Strengthen your AI Projects in 2026. Privacy and AI Governance Insights

Andre Samokish: If you collect garbage, you'll have garbage. In implementation stage, I feel like the moment when, for example, they're not developing AI, they're implementing the buying services. And then just think about it, how you're going to use it and uh again talk to legal team to understand what would be the risks. Because there is some regulation even saying that there is prohibited AI activities you need to be aware of. So that implementation, your use case can be very tricky and very harmful for your organization. So why do not just go over those lists of high-risk activities, talk to the legal team, and understand in early stage what you can do with those tools and what you cannot. So if you still have a problem you're trying to solve with AI, and it's better to understand in early stage.

Chris Hutchins: It's great to have you.

Andre Samokish: Oh, thank you, Chris. Thank you for having me here. I'm so excited to do uh this conversation. Yeah, I'm happy to be here.

Chris Hutchins: It's been a little bit, you know, we I think we met out in Las Vegas at the Put Data First conference, right?

Andre Samokish: Yeah, that's correct. I love all events in person. You can you can meet uh experts and just just talk about different expertise because that one was basically mostly about security data, put data first. But also I met a couple uh privacy experts, and of course, everyone were talking about AI implication and how it how it works together with privacy, security, and all related data, of course, data.

Chris Hutchins: That was good, that was one of the things that kind of struck me about it because I've been to conferences uh and you know, AI is certainly a topic, but it was really cool for me because the biggest concerns that people are talking about these days are really around governance and and the healthcare sector in particular. We're we're a little behind the behind the curve on that one. A lot of folks like yourselves were like really talking about this stuff with a lot of passion. And you know, for me it was like very, very comforting to know that people like you are really engaged in in thinking about the security, the privacy aspects of it, and kind of how pushing us a bit on that side of it so that we are very thoughtful about how we use it. It's one thing to have a good intention, it's it's another thing to accent, right? So very excited about having a conversation this morning. But let's kind of start on you know the real basic premises that people don't probably understand very well. Try to explain for us a little the role of privacy and AI governance as a manager or to a manager that's not an expert.

Andre Samokish: Yeah, great question, because a few years ago we just had kind of privacy governance, right? Data governance, but then new topic came, AI, and now we there is some demand on experts about privacy and AI governance because both of them touch data, data touch all these industries. And as a manager, for me, most important is to, of course, to support business uh on achieving their goals. They want to build new AI tools or implement buying uh some services from third parties vendors, and those initiatives now just going so fast, and the role of privacy and AI governance experts is to keep up and also be proactive. How you can be proactive, you need to think ahead a little bit and of course build processes and controls in place. I don't want to scare people with word controls because controls means you can go with your initiative, business initiative, when you have those controls if it's required. And you can feel safe if you implement those things upfront, what we call privacy by design. Yeah, implement uh some processes, controls, policies on time in early stage, and then you can just be sure that when you when you're launching your product or feature you already done, and it's not just about complying uh with requirements, which is very important, but also it's about building trust with with clients and be transparent as much as you can, because when people start using new tools, they definitely uh the first concern they have what will happen with the data once they enter to the service, or even touch website, right? And say uh again, they known experts, maybe. I mean, they known privacy, non-AI governance experts. So they want to feel safe and trust is something you need to build. So, role of privacy in AI governance manager is to help business to implement, to build, first of all, to understand what controls and processes you need to have in place to make sure that you're using data in a safe way, then also following an ongoing base, following those controls. And I've I would say I would highlight one more role of privacy manager is to educate people on um privacy, eye governance. It's it's not something that the main goal is to solve problems, it's mostly I would say it's also about building uh building uh to be proactive, building trust with clients and even using this, like when you have strong AI governance and privacy controls in place as a business owner.

Chris Hutchins: You mentioned governance uh in uh the it's the sense of controls, and I think that's one of the the bad raps that that governance has had in times past, but it's now not something that's intended to slow you down. It it's the only way you can safely speed up. I mean, uh you know, I just was working on developing a website and I do not code, but I the technologies are so crazy. I built a pre-roll bus website myself, and I mean it's moving between screens and all kinds of things. So I can only imagine that the types of risks that could be taken if people are engaging someone like yourself with this expertise to help them understand where to put your guardrails and don't do that and don't you know get ahead of yourself because we're going way too fast at this point.

