so let's get started so hi everyone thank you so much uh for being here today and spending your lunch hour with us uh we are back with another edition of our East solution Consulting Healthcare webinars today we're going to be talking about data security on the now platform Phi and Beyond so I'm going to let them introduce themselves but we are very lucky to have our platform privacy and security team with us today to take on most of this presenting um before we do begin all of you are I'm sure familiar with this slide but we always do have to remind you that we may be talking about things that are on the product roadmap I actually know in fact that we will be and as a publicly traded company just make sure that you are making all of your purchasing decisions based on what is publicly available today so uh let's talk a little bit about what we're going to be doing today uh we'll start with just a short overview of our platform security Vision we know that many of you are on are at different places on your platform security Journey with with the platform and with servicenow so we'll talk a little bit about what the vision looks like we will then pivot to our platform security team where they'll introduce the concept of servicenow involved so what do these premium offerings mean what do they entail we will walk through a demo of vaults for healthcare organizations and then we'll talk a little bit about the resources that are available to you as well as have some time for Q a hopefully about 10 minutes for Q a um I do want to mention for the demo portion we are going to be walking through a demo talking about creating cases for patients on the servicenow platform so before we start doing that and start talking about that I do just want to mention that we absolutely understand that so many of you are on different places in your security journey of course we have many foundational offerings that are available to you that you're all using today we also have our premium offerings so for today's demonstration when we create patient cases we just thought that this would be the best way to show a large amount of Phi on the platform so that you can see exactly how the Vault offering works of course we understand many of you are not doing that lots of you are using the platform for patient-facing and patient cases so I just wanted to make sure that I called that out before we get started so as far as who's going to be talking today uh uh I'm Lindsay Callahan I am a advisory solution consultant uh in the Northeast so I cover all of the Northeast I am joined by my manager Marjan she's the manager of all of us on the East for healthcare and then uh for cut Jared and Taylor I'd love to let you introduce yourself since you'll be doing the bulk of the talking thank you Lindsay I appreciate it for inviting me for this session hi everyone thanks for joining the session my name is perkas Salim um I'm a Alpine PM in platform privacy and security team thanks for a cup hey everybody my name is Taylor Kozinski I am also on the outbound product management team for platform privacy and security um based in San Diego and prior to joining this team I was on the customer side so in the sense of I was in service now working with our customers um so it's great to be in front of customers again and I hope we can share a little bit more about vault in our data privacy offerings hey I'm Jared Mont I'm on the product success team here at servicenow working with customers to make sure they get the most out of our products then on the platform about nine years now doing uh platform internals Integrations and security so looking forward to any of the uh complicated q a questions that you have accurate of a demo Brave to open yourself up to that um so uh to just to begin before I pass it over to Taylor I wanted to talk a little bit about our platform security vision and what we what we mean when we're talking about the various options that are available on the platform for all of you so of course uh hopefully all of you know that at servicenow protecting our customer data is absolutely a top priority different customers and all of my customers really I know in the Northeast have different use cases so we really provide you with a range of options to fit your needs so what but what you all have in common all of you on the platform is that you are using the now platform for cross-functional and cross-enterprise workflows and there really is a need to ensure that you are working with a secure platform so I'd like to think of security in General on the platform as having three pillars so we have those out of the box you know free foundational elements about the platform that are available to you so you can think of that as almost the bottom of the triangle things like encryption and Transit things like ensuring that you have advanced High availability architecture and your multi-instance capabilities then above that we have some configurable options that are still available for free on the platform so many of you are using roles and AC CLS and different kinds of Security Options to ensure that the right people are seeing the right information so sort of what this slide says and then we also have a variety of Premium offerings that Taylor uh Jared and perkot will be talking about today that many of our customers with lots of changing atmosphere especially in Industries like healthcare and finance are starting to turn to Chevette so that's what we'll be talking about but I did just want to make sure that all of you uh understand that we know you're all on different places within your journey and we have lots that is available already on the platform uh to suit your needs so from there I am going to hand it off to uh Taylor to introduce to all of you the concept of service handballs awesome thank you Lindsay and then we'll