Salesforce, Inc. (CRM) Earnings Call Transcript & Summary

Hello. Hello, everyone, and welcome to today's session, How to Manage and Protect Data with Privacy Center and Data Mask. And thank you all for joining us today. My name is Moya and I'm the Webinar Manager on the Global Field Marketing team here at Salesforce. And before we begin, I would like to cover a few quick housekeeping notes about our webinar platform. So today's webinar will be available on demand after we wrap up and is accessible through the URL you are on now. You will also receive a link to watch today's webinar on demand, so please keep an eye out in your e-mail for the link to today's recording. Please note the slides will advance automatically throughout the presentation. If you need to enlarge a side area for any reason at all, please click the Enlarge Slide button located in the right-hand corner of your presentation window. If you need any technical assistance, please click the Help widget located in the bottom-left corner of your console. We've also added some resources which are available to you through the Resources window that's on the right of the slide area. So there you can find any additional related content, including white papers, demos, e-books, data sheets and so much more for you to check out in addition to today's presentation. We also encourage you to submit questions at any time throughout the presentation using the Submit a Question widget on your console. So feel free to start adding your questions now, and we will be sure to answer as many as we can at the end of today's presentation. But please note that if we do not get a chance to answer your question today, we will be sure to follow up with you after the webinar ends. Last but not least, we would love to know what you think of today's presentation by sharing your feedback with us via our webinar survey that's available to you right now on your audience console. And we also welcome you to share your excitement and what you like best about today's presentation in the moment by using the emoji reactions on your screen. So if you take a look at the bottom of your screen right now, you'll see emoji reaction space where you can heart, smiley face, celebrate, thumbs-up all of your areas that you love the most about today's presentation. All right. So with that, I'm going to hand it over to Amanda to kick us off. Amanda, over to you.

Thanks, Moya. Good morning, good afternoon, good evening, wherever you are. Welcome to today's session on how to minimize deletes, anonymize and protect data, so thank you. Before we jump in, we wanted to thank you for being our customers, for being our partners. Your success really is the priority for us. And quick heads-up, today, we're going to give you a sneak peek into our road map. So a friendly reminder, Salesforce is a publicly traded company, so please base all purchasing decisions based on products and services that are available in the market today. As Moya said, my name is Amanda. I'm a product marketer here at Salesforce. Soon, I will pass it over to Poorva and then to Bryce, some of the best product managers in the business. They will give you a live demo, and they will also give you a preview of our future innovation. So let's get started. First things first, data really is the lifeblood of AI, and organizations are storing more data than ever before, and collecting this data can be a good thing, right? It can lead you to beat out competitors or to create a new, revolutionary product. But a lot of this information can be deemed sensitive, especially from a government regulatory standpoint. So data is an asset, but it can also be a liability if it's not managed in a trustworthy way. Privacy compliance is a legal requirement but also a business imperative. It's more than just checking a box. It's about companies creating real value in exchange for customer data and providing transparency and control with easy-to-use tools. Not only does having a robust data privacy program build trust with your customers, but it also reduces certain costs like fines from the privacy regulations, which are often country-specific and change every year. It feels like every year, right? So for businesses with a global footprint or ones that want to have a global footprint, it can be hard to keep up. I listed here a few of the key regulations here to watch out for. So I'm sure most of you are familiar with CCPA, California, where I'm from. This is currently the higher -- highest standard of data protection in the U.S. We also have GDPR in Europe, and we have FINRA and PCI for those financial institutions. So these regulations require companies to really manage data in a variety of ways. So this can be deleting data regularly and at a customer's request or masking the data to reduce the severity of a potential data risk. So with this in mind, today, we are going to focus on 3 areas, so one, minimizing data in production and also anonymizing and protecting data both in production and in sandboxes. So to talk about the first set of tools to help you simplify data management, I will turn it over to Poorva to discuss Privacy Center. Take it away, Poorva.