Andre Samokish: Yeah, definitely. And those controls, like just a simple example would be uh put proper language language pop-up window message that you're interacting with the i system, for example. This is an example of controls, it's nothing scary to just uh try to design it as early as you can in early stage of your um product development, and you're okay to go. So it's just something that will help you, and just as you're saying, it's not stopping. That's why I feel like one of the roles of the manager to help people to kind of change mind a little bit or shift to a kind of culture of privacy and governance.

Chris Hutchins: Yeah, I mean that that kind of you know touches on there's areas that people are confusing. Uh, you know, I think you know, control versus governance, uh, that's one but what are some of the other ways that people might be confused about really what this means? Or whatever, like some of the misconceptions people might have about privacy and AI governance.

Andre Samokish: Maybe I would highlight one misconcept that governance it's something that should be handled just by legal and privacy team. But I feel like we really encourage product people, marketing uh teams, cross-functional teams, to be proactive and just to contribute as much as they can to talk to our teams earlier, so we can discuss and again just even if there is some risks, it's another topic to talk about risks. When uh we say in risk it it can be mitigated. Again, we can put some controls in place. I mean, it doesn't mean risk you should stop. Most likely you can mitigate that. I feel like privacy and eye governance, it's work for all cross-functional teams.

Chris Hutchins: Yeah, I think it's a it's an interesting challenge too. Is that organizations are pretty sophisticated and they've got a lot of different verticals, and there's going to be some people you need in the room that they really don't spend a lot of time thinking about this kind of technology, but we need them to because their subject matter expertise really is needs to be represented uh to make sure that we are understanding all of the areas that we could have risk. It's important people understand that that you're not there to control things, you're there to support them as a manager that's responsible for this stuff. Um and I've maybe um I think it'd be kind of cool because I'll be you've you've been pretty at the all the times that we've spoken about this. Maybe you could talk a little bit about an initiative that you're that you're working on that that excites you. I mean, I don't know if people think about privacy and security and governance and think that might be exciting, but I kind of think it is because uh you've been we've been having fun conversations since we've met.

Andre Samokish: Yeah, I feel like privacy and AI governance now sometimes associates as uh something very complicated for non-experts and even for privacy experts, even for legal teams. It's uh overwhelmed with new regulations, with complicated topics. So I feel like one of the initiatives that excites me is to implement educational trainings, to talk to um non-experts, cross-functional teams about simple things, fundamental things. What is what is personal data? How AI even related to personal data? So, what are the main principles of uh handling personal data? I mean, I feel like I can talk even just about one principle, data minimization. I can I need one day to talk about data minimization that teams, you you cannot collect data just in case, right? Right. You need to you need to you need to know why you that data, and most important, you need to explain your customers and uh get their uh consent to the how you're going to use that data. So it's just you save a lot of time and maybe few and maybe save time when you need to do future assessments when you talk to your cross-from cross-functional teams just now. Yeah, set up 15 minutes. I don't think like you need a couple hours meetings every day talking about privacy, just 15 minutes about privacy, uh, maybe once a week or something like that. It should be very simple conversation about most important things, and that shift, that education not happening overnight, it just takes time. So that efforts really require, and I like implementing trainings. Also, I'm very excited about automation because again, it's about simplifying privacy processes when you can implement tools that help you to uh automate and move from Excel sheets to some traceable uh workflow, sending notifications in assessments, setting up tasks, involve people through one tool to collaborate on data privacy and AI governance. There is a lot of similarities how you handle data privacy and AI governance process. So uh yeah, these two things I I feel like it's very important to keep in mind for uh for privacy experts.

Chris Hutchins: You mentioned something that I think is really uh worth kind of pausing on for a second. Historically, in my experience over the years, you know, people that are dealing with data and analytics, data warehousing, they tend to go for, I want to pull in everything. I just want to you know have you talk about that just for a second because this is a shift that has to be made and organizations have to think about this differently now. Because if they're going to start using the AI capabilities on it, now you have to really think about things differently. You you can't just have these these uh these new programs or solutions attached to the ocean and think everything's gonna be okay. Uh that there's gotta be some uh conversations and and ways to handle this. And if I know we didn't talk about this um previously, but maybe just talk a little bit about how you didn't you support teams that and help them to think through that effort. It just seems like it's a it's gonna be a pretty significant shift in how they think and how they manage the data sets.