just stop screens [Music] perfect thank you for cut so to kind of set the stage right changing regulations means changing obligations so as our customers want to do more in service now they are adding more sensitive data into their environments and so essentially what we wanted to give you guys is like a high level overview is you know our team is platform privacy and security so our offerings really apply to all of the workflows and bus that we have here but we really did want to focus today just on employee data and customer data to kind of narrow down the topics um so when we're looking at you know overall as we add more sensitive data we really want to you know share and encourage a type of security Excellence which is you know from day one from the planning stages of like okay we are thinking about building this application or having this sort of workflow how can we incorporate security practices to make sure that you know everybody feels comfortable and it's truly scalable and the way that is the most efficient and so I will oh and then yeah next slide here so to give a little context as well as just like the healthcare landscape so I was able to pull some um some studies from 2023 and 2022 so security misconfigurations are a critical challenge facing Healthcare organizations this was really enlightening for me to learn about it too um when we talk with customers we always want to get an idea of what they might be focusing on and so to give you know a high level figure eighty percent of healthcare data breaches uh that were reported to the HHS was accounted for by hacking while only 15 was by unauthorized access another thing too there's been an increase by 45 percent of healthcare data breaches that were attributed by hacking um over just the past five years and 36 of those Healthcare facilities or Healthcare facilities in general that were reported to the HSS at an increase in medical complications going to ransomware attacks so this kind of gives context as well because um when we think about or when I think about oh Healthcare I think about regulations with customer data but it's regulations and making sure that we're protecting against Bad actors in different ways and so I will pass it over to percott who will give an overview of service novelt and then we'll kind of dive into how we can help address those those solutions for you guys all right thank you so much Taylor appreciate it let's talk about volt what is volt and what what is servicenow boat it is our Marquee security enhancement Suite intended to provide additional layer of security and privacy capabilities for the now platform and it consists of five key security elements one platform encryption native standards based data address encryption with customer control key life cycle which allows organizations to comply with industry mandates second data anonymization to ensure data privacy by classifying and anonymizing specific data fields containing personally identifying for identifiable information or pii three the secrets management to securely store and control access to credentials in servicenow like passwords certificates API keys and the tokens before code signing to validate authenticity and integrity of the software on the mid server and the last product is called log expert service or Les which improves security threat monitoring with easy integration of servicenow system logs into larger Enterprise security analytics system for example uh the Splunk Analytics this comprehensive security solution has been packaged to make it easy for organizations to order and consume the solution let's talk about a little bit more around the platform encryption so what is platform encryption platform encryption address is a key customer need of balancing how customers protect and share data in the cloud by ensuring servicenow data is encrypted at rest with only authorized users who can access it it consists of two encryption products One Cloud encryption and second column level encryption Enterprise otherwise known as clay Enterprise let's look at some of the underlying fundamentals to understand the customer value and the differentiators for the platform encryption Cloud encryption works at the database level meaning all the data stored for the customer instance at this level is what we call encrypted at rest the protection this is giving similar to our older products like database encryption or full disk encryption is that if a hard drive was physically stolen from our data center and someone tried to plug that hard drive in outside of our data center the data would be encrypted and unreadable this is this is great protecting the theft of physical piece of Hardware however this doesn't give any additional encryption protection for the app itself if someone is logged in the clay Enterprise comes into play it encourts the data within the app and a customer can configure who should see the encrypt data or not if we look at my instance again we can see in the green highlighted section that certain information has encrypted in parenthesis this means that I'm logged in as a user that's been given access to this data in the app but other logged in users would not be able to see this information the additional benefit of clay Enterprise is that the data stored in the database is also encrypted so the customers using this get the value of AD level and database level encryption with a single product the difference here is that the benefit is only for the specific fields that customer have applied clay Enterprise to like what you saw in my instance for example it won't be all the data like what you get with Cloud encryption but it does provide additional controls to restrict what information can be seen by people logging into an instance and even by our servicenet employees and if this is called layering or defense