Awesome. Thank you so much, Amanda, and hi, everyone. Thank you once again for joining this call. I am Poorva, the Product Manager Privacy Center. Now, Privacy Center is a tool that helps you simplify data privacy needs by optimizing your data and your data storage. It also helps with your automating the process of executing any data subject requests, like to be forgotten [ or Visa ], thereby helping you to reduce your compliance cost. And in the effort of doing all of this, it actually helps you to maintain that relationship of trust with your consumer. Now, Privacy Center has a lot of tools within the product itself, because of which it makes it very seamless and easy for you to actually meet all of these expectations. There are a few use cases where Privacy Center will be super helpful for you. I'll walk you through some of them and then we'll jump real quick demo. So if you have requirements around data minimization, what that means is if you, let's say, have a subset of data in your word, which is taking up a lot of space, but it's not something that you need any more, and you would wish to delete that data, then Privacy Center can help you achieve that use case requirements. Moreover, if you don't want to delete that subset of data, you just have a few fields which could have some PI information of your consumers and you don't want to keep that information anymore with you, then you can select -- and if you want to just delete like some of the sale values or even anonymize some of the PI information to protect your consumers' information, Privacy Center can help you there as well. Moreover, if you have a subset of data which is inactive, but you cannot read that because of some compliance reasons or audits, and just want to keep that data somewhere else so that you can save storage space and costs in the Salesforce world and maybe just delete that data -- keep it outside of Salesforce and delete it after a few years when your compliance needs have been met, then you can definitely use Privacy Center to help achieve that as well. In the data subject request area, if you have to cater to a lot of right to be forgotten request, a right to portability request or as we also call it, data subject access rights request, and you want to automate that process to reduce the manual time that's needed to process every request and the cost, Privacy Center can help you in that area as well. And lastly, if you have requirements around streamlining your consent, right, from creating forms to capture consent to actually tracking it and storing it at a very granular level in Salesforce, then Privacy Center's Preference Manager can help you streamline that entire process. For today's session, I'll be focusing more on the data minimization/optimization side of things. In the Winter '24 release, we came up with a lot of new enhancements with Privacy Center which are specifically around the data minimization area. So let's get started on those. So with the Privacy Center tool, you can create policies where you determine the data from which object should be deleted. It helps you set filter criteria to figure out the data which you don't need anymore. It will also allow you to delete data from any related objects like field history tracking or field audit tray depending on whichever add-on license you have for those 2 capabilities. Now, by default, Salesforce now can do a soft delete, which means it deletes your data and stores that in the Recycle Bin for a few days. But if you wish to delete the data permanently, do a hard delete of the data, which means not store it in Recycle Bin and just get rid of it immediately, then Privacy Center allows that capability as well. And one of my favorite enhancements that we've added is that you can also delete any of the related files and attachments for all the records which are already going to deleted from Salesforce using Privacy Center. This tool also provides you the option where you can choose that, if you don't want to delete the entire record, but you only want to transform or only want to delete data from a few fields for that object, then you can also pick and choose which are the fields from which you want data to be transformed. So you have the option of either changing the value to a static value. You are using some randomly generated characters so that original value is replaced, so that -- or just delete the field value. Again, with this option as well, you can still go ahead and delete data from any of the related field history objects, a field under trays and also delete the related files and attachments. Without further ado, I will now just quickly show you a demo to see how easy it is to actually just create and execute this policy. I'll go ahead and quickly share my screen here, and my screen is loaded. So this is the new landing page for the Privacy Center app, which has gone live since mid-'24 release. On this page, you can see a lot of tiles/tabs which represent all the different capabilities that I spoke about a few minutes ago. For data minimization, you can click on the data management policies to start creating your policies. Once I click on that, you can see a list of all the policies that you've created and their status and other information about like who created it and when was it last modified. To create a new policy, you can click "new" on the top right here. Now, the data management policy is built on this new engine that we call