Andre Samokish: Yeah, great question. Uh, because I feel like now every team wants to onboard some tool, and most likely that tool will have high capabilities because it's speeding up process and everything. But I feel like everything starts with a simple question. Why you need the tool, what that tool will be doing for us, and how I mean, as as a privacy side, I would ask how they're going to use our data and whether they we want them to permit to use that data. Is this mandatory mandatory or we can turn it off? Right. So it's it's just a simple conversation with business owner of the process or who is going to implement that initiative just to to talk about what is the value of the tool and is it really solving out a problem issues or challenges? And okay, now let's talk about data. And uh do we have controls in place for that? If not, can we implement that quickly in parallel with your uh with your initiative? Are there any risks? Are there any high risks and whether there is a way to mitigate it? And again, talking about risks is just something we need to work on. Of course, if there is high risk and there is no mitigation, there is might be a question to stop that initiative. It's a it's a conversation with business in early stage.

Chris Hutchins: Yeah, and I think you know you're you're touching on probably one of the more important aspects of the governance piece of this. It's really what responsible and ethical AI. Um talk a little bit about that. What and how would you describe that in your words? What does it really mean?

Andre Samokish: I feel like we touched a little bit this topic, but ethical AI may there are many uh subtopics, sub-uh things to talk about. But I wouldn't highlight this one that to be very transparent and with your clients, customers, and even if it's not a requirement, think about what you can disclosure on your website to put more information for customers so they are aware about your technologies. If you have AI component, maybe you can explain the logic, logic behind AI. So, how much you are are you transparent with your customers about your business and whether you have controls in place? Let them know, let them know what certification you have. Are you following the major certifications? Do you kind of how you care about data and uh AI safe use in your business, and just make that publicly available so people can go read people reading now, people read more, I'm sure, and they want to know about your business and AI component because there is a different opinion about safe use, right? Safe use of your data, what AI can do, and how scalable it is, and then they make decisions do I need to use this tool or not?

Chris Hutchins: Touching you're touching in on a great kind of an important topic. You mentioned the explainability piece of it. There's a translation needed because you you're not gonna, as a hobbyist, understand, you know, that it's not like you can turn turn over the code and someone's gonna be able to understand what you're doing with it. There's really a translation aspect of it. So, I mean, how do you think about translating abstract principles into concrete controls?

Andre Samokish: Yeah, I feel like first of all, you'll be translating that to different audiences. For example, your employees who have some expertise, right? They they uh they have expertise in tech, most likely, about data in general, not just personal data, but in data. So you can talk to your teams, just help them to understand that that transparency explainability is very important, and involve them to uh to create those documentation about how it works to help them to uh actually future products because now they involve to understand importance. They're not just reading what you created, you're actually working with you together to develop this uh those controls. Another audience would be just your visitors, for example, on your website, your future customers or current customers. So then you need maybe to make sure that you're talking to them same language, I mean professional language, tech language, and explain simple things to help them to understand, like in plain language, in simple language, how your system works. And I feel like collaboration with legal team would be very helpful because they have component of regulation language, and they can explain to you on you can ask all questions legal first, and then kind of translate that to make that more accessible to to your audiences uh to understand that everyone will benefit from that.

Chris Hutchins: Right. Yeah, I I think the the tendency of at least in the last year or so, I mean everyone's going at such a breakneck pace, moving so fast. Um if you're not involving the legal experts, you're you probably have a risk right now that you don't know about, and it's it's a good idea to go go talk to the legal team. Get get them involved. It's just it's it's just this really important thing we don't you don't lose sight of right now because I I think this is gonna be the year of governance for sure. And I don't think it's all gonna be rosy. I think it's gonna be some some bumps here. So I'd encourage people to to really hear this this point. And you know, Andre's absolutely right. You you won't even work with your legal team. There are all kinds of differ different um reasons that that things fail. Where do you see the biggest current failure modes? Data collection, model design, deployment, organizational culture?

Andre Samokish: Great question because every piece is important. You start maybe with data collection, and I feel like I'm not going to say something new when I when I mentioned that if you collect garbage, you'll have garbage about data. So yeah, sorry about that terminology. But also I want to, yeah, I feel like in the in implementation stage, I feel like I want to highlight the moment when, for example, company, they're not developing AI, they they implement in the buying services, and then just think about it how you're going to use that tool, and um again talk to the legal team to understand what would be the risks, right? Because some regulation even uh saying that there is prohibited AI activities you need to be aware of. So that implementation, you your use case can be very uh tricky and very harmful for your organization. So why do not just go over those lists, couple lists of high-risk activities or prohibited activities, talk to legal team and understand in early stage what you can do with those tools and what you cannot. So if you still have a problem you're trying to solve uh with AI, and it's better to understand in early stage, maybe you need to look for another vendor or implement controls, right? Start implementing right now, because some controls really take place to implement, it's not happening overnight. Uh for example, some certification or registration in your system in database, whatever it can I it will take time. So, yeah, I would say um, I don't know, sometimes it's it all comes to communication and collaboration between teams if product or business owner to come to our privacy team, yeah, governance and just share ideas when it's in idea stage, then it's easier to manage.