in-depth approach by applying both Cloud encryption and Clay Enterprise together customer can get the benefit of protecting all the data at rest in their database with additional app layer protection for specific extra sensitive fields and for where they're additionally concerned about who from their end or RN can access that data both of these uh products also have customer managed encryption keys that they can control from right inside their instance with no need to interact with anyone from servicenow finally remember that a competitive differential differentiator for us is that we can provide this app layer protection encryption while still allowing those encrypted fields to work with app functionality let's talk about servicenow data privacy which is the second product of world bundle service not data privacy provide a tool to protect sensitive data like pii and a Phi by using data Discovery classification and anonymization capability to specific data fields or user to ensure the privacy of confidential data and increase Regulatory Compliance it consists of three major components data Discovery data classification and as well as data anonymization both data Discovery and a classification feature really help users to discover and understand their confidential data properly with the the right data classes before it gets anonymized the data anonymization give users the ability to redact sensitive information in servicenow instances by using the right tool and techniques servicenow data Discovery is is a new feature that we introduced in May as part of data privacy what it really does is to identify the sensitive data by applying intelligent Discovery capability to the table we Target and find find what data we have where it's located with classification statuses as well it also elevates compliance level and the reduced Risk by identifying sensitive data and avoid potential threats and a negative impact to your brain data anonymization gives users the ability to redact sensory information in servicenow instance by using the right tool and techniques and a data privacy including data anonymization feature has two main use cases one the first use case is gdpr right to be forgotten request the let's say an employee leaves a company or a customer of our hours has their own customers like uh think customer service management for example where they need to anonymize customer data you can select a user and anonymize pii or Phi associated with that user in a production instances the Second Use case is to anonymize sensitive data going into clone or sub-production so that the information is not accidentally shared with third parties or as part of delegated implementations such as with system implementers delegated development in a customer's soft software development life cycle or sdlc the production environment has all the real data including pii and Phi and more and in order for these other instances to be used properly they also need data most often copied from production environment in order to provide the most realistic way to test new configurations but if we copy information from production environment that usually means we're also copying pii in a Phi and exposing it to the development teams that they may not need to see it or technically they shouldn't see it these Dev teams in these other environments can be full-time employees of the company themselves or they could be the third party contractors working outside of the company or even country so where the the development is occurring in and so the customer can use the data anonymization to natively de-identify the Phi and a pii associated with the information they're pushing down into these lower environments this ensures that these subprods have the legitimate data that they need to con they need for configurations and the testing without exposing information they shouldn't be exposing outside of production environment with that I'm just going to go into the demo so for the sake of this demo I created three different Persona and Lindsay of the explained earlier as well that you know there are certain the goal of this demo is to just kind of like how to reflect and anonymize and protect some of the sensitive information that we're using in various different applications and I'm sure that each of you might have different solution as well as the way of creating some sort of procedure request but for the sake of argument I created three three Persona to reflect some of the data privacy there as well the first Persona that I created is Mike Salem who is a hospital staff responsible for creating a case for patient the second Persona is John Jones who is the case scheduler responsible for monitoring the open cases with different statuses and the third Persona that I created is uh the Tom Holland who is our I.T admin are responsible for maintaining it platform as well so with that let's jump on to the demo itself so here this is Mike Salem uh who is responsible creating some procedure request on this portal uh what we call it Healthcare life science tool that service now recently created so Mike is going to create the procedure request for existing patient called Gina Parker so here my comes to the case and I click create a procedure request with this he fills all the information about the Gina Parker and the click submit and here the procedure request for Gmail Parker is already created now I will switch it over to John who's responsible for handling these cases now this is the workspace for John and John can see all the the procedure requests that is created for other users to make sure that the status is right it's being preceded as well as there it's assigned to the right person as well John can see the the procedure request that Mike created on behalf of Gina Parker and he clicks this and here he can see all the Phi data here