the Privacy Policy Engine. The right to be forgotten capability is also built on top of that, so both data management and right to be forgotten is a sustained engine and has a very similar UI with minor nuances. For example, if you want to do bulk deletion of data, then you can use data management policies, whereas if you want to cater to a right to be forgotten request, which means just deleting one record or starting from one parent record and then deleting child for that one particular consumer, then you can create right to be forgotten policies. So for this demo, I'll go ahead and create a data management policy. So once I click next -- so for this example, I'll just show you on how you can delete or minimize data use in contact. So I'll just give this name here. Once I do that, it creates a record for the policy. On the right here, I can click on "add object" to start adding objects to this policy. On the left here, you can see a list of all standard and custom objects that's available to that user who's actually creating this policy. I'm an admin user, so I can see almost all the standard and custom objects. I'm going to choose quick find, and I'm going to find [ Gonback ] here and select next. You can actually give a description of what you're trying to do with this contact and with this object. Let me select next. On this page, you can set your filter criteria. Now, if you have multiple filter criteria, for example, delete contacts which are older than 10 years, which have not had any activity since the last 5 years. So if you have these multiple criteria, then you can choose this drop-down and select what kind of relation you want between those criteria. So do you want an "and" condition between them, an "or" condition, or if you have a custom and/or logic that you want to use, then you can also choose that using this drop-down. Actually, if you want to delete data from all the records for that option, then we also provide you that option. Depending on what option you choose here, once you click on add condition, it will show you a list of all the fields, again, standard and custom, for that particular object. And then, depending on the [ processing ] time, it will also update the operator and the criteria value on what you would like to change and how you would like to keep that. So just for this demo, I'm just selecting a Boolean value. So as you can see, the operator just shows whether it's equal or not equal, and then the criteria gets populated to either true or false, and you can select one of those. If you have some complex criteria, like, for example, if you want to delete contacts which does not have any related cases, then, in such case -- such scenarios, your filter criteria is also dependent on the child object, which is, in this case, the case object. So you can click on "add cross object query" and then set the filter criteria on the child object. By doing that, it will help you filter out all the contacts which does not have cases associated and only make the policy act on those contact records only. The other way around, if you have any parent conditions that could affect the way of filtering [ a condition ] for your current object, then you can click on "add parent condition" and set the filter criteria on the parent account. So again, an example here would be let's say you want to delete contacts which have accounts which have not been updated since the last 10 years, so again, the criteria here is also going up to the account object. So you can click on "add parent condition" and set the criteria on account so that way it will only help you filter out the contacts which does not have any accounts, which don't meet that criteria. So I'm just going to go ahead. So you can set your filter criteria here and click next. And then on this page of the wizard, you can choose what action you want to take on your data. If you choose delete, this will delete the entire record for all those records that you filtered out. Again, using these check boxes, you can delete data from field history object, field audit tray or do a hard delete. When you scroll down to the bottom of this page, you will also see an option if you want to delete or keep the related files and attachments. And then if you choose the Mask option over here, then, again, you can see all the fields for that particular object. And then you can choose what actually you want to take on all of those fields, whether you want to just keep the value as is or anonymize it or delete that particular field value and thus protect your consumer PI information. Once you set your policy, also, if you have any kind of data classification and categorization set, and you only want to anonymize data based on those categorizations, then you can use this drop-down and find all the fields which have a particular categorization. So for example, in my own, I have a few fields which have PII categorized. So by doing that, this will help me narrow down and find all those fields. And then I can either choose different actions for those fields or I can go here, click-on "bulk action" and then choose the same action across all the fields having the same level of categorization. Once you've decided how you want your objects to be processed for one of these objects, if you just have one object in your policy, then you can click "save" and proceed, or if you have multiple objects, then you can, again, click on "add object" and repeat the same