Chris Hutchins: You're you're hitting on, I think, something that we got we really need to think uh think about a little bit differently than we have before. Inside of a company, there's all generally there's a an intent to speak the same language, right? Yeah. So you and I have talked a little bit about AI literacy before, but what does it mean? What does AI literacy mean for uh non technical staff versus technical staff? I mean the they really do have to be able to communicate and understand each other everybody that would it's it could be even worse than a language barrier if it's not if it's not gonna give some attention.

Andre Samokish: Definitely. And uh that AI literacy um I mean there is different definitions, but I would say there is at least three components. The first of all, it's about AI and technical side, how what are the capabilities, how it works, and for example, what are their performance indicators, what are their metrics you can you can measure performance, what is the accuracy, what are there uh other uh technical things you you need to understand logic behind AI. The second component would be uh it's of course it's regulation. You need to you need to know like what you can do, what you cannot do with AI, and what controls need to be in place. And we talked about this today a lot. So you cannot you cannot avoid governance pieces, it's it's just mandatory. And also opportunity to uh to scale your business and add to your value proposition that oh my gosh, we have such good governance in place. So we want to talk, we want to give it as a benefit to our to our clients. And third component is ethical AI use. Ethical use, I feel like uh what we touched today. Um you cannot collect data just in case. So it's then what purpose you will be using that data for, and that ethical um part now sounds more often and more because it's about how people feel about using the data and what have transparent AI systems. So these three components. So since you mentioned technical stuff and non-technical things, they both would benefit because technical things is a requirement, and technical personnel can explain the site to, for example, for privacy team. Hey, you guys, tech, please give us a little bit more information how it works. And non-technical stuff, of course, we can we can talk to tech to explain importance of understanding technical side. So just just mutually beneficial things.

Chris Hutchins: We've talked a little bit about their data usage uh things here, but let's talk about some of your your lessons that you've learned, including privacy awareness and training, uh, since you've kind of opened this topic, uh how these lessons apply to AI literacy.

Andre Samokish: Great question. It's a very, very big topic about uh trainings, and I feel like uh sometimes businesses need to shift to kind of just to think about it that you're using so much data and you're serving customers. So uh you have so much regulations, you have so much governance in place. How much time do you dedicate to educate people on what you have? Right? Just compare like how much is it compared to what you have, and uh again, education happening not that quickly, not overnight, and especially when people facing data privacy training or governance first time, they they just it's like something undiscovered yet, much, and something unknown. And that was that's why maybe you feel sometimes resistance from cross-functional teams. Uh and other things I feel like now it's almost responsibility of privacy experts to make that materials privacy content interesting to people. Then well, then good design come to play, comes to play because uh you can think in different ways how to design your content, your slides, maybe short videos, and it is ongoing should be an ongoing base.

Chris Hutchins: You know, it's it's increasingly important as the the regulatory landscape seems to be evolving towards uh explainability and transparency. And at the moment, it's it's not really at the federal level uh that moving at the pace that it is in some states. So it uh you know, some of the first cases in California, in Texas, for example, that they they've put some things in place for healthcare uh specifically uh that are requiring the the transparency and and the the explainability in order to actually achieve informed consent. And there's you know pending lawsuits about this stuff now. So if if you're not designing with that um in mind, you you really need to take another look at that. Uh so that you can you don't end up having to go and pull something out of production and rebuild it. But that's the risk you take if you're not thinking about those things. Uh 50 states in 50 different approaches is quite possible. It's happened before. So I just harsh on people to make sure that you're looking for that.

Andre Samokish: Yeah, and one more thing about sorry, I'm I just want to add one more thing to this question. I pop up in my mind. Uh some time ago, I believe privacy trainings were like something like happening once a year, or just onboarding security privacy trainings. And I feel like sometimes there is a kind of those trainings mostly about security and less about data privacy. So it's just naturally people forget about data privacy. There is not huge retention knowledge about data privacy. So I feel like there is some shift required from just onboarding trainings to ongoing privacy and AI governance. Similar, similar, similar challenge, uh educating people about governance, AI governance. It should be small, small pieces of information in ongoing base, well designed. Think about maybe involved designers who will help you to uh again, cross-functional collaboration who will help you to design your content well and think about it how deliver in an interesting way.