for example procedure information the patient statuses they're requesting practitioner the other detail and patient information as well here you can see all the Phi data is automatically encrypted with the parenthesis called encrypted the symbol here and this is where our column level encryption Enterprise as part of platform encryption bundle comes into picture and protect and encrypt all the personal uh Phi data here the reason why you think that John can see all these Phi data is because he has the right policy access as well as the model assigned to him in order to proceed with his work and I will switch it over to Persona with it to see how that data will look like and here John can proceed with other the details of this Gina Parker to make sure that everything is right as you can see all the clay Enterprise the common law encryption is already protecting all these sensitive data here to make sure that it's not exposing to other other people who doesn't have the right privilege access to to see these data here here we can see the patient's information and again we have see we can see all the Phi data is identified and already reflected and protected here now let's take a look at the another Persona called Tom Holland since Tom Holland is our I.T admin who is only responsible for maintaining or servicing for it operation so he doesn't he's not required to see any Phi data but he still can't proceed with his with his work for example if Tom can open the exact same table that where this the Phi data is already located for example the procedure request table for Gina Parker as well as other patients the all he can see is Justice status as well as priority the other Phi data is already encrypted the reason is he doesn't have any privileged access to the the c or decrypt the these data because he doesn't have the right module as well as the policy assigned to him so this is how the clay protects con level encryption Enterprise protects the data not only on the the database level but also on the application Level as well now I'm I'm going to share the screen one more time and then I'll jump to another demo which is our data privacy so before I jump to the data privacy uh the section here so if we look at these two tables as you can see that one is the patient procedure request table this is how the data privacy will come into picture based on the based on the tables and column anonymization for example if you look at the the mobile phone number on the left side this has a mobile column that has all the mobile phone number that with the original value there and after data privacy takes place with using old anonymization technique this is how you see on the right side bottom the mobile phone number is already redact and replaced with all the different text and if you look at the credit card number on the left side so you can see all the full the credit card number there as as original in the table but if you look at the bottom after using data privacy technique it redacts as well as replace old value based on the what technique we chose and for gdpr right to be forgotten request which is the second use cases that I explained earlier if you look at the table which is the user table where you can find all the users information for example if you anonymize some of the user's information due to they requested that their uh the data has to be completely remote or anonymized this the one on the left side is the original table and the one and the the bottom is the table that how it looks like after anonymizing some certain users information for example uh the first and second user Mike Salem as well as Abraham Lincoln they uh we anonymized their uh the personal data uh the email if you look at the email uh the value here after anonymization happened it completely removed the old email section the employee number uh the employee number is already removed as well and the mobile phone number as well as the mo the also mobile phone number is removed as well so this is how we apply data privacy to our the Second Use cases right to pay for accounting request as well and again it's up to the company itself to see how much uh the the personal information or data that we can remove so in base in for the sake of this uh the demo I just removed the three The Columns here which is email uh the employee number as well as mobile phone number here let's talk about the data Discovery so in previous time oh that's the slide I explained that the data privacy has three major components one is data Discovery and the data classification as well as data anonymization which is the right technique to anonymize these data so the further data Discovery piece like there's three major components that we need to pay attention one is data pattern which is the the data Discovery the logic that we will use uh for data Discovery job to run uh uh when it scans the table uh when the uh the target table when we uh when we decide to to anonymize so the second one is the table itself the wish table we're going to reflect or we're going to anonym uh to discover I know it's really depends upon the user itself like where you have the Phi data located for example in this scenario we have a patient procedure request table as well as the patient account table and where I think that all the Phi data is uh distorted there so that's why we selected uh the patient procedure request table and the patient account table to uh for this data Discovery to run and identify some of the Phi data for me so the third part is dictionary entry which is literally the column itself after the scan and uh the happens so data Discovery will populate based on the pattern as well as the target table that I selected with all the information that matches with this pattern as identified in the data entry section so these are the Phi data that data Discovery