process. If needed, you can also add the same object multiple times because you could be having different filter criterias and different frequencies at which you want that subset of data to be affected. If you have requirements where you also want the child objects to be affected, then you can also click on "add child object" here. And by doing that, now, in this list, you will see all the child objects of contact, and you can choose that and then again set this filter criteria and the action that you would like to take on that particular object. Once you're ready with your policy, you can click "save" or you can click on "edit". And over here, you can set the frequency at which you want to execute this policy. You can run it now, immediately. You can schedule it for a later date, or you can set a frequent cadence depending on your use case. Once you're ready with your policy, you can click on "publish". Once you click "publish", the status of the policy will be changed to active. And then depending on the frequency you've set, the policies will start getting to execute. If you want to see the status of your policy execution, then you can go back to the home page and look at scheduled jobs tab. Over here, you will see a list of policies execution which are either going to be scheduled or are currently running. We can also update the list view to see all the previous job sessions that were executed and anything that's getting executed right now. When you click on any of this job session IDs, you can see the status. If it's currently running, you will see an updated status on this page that shows how many records were loaded, how many were acted upon, if any of the related objects' data was deleted or not and what's the final status of this execution. And when I scroll down to the bottom, you can actually also see the total count of how many records got affected. Now, if there are any errors in your execution, you will see account here. And if you want to know more details about what the errors are, you can go to the related tab and take a look at the Failures Related list to see what happens... Okay. So this is how simple it is to actually create and execute your policies. Now, of course, because we are working a lot with Privacy Center, we are actively and continuously making changes. So we do have a lot of fun and exciting things that's coming off the Privacy Center. So far, I've shown you a demo of how you can delete data on your own or minimize it. We are doing a pilot of the capability of data retention where, if you want to move data over to a separate retention store and then delete it from there and set a retention duration, then we have some new capabilities coming up there. We have a new retention store, which is built on Hyperforce. The pilot is ongoing. So if you are interested, you can reach out to your account team and they can work with me directly to help you get onboard on this pilot. We will continue to add few more enhancements around Privacy Center. So we are going to be updating the way the error logging is done, so it's more clear and more -- it has more data on why -- the policy execution phase. We will be adding some more enhancements in the UI, so you can import policies across your own very easily. So this way, you don't have to create -- recreate the same policy again and again when you're going from your test environment to your production mode. We're also going to be adding support from some very special objects like users that you can also do [indiscernible] for user object. And we are super excited. We are also working on this capability called Privacy Hold, which is basically our version of Legal Hold. So let's say you have a record which you do not want to get deleted or updated because of any kind of legal issues or any audits or compliance going on. Then you can use Privacy Hold tag and set that tag to active on that record. And once the tag is active, none of the Privacy Center processes will be able to update or minimize or delete that record. So we have that coming up as well in the next release. And then for the other areas outside of data minimization, we have updates coming to Preference Manager. So we are working on an instant release. We already have data where you can actually sync some of the content objects to Data Cloud. We're going to work -- we're going to continue to work on this iteratively and make sure that we are able to sync all the consent objects from your Salesforce [ code odd ] into Data Cloud. So this way the consent remains in sync across all of your works. We are also going to be adding some more API enhancements specifically around data subject access rights and right to be forgotten so that you can integrate that with any other third-party application where you also might have some data. And lastly, we will -- we're also working with Salesforce Backup so that you can integrate any of your DSAR requests, specifically on right to be forgotten in Salesforce Backup. So if a request comes in that says, "Please delete my data", then you can delete that data from your Salesforce [indiscernible] and from Salesforce Backup. But the fun doesn't stop here. We have a lot of resources as well. So if you would like to learn more, we have a lot of these websites that you can take a look at for Privacy Center. And with that, again, I'd like to thank you for your time and then pass it over this virtual podium to Bryce. Over to you, Bryce.