Chris Hutchins: Right. Yeah, I think I think for the there's gotta be a shift in thinking about this from the standpoint of a AI models are evolving and they're they're being retrained constantly. So, you know, we you can't really treat this as a checkbox like like you we could probably do in some instances in the past because the policies just didn't change that much because the the the outputs that the organization is accustomed to really don't change that much in terms of how they're being produced. You know, if you figure most workflows don't change overnight and they they never have. But today that's not necessarily the case. So I think it's a really important uh concept that you're talking about there, because it's gotta be it's gotta be thought of differently, and we've got to have some kind of a process to keep feeding uh the the information to make sure that we we keep current people are understanding you know this stuff and know they know what the risks are.

Andre Samokish: Yeah, that's that's that's that's a big topic, definitely.

Chris Hutchins: What do you think are some of the common misunderstandings that you encounter? Like this AI is magic. Well, they will handle it, we're gonna worry about that. Or it's it's the vendor they've got this. What are some of the other things that you read into?

Andre Samokish: Oh, I feel like I feel like yeah, we talked about this a little bit. Just just feels like uh even if vendor saying everything okay, if you have everything, just go over again everything and involve your teams and use that maybe as for educational purposes, just do it again and make sure that that what they're saying works. And uh if you're planning to use that vendor for long term, just uh go a little bit deeper and again start with business owners, involve responsible people in the process, communicate with vendor, and sometimes on the way you can uh uncover something like new cases, business business have plans to use use uh this tool in different use cases, and how you can know that without collaboration, how how legal or just privacy team can handle that if they do not know business purposes or plans about those tools or uh specific implications. So, yeah, um we just technically cannot handle uh data privacy and a governance by ourselves, and I feel like uh when you're collaborating with teams, it just shows that you have great communication inside company with other teams, and uh that just also add to your product value proposition that everything is clear, even for non-experts, and when they talk into prospects, they can mention and sound like experts about privacy or well-educated people about privacy. Well, privacy is very often about data. So it's I mean, since you have a lot of data, most likely there is some personal data, and people want to know about it. Why not to add to your expertise? We're happy to share.

Chris Hutchins: We talk a lot about the you know the technology and the data, but maybe let's talk a little bit about the the role that uh of tools and platforms like OneTrust versus human process and culture. That's an area that's I think not getting quite enough airtime right now. We're talking really it's the cultural stuff that and the human impacts. Um, I'd love to hear your thoughts about that.

Andre Samokish: Yeah, it's a great question. Uh, and I feel like automation similar to AI, uh serving one purpose to help, to help, to save time, to simplify some very predictable tasks, and so you can focus on more strategic things. Uh, I love automation, and particularly I work in using and have got certification for OneTrust. So uh it's very good practice and says about your data handle and maturity level, how you handle data. So you have one tool, one source of truth about data, for example, about vendors. You have one profile of vendor, all risks, all your communication, all uh mitigation uh measures you implemented in one tool, in one profile. You can generate reports, you can you can have dashboards to see how many risks you have. What is the situation, overall situation about privacy in you? Uh it's very kind of hard a bit harder, I found harder to do with without tool. And it's definitely save time, and even um non-privacy experts, they can, if they have permission, they can go to a specific vendor and take a look. Hey, can we work with this vendor? Do we want to work in the future? What is the risk level? How responsible they are to mitigate those risks, and uh it's just great to collaborate together on on data privacy and AI governance, and just keep in mind that tool like that, you need to most likely you will need to customize a lot of things because every business is unique and to be looking for different to to cover different goals. So once you work on setting up the tool and customize everything, then you can really enjoy with uh flow, workflow, and automated tasks, and your assessments will be going easier, and just you will have visibility to what's going on with your privacy and what situation you have there, and AI governance, and you can share your practices with your potential clients. You can just uh generate reports or uh share a certification, whatever. So it's very convenient, and I feel like it's really says about great leadership support about how you handle your data, AI governance. So things to consider, I would really encourage businesses to have it some some data management tool, whatever you select.

Chris Hutchins: We're kind of getting towards the end of our time. Uh there's a couple of topics here that they're they're they're really related, but it occurs to me that there's probably some some folks listening that are really not quite sure how they're going to embed governance into product and engineering workflows. So it doesn't feel like a blocker. That's one side of it. But also there's there's some skills that I think are probably uh ones that people should be thinking about and prioritizing as well. So maybe you could kind of talk about those those two things. So, you know, first, you know, how do you embed the governance? Uh, but then what are some of the skills that you think need to be developed or acquired if uh if an organization doesn't already have them?