feature found for me after I run this job after we found all the data Discovery job here so the next piece is classification so I have all the Phi data which is I already identified and found so next what I do what I would like to do is just to classify them I after I classify it it will status will change from new to classified and these are the data that I will use as product anonymization so the second component is classification so here I just classified all these data after I found where those Phi data is located and now you can see the table itself and a column name that the data Discovery job found for me with all the classification name as well and I for the sake of this demo I called all these classification as confidential and again it's up to users to mark them as confidential or pii or Phi or or many so there are two things that we need to pay attention the one the data classification is a prerequisite for using data anonymization to redact the data so in order to use the third component of data privacy which is anonymization to put all the right techniques to anonymize those data we have to classify those data that we would like to anonymize any unclassified data I cannot be anonymized with uh with these techniques if we do not classify them properly so here these are the techniques that the first row the five techniques which comes out of the box as part of an atomization and the bottom three is the one that I custom like customized and created for the sake of this demo I created three customized technique that I will use in the next part the One anonymization credit card the second anonymized email and the third the customer phone number and it really uh it's really up to the users to use uh the either they can create their anonymization technique by their own or they can use the one which is on the first row uh the five out of the box components as well Papercut I think this might be a good time I I'm doing great I didn't want to stop you but at the switch over to this it looks like someone had their hand up and I'm also realizing that it's possible that maybe the chat's not working I just I want to make sure that Calvin we know we saw your hand um and I know we only have 20 minutes so if there's a question on this piece um do we feel okay about taking it sure okay uh Calvin I think that I need to no you're good Calvin go ahead thank you um could you could you talk a little bit more about uh the discoveries using schedules are we talking about servicenow Discovery are we just talking Discovery in general uh do you are you are you asking the data Discovery piece correct okay so uh can't repeat your question one more time do you want me to explain a little bit more on this well I just want to know are we talking about using the discovery tool within servicenow for the data discovery uh the data Discovery is the the new released product as part of the data privacy yeah release so this is uh this is part of like the data privacy we used to have classification as well as anonymization feature but now we have the data Discovery uh added that really allows users to just kind of like just scan through all the the tables where they think the Phi and API resides without manually checking the whole thing so as long as they had select the right data pattern as well as the target the right table the data Discovery job will run all these tables based on the data pattern that we created and then populate all these uh the the pii and Phi columns there and so good question but I'll I will jump in and say yeah similarly named but completely separate from the itom discovery that populates the cmdb got it got it thanks guys for the clarification appreciated meaning no this is a good question I'm glad you asked and I wanted to make sure that you you all know that I don't think the shot's working so if you do have a question raise your hand and then forgot before I let you continue you have about 20 minutes left so maybe we will um just keep the last 10 for Q a and for pot you can take the next 10 and Taylor will skip her resources piece and we'll send it out via email does that sound good sure sure no no keep going I'm I think that you you still have a piece of your demo so definitely keep going for the next 10 minutes yeah sure sure so with that I'll conclude the uh uh the the data privacy uh component here so here you can see the mobile number is completely changed and replaced with the text called text one three three uh which is already anonymized and the credit card is already reflected here as well uh this is the first use case table and column based and the second one is the gdpr right if you forgotten request as you see the value the email is already remote as well as the employee number plus uh the phone number as well so with that I conclude my presentation and pass it over to Taylor to go over the next slide thank you perfect so I'll go through these really quickly and if anything will just be context into what will be sent over email uh okay if you go to the next line okay so just to recap for today um we went over so we were giving context on service levels for cotton if you want to um forward a little bit to to populate the slide So within servicenow Vault there are currently five products it's platform encryption uh data privacy which is what we went over today but we also have log export service code signing as well as Secrets management so to recap we went over platform encryption specifically cloud and column level so cloud is that data at rest level of encryption we like to have a layered approach so Cloud encryption is data at rest and then we have column level encryption Enterprise which is data at rest and data in use so that is the type of encryption where you're in the application and you can create a module access policy that for cot was talking about