Thank you, Poorva, always a tough act to follow. That was great. Hi, everybody. Just reiterating, I'm Bryce Blackwell. I am the Product Manager for Data Mask. Really excited to talk about some of the new and exciting things we have coming to the product. Before I get kind of into the technical details, I just want to set the scene a little bit and talk about why data privacy and protection is so important in sandbox environments. In production, you have your typical production data. You have all your sensitive PII, your customers. A sandbox is an exact copy of that production data, all the PII, your Social Security numbers, your e-mails get transferred to that sandbox environment, but all the protocols and security and make sure you have at least privileged access set up in production is entirely different in your sandbox. You have different developers and people working with that data that can be very sensitive and they don't need access to that. And sadly, within sandbox environments, we've seen threat actors come after sandboxes. Just the way that sandbox credentials are shared makes it to where access is much easier to all that sensitive PII data. And it's become a major concern for a lot of our customers about how they're protecting and ensuring that their sandbox environment is regulated and has all of their data properly secured. And so that's where Data Mask comes in. Data Mask is a great tool for making sure you're protecting your sensitive data in sandboxes today. It helps you deidentify data, make sure you're managing compliance, but still ensuring that your data is useful for testing. The way that we do this is create masking rules that you can replace names with names, e-mails with e-mails so that all of your data is still functional for testing and making sure that your new features are going to work correctly. But all of the data that you might have, the PII, is successfully de-identified. So your team can develop and customize with agility, work with realistic data and not have to be concerned about where and when is PII being transferred or wish privileged access. So diving a little bit deeper into the use cases for Data Mask. First and foremost, we have legal privacy compliance, so making sure you're in regulation with wish privileged access. Poorva called out some of the privacy regulations that are coming into place. So making sure your sandbox is in compliance with that is really, really important. The second thing is that a sandbox is absolutely another place that you have to have security overhead. You have to make sure that, that environment is protected. By using Data Mask and ensuring that there is no sensitive information in your sandbox, you can help reduce some of that security overhead and not have to worry about the exact who's in and out of your sandbox because there's less sensitive information in there. One that we see a lot of times that people ask questions about is, how can I support data residency and external staff where people from cross borders or people from different teams might be accessing their sandbox environment. Using Data Mask to ensure that, that data has been deidentified allows you to support external staff wherever and however they might be working. Lastly, trusted AI development has absolutely been in the news lately in making sure that, when you're using AI and GenAI tools, that you're not exporting sensitive customer information while you're testing these new features. Using Data Mask in your sandbox to ensure that, that data is protected helps make sure that you're using GenAI in a trusted way. So that's a little bit about how the product is used and some of the things we have today. I'm going to jump into a demo. But one of the things I'm really excited to talk about is some of the major changes that we have coming to the product. Data Mask, for anyone that was previously familiar with it, was built as a managed package, which was great. We've been able to make some really awesome improvements though as we've migrated Data Mask into the core platform. So a lot of amazing tools we have internally as part of our core platform architecture, and we're starting to take advantage of those as a product. Some of the 3 core functionalities that we're gaining as we move into core is that we just have faster performance. We're able to multi-thread, manage records kind of in parallel to where, when customers are trying to mask millions of records in their sandbox and do testing at scale, we can handle that in an efficient amount of time. We also deactivate automations previously. So if you have triggers and validation rules, those can get a little bit finicky when you're trying to mask that data to be a similar data set. So now we're able to bypass those automations entirely rather than having to worry about deactivating them and managing other third-party managed packages. Lastly is just making sure that we're handling complex data. Using Salesforce nowadays continues to grow in relationships between objects and then a variety of different schemas and objects that you might be using. So we've made sure that Data Mask can handle all of those different data relationships in a clear and concise manner so that your data remains yours in the fashion that you want it to be. So real quick, I'm going to just go ahead and jump into a demo. I'm going to give you guys a preview of what Data Mask Encore looks like. We've got some more fun changes coming in the next releases as well that I'll be able to share. But let's go ahead and jump into a demo. So Data Mask right now kind of takes you directly into your configuration page. This is the list of all of our teams, a variety of tests. You can create a new one here and you can create a name. I'll go ahead and jump into one that I've spun up in this talk. But you can see you have the ability to clone, delete and run from this page. I'm hopping into here. Data Mask has 2 parts. The