Andre Samokish: Yeah, uh, I feel like it's uh something that almost every business will face uh this challenge, how to build AI governance. But there is one easy component. If you have privacy data governance in place, you can you can transition, you can transfer many controls from there to AI governance. For example, you can handle your assessments uh using some questions from your previous questionnaires. Uh, you can use the same tool, data management tool, to handle AI assessments, uh vendor assessments, internal assessments. And talking about skills, I feel like uh it's very clear you need to have technical things, uh, data privacy skills like getting some certification from famous organizations. But also I feel like we're missing uh in general, it's kind of not something people think about. Even communication skills, right? Even project management skills, how to uh how to communicate to cross-functional teams in early stage, how to embed privacy component, AI governance component to project scope, project plan, and uh how to use project management tools to create that visibility and communication, shareable documents, dashboards, and everything, just to simplify life for non-experts, non-privacy experts and help them to simplify everything. And uh yeah, project management skills will be very helpful. Communication and communication will be helpful for every team because we will be talking and it's okay to talk initiatives in the beginning, giving feedback. I feel like communication skills, uh, how to give feedback and request feedback, request help from experts, different fields. It's it's all about communication, and it's not just naturally you're born with those skills. There are skills you can learn a lot about. Take some courses, take some uh education on that component also would help because cross-functional uh collaboration uh really will help to save time and there is reduce those blotters.

Chris Hutchins: You lead into the probably uh one of the biggest most important points of our conversation. Where would you encourage people to who are listening who want to learn more? Where should they be looking? Or their community certifications, resources, where where would you point them?

Andre Samokish: Share my experience. There is much more resources you can learn from. I mean, um, I got a couple of certificates from IAPP organization, international organization of privacy professionals. And there is uh AI governance certification as well. It's not just about privacy, but they have that. Also, since we talked about automation and implementing tools, I think um OneTrust uh has a lot of educational materials. You can find them, some of them free access, uh, and uh maybe you can start from there. And also, I I personally like how much educational materials you can find on different platforms like LinkedIn or Coursera, or like just just try to focus. There is so much, so much information, just try to focus on what specific area you want to work on. Right. And uh maybe you want to be security experts, it's a bit different from privacy, even if you're working closely. But it's it's funny because when I go to to just conference, sometimes I I hear a question, hey, what do you do? Uh and do privacy, and oh, you're a security guy. There is some similarities, but this is when we need to start to educate people just to for even from simple things. What is the security? What is the privacy? What data are you responsible, responsible for? And um I think this is kind of a great question to people to understand, even like, okay, why ai system needs my data and where like search where to go on business website, where to go specifically to read like how that data is going to be used. So where is it? Where is that component? And it's a yeah, it's uh it's just um uh I don't know. We talked today about or not about shifting to to that culture, culture of uh safe data, and for businesses to understand that can be your value part of your value proposition. You can even emphasize when you have good AI or privacy and both governance in place, feel free to add it to your value proposition because it's working.

Chris Hutchins: Andre, this has been a fantastic conversation. I've learned some things. Thank you. Uh I always enjoy speaking with you uh for listeners. Uh, you you'll find uh all kinds of good information in the show notes. You'll you'll find the anything you need to know about how to reach out to Andre if you have questions. You do you just want to have a conversation and and bit or glean from his wisdom a bit, I'm sure he he'd love to hear from you. But that's gonna do it for this episode. And Andre, thank you so much for being here. I can't thank you enough. It's been fantastic.

Andre Samokish: Thank you, Chris, so much for having me here. And uh your questions just right to the point. And I'm happy that uh you are curious about that, and I'm sure our audience wants to know more about that. And of course, uh, there is experts from different fields who really want to become privacy experts and they want to start from somewhere. And I'm sure they will find implication of the expertise in the privacy field because it touches a lot, and it's also a global and international uh field where like privacy uh once business have presence somewhere in Europe or whatever, it became become international and global, um global thing, and so so much things to learn about privacy and now and governance. So I'm happy to share my knowledge uh with people who want to reach out. I'm happy to talk to.

Chris Hutchins: Excellent. Thank you so much. Thank you, Chris. That's it for this episode of the Signal Room. If today's conversation sparks something in you, an idea, a challenge, or perspective worth amplifying, I'd love to hear from you. Message me on LinkedIn or visit SignalRoomPodcast.com to explore being a guest on an upcoming episode. Until next time, stay tuned, stay curious, and stay human.