earlier where it's like we only want this role to see this or vice versa right and then data privacy which is what we're caught talked about we did highlight data Discovery and data anonymization today but just so you guys know there's also data classification that is part of data privacy as well and so of course if you would like to learn more information because there's a plethora of knowledge and infinite things you can learn around platform security to reach out to your SC for more information and the next slide for Kant this will give a little bit information this is a really common question we get um and we didn't see it come up yet so wanted to get in front of it is what is available to us right now if even if we're not a service novel customer so for column level encryption I wanted to give a context into what we have available as a core platform feature that you can turn on today versus something that's more Enterprise this is part of our premium solution which is available in vault as well as platform encryption does exist in its own bundle so say you're like I only really want platform encryption that is also available then it has cloud and clay or column level encryption into it as well and then the next slide will give a little high level overview of data privacy so we did talk about data Discovery and anonymization today those are our two topics um but as like a highlight again data classification is also part of Vault um in data privacy and data classification is also a core feature so that is also something you guys can leverage today and then next slide here so we do have some Academy sessions coming up we had one yesterday and then we're having some later this month and into the um the summer and fall so I will pause here we do have a QR code if you'd like to scan it um and I will go to the next one just so you guys can see it so for the next slide we have more information around what is available if you'd like to learn more um you know we have our social media platforms but of course I bet you guys are all familiar with our documentation so of course you know product documentation is the best way to go to see the latest and greatest for the release um but we hope you guys you know learned some more that is also forward-looking as well as we roll into Vancouver and I think that will be the place for Q a I will put the the QR code here just in case somebody wants to scan a tailor yes awesome thank you perfect thank you so much Taylor and forgot we want to open it up so um if you have questions would love to hear you speak just use that race hand icon and we'll do the need for awesome we have someone hey hi there can you hear me so uh does data does the testing work uh like it's supposed to data anonymization or would it work differently so I'm talking about the automated test framework [Music] yeah yeah you cut out at a section of your question do you mind repeating it sure sure yeah so uh when we are doing data anonymization at that time uh does it impact uh the testing the automated tests that we have created in any way or does that remain the same so if we have we have a couple of tests that we have created so let's say if we anonymize a data would that remain same or will that need to change this is Ray so I think from that perspective uh um it really comes down to is the ATF really looking at the data itself looking at the process right so in most cases what we're looking at is that the data pii data you're not necessarily testing unless you're testing you know this feature right that are we did we automize it or not maybe that's what you're testing because the idea here is really protecting your PA pii Phi type of content so we really don't want that stuff in your non-prot environment and making sure that those things are securely encrypted or anonymized so that you don't run into any legal or regulatory issues that makes sense thank you Ray yeah I don't see any uh problem with if you're testing out for example that right to be forgotten use case and sanitizing an individuals records um that I haven't personally tested it but I don't see any issue with ATF should be able to anonymize or sanitize that user and then ATF would roll back and then put everything back to where it started so you'd be able to run multiple tests on the same user so um yeah I think both use cases should be compatible thank you uh be young Q go ahead that's me Brian can you hear me yes we can um how does this uh I'm I'm interested in the module encryption how does this uh dovetail with EMR help because we've uh implemented EMR help um and I'm just curious about the modular uh restrictions because uh for perim our help what I had to do because we're talking about for those people who don't know um we're we're integrating what's Epic and so in order for the clinical people to to use it you basically had to give everyone the modular role so they could get into the portal so it kind of um in my view it kind of defeats the encryption part of it because if everybody has the modular role module role they can everybody can see so um I'm a little curious about not necessarily anonymized part of it but the modular uh encryption part of it yeah I'm not I'm not personally familiar with EMR is uh but I was just going to say Jared that's um the product where we are embedding a service portal inside the electronic health record so that clinicians can open an incident regarding issues with the electronic health record from directly inside it as opposed to going to the service portal or giving the help desk a call yes so um there's lots of you know customers are concerned about the fields within the electronic health record because they can contain patient health information I think I think Brian from from the perspective of what you're looking at think about who who you're giving that modular access