first is kind of what we consider org-wise settings. These are things like deleting case comments, deleting e-mail and chatter, but you can also set run frequencies. So if you have data that's coming in from some form of integration where you need to make sure that, that data is masked outside of a refresh period, you can run an automated data mask to make sure any new data is masked automatically. Jumping into the configuration, right now I have account and contact, but you're able to search for -- here for custom objects and a variety of all the standard objects that we support. But using account as an example, we have a variety of different fields that you can see in different types of masking. I've used random here where you can set mins and maxes to make sure that you're testing kind of edge cases within your sandbox environment. You can use libraries, and we have a number of preset libraries here to where, based off the type of text that you want to be filled in, you can use one of our values that will create randomly generated value of all different sorts. So this is just an example on account. You can make values unique that's specific to your testing. Jumping over to context here, I can give some examples of how we use pattern masking as well as the data filter. So I mentioned a minute ago that, if you have data integrations coming in and you have data that's coming in outside of a refresh process and you just want to mask that subset of data, you can use a filter like the last modified date to target that specific section of data. You can do similar things that, if you wanted to mask data in one part of the world in a certain way and then another subset of data some other way, or you have more PII [indiscernible], you can use the data filter and do that. Jumping down here, you can see a couple of other examples of how we use library and randomization to create different values. And then we have a pattern, which you can see here as an example. You can create a variety of different patterns. I can do 4 digits. I can do a variety of 4 digits and characters. And you can see how that's generated here on the site. So each time that the data mask goes through a record, it will ensure that has been masked appropriately. You can also use a variety of static values here that we have. So right now, I've created a pattern for the main part of an e-mail, but I've ensured each of the e-mails end in sfdc.com, so a variety of ways that you can use pattern and library and randomization to create a masking configuration that works for you. Jumping over to what it looks like on our jobs page of how you can kind of evaluate a job that's run. Let me actually jump over here. It just disappeared. But similar to what Poorva shared a minute ago is that you -- we have a run log page where you can see all of the different run logs as they're going on. You can see each account object run-by-run so you can evaluate where that data match job sits as it's progressing. Jumping back, let me actually go over to what's coming next to the product is we have our road map. And so some of the things that we had talked about is now that we're on the power of core, we have the ability to continue to ramp up performance by multi-threading multiple records at once. We're going to be bringing the entire new product, Encore, both coming with that core engine as well as a new UI, so all kind of new enhancements coming to the configuration page for you to make it faster and easier to identify and create masking rules where your PII exists in your job. We'll also be launching with our job scheduler as well as some new enhancements coming around just automation and customization in your sandbox environment. That includes things like custom libraries to where, if you have specific subsets or data that you want to be filled in, rather than using our presets, you're able to make your own. You can also do it with multiple languages. If you want to use Japanese or Korean characters rather than English, you can do that, too. So you can ensure that your data set is customized to the way that you want it. Two of the things that we get most often asked about that we're really excited about is chaining the data masking and augment in the refresh process together. Right now, we have a job schedule that you can use to run Data Mask on a frequency. But in our upcoming releases, we're going to make it to where you can signify specific Data Mask configurations to run as soon as a refresh happens. So your data is masked automatically without ever having to log in or set a Data Mask job to run. You just know, when that sandbox is refreshed, your data has already been taken care of. Lastly, we have synthetic data. Right now, Data Mask takes data that you have from production and we mask it based off of a series of rules. Synthetic data is the next evolution of that where not only can you use your production data to make data that you have today in your sandboxes, but if you have new use cases like new features or data that's not yet existing in production but you'd have to test with, whether for training or new features, you can create that data using our synthetic data tool. We have many other exciting features coming to Data Mask, including with Data Cloud, that I'm really excited to share in the coming months. But we're really excited to bring Data Mask Encore to you guys here very shortly. If you're interested in learning more about Data Mask resources, we have our website, our demo and data sheet all online, as well as a really awesome trailhead that can walk you through why it's so important to use the product as well as what happens once you start installing and configuring the product and best practices that we have there. So with that, I'll go ahead and turn it back to Amanda. We really appreciate your all's time. I see some questions popping up, so I'll get to those here shortly. But thanks again for listening.

