed. So in the time dimension we look at max access period. This determines how long you can have access to the data for. The more sensitive the data is, the shorter the allowable access period is. We also look at retention. This determines how long the data should be kept. This retention policy should also apply on any derivative data the user generate while using the data.
So the policy in this time dimension ensure that the access to the data is time bound and this can help minimize the risk of data leak. So the last dimension is place where data is stored isn't always where the data should be accessed from. So we consider the following. Is there any geo or region boundary requirement when accessing this data for a purpose and also which workspace is appropriate for data to be accessed from.
So these three dimensions data time and place work together to enforce the least privilege. Before peback access was often granted too broad. We either give user more data than they need or they have access to the data for too long. With feedback, we give user the exact amount of data they need for the exact amount of time they need. No more and no less. Every access request is tied to its specific purpose. And we know exactly how to narrow down the data scope, the time scope while still letting user fulfill their purpose.
So this model is great, but how can we encrypt this model into our day-to-day workflow? And hopefully the legal and security doesn't have to review every single data access request. And yes, this is how we can scale. We free up legal and security from having to review every individual data access request when introducing peback when introducing data taxonomy on peback. So first we will define purpose classification. So for each canonical purpose here we define the people data and scope of this purpose.

20:10 Scaling through a platform

For example, operational purpose engineers are allowed to access system logs of the service they own. Purpose classification streamline the review of the people and data grouping. Then for each purpose, we apply data taxonomy on the column field to automate a review of data handling details. So each label here has a definition of the details we mentioned in the data time and place dimensions. By grouping individual requests under purpose classification and applying the data taxonomy we scale up legal.
Legal only need to jump in when we introduce a new purpose or when we need to update the existing payback policy. So we've seen how the peback model can scale up legal and security. But what about producer and consumer? Can we help them scale too? To scale up producer and consumer, we introduce data discovery. So this is a metadatriven approach that bridges the consumer and producer. So when a consumer asks can I get access to system logs to debug service X in region west US data discovery is the component that allow us to locate exactly which data set is needed without asking the producer.
Each table will carry us structured metadata like this including the purpose, the region, service and owner. As the company grows, the data volume doesn't just double, it explodes. More team will produce more data sets in more region. And on that scale, manual coordination simply doesn't work. And the data discovery becomes the key enabler here to scale up producer and consumer with the scalability for all parties here producer consumer and legal.
This is where we shift from PAG as a model to PAG as a platform. On this platform when a producer publishes a data they also need to register this data in our data registry they will they will provide a data taxonomy on the column level and also metadata of this data set in a data discovery. legal only need to step in when there is a new purpose introduced or when we need to update existing purpose classification or the payback policy and when a consumer need access to data the platform enables a selfserve experience for this consumer the purpose classification will determine whether this access is allowed the data discovery will locate exactly where the data is the data taxonomy will define all the details we need to know when it comes to handling this data access.
So the platform not just uh not only streamline data governance but also enable a secure purpose aligned data access journey throughout this whole process from producer publishing the data to policy maker defining the controls and all the way to consumer using the data. And today you can use datab bricks product to build such a platform. And let's take a look at how we built payback platform at datab bricks using datab bricks.

24:10 Architecting the PBAC platform

So we will walk through the architecture of our platform through the user journey of a consumer. So the process begins when a consumer submit a data access request on a UI of a datab bricks app. Data bricks app offer great data governance and security features such as authentication manage service principle and good integration with the unity catalog. So once the request is submitted the app will identify the user from the building authentication and combined with the data and a declare purpose provided by this user.
The app then will check if the people and data grouping is aligned with any pre-proved purposes. So we will issue a query to the purpose classification data table via the SQL warehouse. If the if the validation pass uh we will move on to the next step. So far, our team has productionized several frequently requested data under the operational purpose and we plan to cover more purposes by the end of this year. So after the purpose validation, the app queries the data discovery metadata via SQL warehouse to locate exact table name the consumer is asking and along with other information such as the data filters or the format of the output.
We then package this whole information into a data snapshot task and add it to the message queue. There will be a task distributor that's consuming the message from the message true on the other side. Based on the storage location and other metadata of this task, we distribute the actual data preparation job in one of the data bricks workspaces. Then the job will perform reduction or data filtering according to the feedback policy defined on the data tonomy.
So the final output here is a purpose scoped snapshot containing only the minimum data necessary. So this is how we achieve the list privilege not just in access but also in volume. The original data set may be exabytes in volume but what a consumer receive in the end is probably just a few gigabytes. So this is a billion times smaller in volume tailored to their purpose. Then the platform creates adjusting time access group.
This group will be used for the consumer to access the data later. Group access is easier to manage when it comes to adding new user as a collaborator or revoking the whole access when the max access period expires. The last step here is the data placement. The platform will check the payback policy and ensure the data are placed in appropriate workspace. We use two mechanism to make the data available in a separate workspace.
One is data share. This is often used when the source workspace and a target workspace are using two uh different UC mana stores. Another mechanism is the catalog binding. This is often used when the source workspace and the target workspace share the same UC meta store. Finally, we notify the user you're ready your data is ready to use in this workspace. This is a safe place for users to explore. In this workspace, users can only see the data scoped and prepared by the Pback platform.
The access to the data is also time bound. How consumer use the data is also monitored by the platform. Any data generated by the consumer is tracked by the platform thanks to the rich lineage information provided by the UC. So after the consumer are done using the data, the platform will also trace any downstream data they generated and apply the retention policy accordingly. In this architecture besides the message coupled using the datab bricks product which unlocks another key benefit auditability.
Since audit log are built in to the datab bricks product we maintain the audit trail for free covering the entire life cycle of every data access. So the platform scales up all parties. Consumers can just get a data within minutes without talking to legal or producers. Each producer only need to register the data maybe monthly when they publish a new data set. And legal only need to step in when there is a new purpose introduced or when we need to update the existing feedback policy.
So are legal and security happy? Yes, we achieve the list privilege on this payback platform. We give user the exact amount of data they need for the exact amount of time they need. No more and no less. Thanks to the audit log that's built into the data bricks product, we also get a good coverage of auditability for free. It covers the whole life cycle of every data access, who access what and when. The platform is also designed to enforce the data security based on the payback policy and we also provide end to end monitoring to make sure these enforcements are in good place.