to you're giving it to the clinician so they already have ndas in place with the organization to actually see that data so they're already working in the EMR where they're seeing the Phi data so this is just being passed so they're ready and epic they already have access to that type of data so yeah you would give them access in order to submit that uh again when it comes back to the people that are processing that data that's probably where you would really try to highlight well now from a support perspective who are the individuals that have access to this is we're only where you want to add restrictions right we have the help desk people and and anybody on the plow that that needs to get the ticket down Downstream so technically everyone that has access is authorized for that access now right someone from you know uh your your message messaging team right shouldn't have access to that data they probably not in that process so technically speaking right if if they have access then they they do have access to that data and they should have visibility toward to it okay thank you I will add one thing about just a product diagnostic comment uh these multiple access policies are able to be configured where the submitter can insert data into an encrypted field and also an encrypted attachments and um not have access to view it's after it's in the database so you can open up you know the entire organization to be able to submit any kind of record and it would be encrypted the downside is the submitter would not be able to see that but then most of the time that's not an issue so I'll throw that out there as well uh that's interesting I like that thank you Michael P go ahead and unmute and ask your question all right thanks everyone um we currently are having calm love of encryption and uh ACLS for keeping some of our Phi information um looking we currently have some business logic that's identifying uh Phi information that's being put into Fields through our service request or via the incident itself um does that anywhere in you know the data Discovery or into classification where the system can help identify um you know Phi being entered into selected fields and you know redacting that information so for cotton and I were talking about this I think maybe a month ago um yeah that unfortunately that's not that real-time detection is not part of the data privacy 1.0 release um I won't make any promises about what's coming in the future but there is another plug-in on the platform called sensitive data handling that is paired with the virtual agent plugin that does have a a real-time API that you could run the work notes uh you could run short description description those sorts of things and detect uh whatever type of sensitive data based on a regex configuration so that plug-in um it is an extra business logic step but that plug-in can help you detect that in real time that's that's great to hear because yeah currently we have to you know receive feedback from someone that's been entered and then we're redacting it via scripts in the background so great to see there's another plug-in tool that we can use and uh utilize so thank you very much yeah and to add on that Michael just uh data Discovery uh the the in Vancouver release we're going to be having full scan future which is similar to the one that Jerry is just uh the highlighted uh for sensitive data handling so it it scans the whole table and I identified those uh the Phi and Pia data great looking forward to it thank you everyone sure thanks Mr pomprion um Mark greeker go ahead hello everybody glad to see all these modern faces again gray and Lindsay um I have a quick question so um here at the near Quest term we have a different epic help scenario where we take a screenshot and we load it into a local encrypted server so we can this do both tools store images and then create hyperlinks within the incident record yes this column model encryption products uh that was discussed handles a variety of yields string Fields URLs dates things like that and it also does handle attachments on the platform so whether it's stored on that table with the record or if you have a separate table to store all these attachments and you link to it through a reference uh the column level encryption should get you there and right you know my next question so vault is it expensive I'll talk to you later about that Mark I couldn't get off a mute there that that we'll speak to John yeah okay yeah we will speak in five minutes if you want yeah thank you thank you quick quick presentation thanks Mark and for all of you I mean of course we've already mentioned this a few times but this was of course only 50 minutes uh we would love to connect if you have more questions if you want to dive in deeper learn about something like pricing you can always reach out to any of your to your essay and your ie do you have any other hands up and I don't see any right now um should we do a last call for hands up Lindsay why are the questions coming in uh if any uh components the team is interested in learning more about it that they feel free to just scan this QR code and we're going to have very granular level of uh the demo based with the use cases including the healthcare industry uh with all five different individual components as part of this Academy session this year as well all right well um if there are I don't see any other hands up so I think that it would be more than fun to give you all four minutes back uh again as always thank you it always blows us away how many attendees we have we are so grateful that you've spent your your lunch hour with us uh and again if there are any follow-up questions any uh requests for further conversations we are happy to support so please just reach out and uh with that I'll let you all go thank you so much for your time