All right. So let's move to Q&A. It looks like the first question here, my favorite question because it relates to AI, and it wouldn't be a Salesforce presentation if we didn't talk about AI. So the question is, I hear AI everywhere now. How can some of the Salesforce products mentioned, help me stay safe for compliant while using AI? So I will pass this off to Bryce and then Poorva to fill in any gaps here.

Absolutely. So especially as we talk about AI, I mean, one of the #1 consideration is, where is my sensitive PII going? If I'm using a GenAI tool, is that data leaving the Salesforce trust boundary? What does that look like? We, of course, have all of our amazing tools with our Einstein Trust layers. You can ensure that, that data stays within it. But when you're testing other trusted AI features, it's really important that you use a protected data set. And that's where Data Mask can come in and ensure that your data is still usable and testable and similar to what your production data will look like, but it's been de-identified to where you don't have to worry about customer information potentially leaving your sandbox environment.

Awesome. Thanks, Bryce. Poorva, anything to add for -- on the Privacy Center side?

No. Yes, absolutely. Plus 1 to what Bryce said. It's also important that, when you are feeding your data into all of your AI systems, you make sure that you are feeding the data that's only relevant. And that's where Privacy Center can help you with that process where you can actually minimize the data, get rid of data, which you don't need any more, or move it away to a separate place so that, when you're feeding data, you are actually feeding clean data, you're feeding data that's relevant so you're actually protecting your customers' information, whether they're an active or an inactive customer.

Beautiful. Awesome. So Poorva, don't go too far because the next question, I believe, is for you. It was asked during the Data Mask portion, but the question is, does it require Heroku at one point? I thought it did. This is from Todd. So I believe this might be a Privacy Center question.

Yes, it is. So as I said in the demo, that we are doing a rearchitecture of Privacy Center. Yes, in the past, we did require you to have a Heroku connection for Privacy Center, but that's what we are rearchitecting. We are moving away from that architecture and bringing everything from Privacy Center onto the core platform. Everything that you saw in the demo does not require any Heroku connection. As -- if you have the license for Privacy Center or whenever you get the license, you should be able to just start spinning up this product and create your own policies. We are also working on one small aspect, which is data retention, where -- like previously we were using Heroku as our retention store where you could move data over from Salesforce to Heroku. We are rearchitecting that as well so that we don't need to use Heroku. We are coming up with a new retention store which will be built on Hyperforce, and it will have the same UI, the same capability that you saw in the demo with some more enhancements on to move data to retention store. And that we'll be just not requiring any Heroku and anything. It'll just be like Privacy Center Encore.

Awesome. Thanks, Poorva. We did just get a question. I think this may be in relation to what you were just discussing, Poorva. Is this integrated to the core systems of FFCC from China?

Yes, it is. It is -- since it's built completely on top of the core platform, it's integrated with Salesforce, you know. So just like any other objects that you see, data management policies also are built. Because they're built on top of core, you can still have list views. A lot of these policies like right to be forgotten can also be used in reporting. So you get a lot of platform capabilities as well, workflows, process builders, et cetera.

Awesome. Okay. An easy question that I can take. Is Privacy Center an add-on? What about Data Mask? Are they separate? So these are 2 separate products, 2 separate add-on SKUs. So I had a few questions around that. All right, let's see. Okay. It looks like, Bryce, we have a few Data Mask questions. Do you want to read those and answer them?

Yes, happy to. So one of the first ones we got is, for data masking and masked data, do you need to disable automations and flows and process builders? And the answer is no. In both versions that we have of Data Mask today, all of the automations are disabled or bypassed on our end. So you don't have to worry about those. You're not going to get a bunch of e-mails being sent out as we transform data or things like that. That's handled all on our end. And then the next question I had is, does Data Mask support Sandbox seeding, or is that on the road map? And the answer is it is on the road map. And so we look at seeding as specifically the migration of data between environments. And that is something that we're working with some other teams internally on that the Sandbox team can bring to the table. We also have the synthetic data that I mentioned, which is the creation of net new data. And so that hand-in-hand gives you a lot of tools to ensure that each of your different environments have the data that you need in the format that you needed it.

All right. Well, that looks like the last of the questions. So Bryce or Poorva, let me know if you have any closing statements before -- anything else you missed or questions you thought should be -- maybe should we have been asked.

I did see a question about, is Einstein integrated with it? And the answer is, we do have some really exciting AI and Einstein-enabled features coming as well. One of the specific ones for Data Mask is the ability to go and identify where your PII is in your environment. Right now, we have compatibility with data classification where, if you classify a field, we're able to sort and show that for you so you can easily mask those. But here in the future, as part of our automation efforts, we want to go ahead and tell you where that PII exists and even help you create configurations for that automatically. So Einstein is also coming...

Well, very exciting to hear. All right. Well, thank you all very much for attending the session and for all of your great questions from Ken and Chanu and Ankur. It's just so many great questions. We really appreciate your time, and thanks again to Poorva and Bryce, and we'll see you later.

Yes. Thank you so much, everyone.

Thank you, guys. Appreciate it.