30:17 Benefits of the platform

So what about our consumers? Consumers are also happy about this new user experience. There's no more back and forth conversation talking to producer and legal. We simply go to a simp UI submit a data access request and the platform will do the policy track access request validation and data preparation for us. Within a few minutes we receive a notification that the data is ready to use. What's also nice about this platform is my job is done the moment I'm done using the data because that I know the platform will clean up all the downstream data I created when I'm using the data.
So who else is happy with our platform? Our CFO is happy when we achieve the list privilege with this platform we unlock another key benefit on cost. So list privilege means we give consumer the minimum data to read. So what happens is when we have broader access to the data, we also tend to scan more data than we need. With the peback platform, we are able to reduce the cost by only giving the consumer minimum data to read.
Besides the costsaving, another benefit this platform gave us is agility in regulation change. Remember the story we shared in the beginning when there is a new change in a data regulation teams don't have to all jump in adapting this new policy on all the data with peback platform all we need to do is to change the existing feedback policy and it will automatically apply to all data registered on our platform. So yeah, the payback platform completely transformed how we handle data access, making it safer, faster, and cheaper.
It enforces list privilege by design, scales policy enforcement without friction, and let users focus on their work. From security and compliance to developer productivity and cost control, the feedback platform is the foundation that keeps the entire organization moving forward with confidence. But it turns out when you build a strong platform, great things start to happen. And now let's welcome. He's going to share with us how BBback unlock unexpected superpowers.

32:39 Platform superpowers and future

Thank you, shooting. That was a very nice walk through. Um, I've spent most of my career building platforms, data platforms, of course, notification platforms, billing platforms. At one point, I even built a platform that lets your car control your living room lights. And now I build access control platforms. The goals have changed. The scale has definitely grown, but I keep coming back to the one thing over and over again.
Great platforms tend to deliver three big superpowers. The first, great platforms optimize for both productivity and quality. Most systems you encounter will force you to make a trade-off. Ship fast or do it right. Move fast or stay safe. But a good platform doesn't make you choose. It changes the equation entirely. Picture this. A support ticket just came in from a customer. In the old world, a support engineer would waste invaluable hours just trying to get the data they need to triage and fix that particular issue, finding out which logs to use, jumping through approval hurdles, and just waiting for those to come back.
But with a peback platform, that same ticket can trigger a secure automated workflow. The platform goes off and fetches the correct logs. It runs AI based triage and posts a diagnostic summary back into the ticket. By the time a support engineer picks it up, the hard part's done. They're fixing, not waiting. That's productivity and quality built in. The second superpower, great platforms have network effects. Once they're in place, everyone wants to build on top of them.
And that's exactly what we saw when we rolled out the Peback platform. We had ended up providing a secure, well-defined API for the rest of the company to access data. and all the other teams couldn't wait to ditch the manual data access workflows that they were so reliant on in the past. Internal tools, apps, dashboards, even AI based assistance all started to build on the PBAC platform to securely fetch purposebound data on demand.
Legal and security actually became some of our loudest champions. Now when someone asks for data they don't revaluate that request they just redirect. Have you considered using the payback platform? And the third superpower leverage. The kind of leverage which makes a small team a force multiplier for the entire company. Remember that conversation we overheard in the beginning? our optimistic CEO a little too confident about my company's preparation for this upcoming data and AI regulation and legal quietly panicking.
In most companies, a shift like that is going to trigger months of chaos. Rewriting workflows, retraining employees, and handholding every consumer of sensitive data. But with a peback platform, it's a totally different story. Because the system is purposefirsted and decoupled, only two things need to happen. Legal and security need to implement the new policy, and the Pback team needs to implement the enforcement. That's it.
Thousands of producers and consumers just keep doing what they're doing, probably even unaware that something like this actually got rolled out and applied. This is what leverage really looks like. Now, some of you might be feeling a little bit of deja vu. That's because we've seen this movie play out before. I'm sure many of you in the room live through GDPR back in 2018. You had a beautiful road map for your company, a perfectly built sand castle, and then GDPR rolls in like a tsunami and flattens most of it.
I bet you wish you had a payback platform back then. So, here's the big takeaway. A Pback platform doesn't just make data access safer, faster, and cheaper. It makes your organization futureproof. Whether it's the next big AI use case or the next big wave of regulation or the next big data crisis, you're ready, not scrambling. So if your 2025 road map is still made of sand, I highly recommend investing in a platform.
And with data bricks, you already have the tools at your disposal to build a peback platform and achieve least privilege at enterprise scale. We just spoke about how good platforms provide leverage. Here's a perfect example. That's exactly what the data bricks platform did for Peback. It allowed us to roll out our first version of purpose-based access control in three months with a small four-person team. Thanks for sticking with us.
And now here's your reward for staying till the end. We've talked access control and scaling with grace. We've met up the future where risk knows its place. From wild west chaos to purposebound rules where locks fetch themselves and AI got tools. We hope you feel ready, empowered and bright to tackle your data access and sleep well at night. Now go grab that drink. Your access is cleared.

Learn more about the Databricks Data and AI platform.