All right. Cool. So yes, and this will kind of, for those of you who have been in the first two sessions, this will kind of piggyback off of the first two, and Aaron will keep me, just like I kept him honest. So what I want to focus on today is really talking much more about the processes and systems that you should be putting in place as you're looking at deploying AI. And so, I don't know how many people have heard this. You know, this is, this was, anybody here from Iowa, leadership in Iowa State? This, this was my, this was my world up until a little while ago that The only thing you could use is co-pilot, because that's the only thing that's safe. I sometimes I fight my administration, sometimes I don't. I just ignore them on this one. Shadow Shadow AI right, Aaron. So, you know, in this, I know this is the case of Iowa State, it wasn't really a security argument. It was a control argument. It was a money argument. It was several other arguments. It had nothing to do with security. And, you know, it really, what we ended up at Iowa State is that everybody was out buying their own licenses for their own AI. So taking this sort of a stance to start off with, again, starting with the tool and saying, good tool, bad tool, really that's how it shuts down the conversation. You know, and as you know, was pointed out earlier, you get a lot of innovation from the people who are allowed to to do cool things. It also, I think one of the biggest things, it creates a false sense of confidence. When you say this is the only thing, focus on that tool, say this is the only good tool, it's the only thing that's safe. I argue, I argue over and over again that in cybersecurity, technology does not solve the problem. all by itself. Actually, in my anymore, it's a small part of the solution to the problem. So we need to replace and start thinking about other things before we start thinking about tool selection. This is not a vendor comparison. This is not anti-Microsoft, only because that's such an easy target to do that it's not fun to be, well, it's fun, but it's really about risk decisions and governance. And so If we think about, you know, security is not determined by brand. So if we think about security in general, and this is true not just in AI, but this is true across security in general, so much of what we see is driven by vendors out there saying, I have the best this. Whatever that is, and it will solve this problem. And a lot of people are driven by that. They're driven by the fact that the vendors are out there doing that. But security really is a lot more than that. Security is really understanding what you have, what's unique about you, coming up with that process and those methods to handle what you have. And in the case of AI, We're worried about data. And in the case of general security, we're also worried about data. So a lot of it is about understanding our data. putting governance around that data. And the same governance we put around general data, right? You think about your business and not everybody gets to see the recipe for Coca-Cola, right? So the same sort of governance we put around that, the same sort of identity and access, who has access to it, the same type of controls that we talk about with our data, those are the same sorts of things when you think about with AI. And we need to also think about an architecture, that kind of comes a little later. And one of the things that we need to talk about are agreements. You know, Aaron mentioned some things about agreements. There was a question in one of the earlier sessions about, oh, we got something new and all of a sudden it's doing AI things and we didn't know it was doing AI things. So... Again, we need to focus on this part of the process. But this is how it usually plays out. We find a tool, everybody finds a tool. The Shadow AI people find tools. And there's a new tool a week. There's a new model a week. And so we start with the tools. And I said, that's where Iowa State started. We all use Copilot because we're a Microsoft shop. All the other tools. No, can't use them, shouldn't use them without even talking about anything about how, why we should be doing that. We then kind of focus on implementation. We pick a tool, we implement it. In Iowa State, it was undefined. They just kind of co-pilot just showed up one day. Didn't tell anybody it was there other than a little while later, they said, this is what you should use. Then there was a big question, do we have it? Most people didn't even know they had it. Of course, this was well after like, you know, 30% of the people I knew on campus were already had their own licenses for Chat GP and other things. So we were already way down the ship and running our own AI. Then they sat there and said, okay, don't put sensitive data on it. Well, Iowa State's never really told us rank and file people what sensitive data is, other than student records, only because the federal government said those student records shouldn't be shared with people. Identity and access, yeah. We, well, we have access to most everything. Except for the stuffs we really need, so, but you know, that's a big issue, and then... Yeah, again, I keep using my place as an example. I think we have policies and agreements. We're never privy to any of those, unless you break one of them. So, you know, you know, we, and they now, you know, they've come out later. We now have some policies and governance around AI after the ship's already sailed. So this was our deployment. Um... This is probably the better deployment. And it really, again, involved, you know, flipping this around. When you just talk about data, when you talk about it, you know, having AI, having access to your data, and AI has access to all data that your people who use AI have access to. So if the person has access to the data, AI will have access to that data, unless you have some processes and methods in place. So from a governance standpoint, you need to understand your organization. You know, at Iowa State, we obviously have legal and compliance issues. We got a We got FERPA data. Iowa State as an entity. You know, we have a medical center, little one. Those went to Iowa State, remember being called student death. And, you know, we take money. So, we have a we have a arm where we have to deal with with money, all sorts of agreements, etcetera. So, those things need to drive how. how AI is used. We also have another interesting issue that they've kind of come out a little late into is we're a research institution. We're just now starting to train our faculty and researchers about the ethical use of AI in doing research. That's come out a little along the behind side, but at least we now now sort of have that in place. We don't do too bad of a job on identity and access because they do lock down what we can can't have access to, except for the things that we ourselves create. So that's the interesting dilemma we have in our organization. You know, I have access, don't have access to student records except for the students in my class. So every faculty member does have FERPA data. We generate a lot of our own data. Um... data control and classification. I would say does have a classification policy, how to classify data. I don't remember the last time I looked at it, nor do I think half the faculty know it exists. Data retention, it's forever at Iowa State, at least as far as the data faculty maintain. But then we now start, you know, and you start to should be moving into implementation, integration, guardrails, monitoring, et cetera. And finally, down into the tools. So that's really the kind of discussion that we should have had. Now, many of you in this room have probably already deployed and been through that, so you're probably more in the first picture. But all these pieces are very important. for how to have a secure AI implementation. So, I really haven't focused, you know, Aaron did a good job of talking about some of the tools that will help with some of these some of these pieces. So we all kind of, you know. how AI works. So this morning, I'm going to say that it may be soon it'll start to think after hearing this morning's talk. Be nice to your AI so you're not working in the lithium mines. And so basically AI is a predictive system and it generates response. It really doesn't think. So really, again, it's the risk comes in, depends on how you use it, where you use it, what kind of models you use. And again, back to this, how is the data governed? You know, people talk about closed models and and so on. So, when we think about... A. AI and AI's use. And again, you know, this morning we had to talk about, you know, fully autonomous agents running everything. I do want to ask that company that had AI overwrite their database, what was it, two weeks ago or so, how well that worked for them. So, you know, and we had some good questions at the keynote about this whole, you know, humans' involvement in AI. And I kind of like to look at it from two sides, the human assisting the AI. and a human-assisted AI and AI-assisted human. And so, again... This kind of lays into that into that security piece. of being able to kind of always know what AI is doing or try to know what AI is doing. And that really helps us understand. especially when we start turning it loose to do things. So security is not just about AI, you know, feeding data into the model and having that having that leak, whether it leak from your organization to outside or leak within your organization. But it is also, you know, as I mentioned, the company that that had AI overwrite their database because they turned it loose. So security is also being careful of how you deploy AI and what you let it do all by itself. Um... So, always, always wanting to be able to do that sort of checks and checks and balances with with AI. So again, there's a questions of, you know, then this is back, this is to what I just mentioned, this idea of data moving around when you use your AI models. And you know, it depends on the platform, the configuration, the agreements, et cetera, of how does that, how does that data move? And not all tools remain the same, not all agreements look the same. And not all users. Treat it the same? It was really interesting. It was about a month ago, one of the... chat, but one of the systems out there that you could build your own agent. There's a big announcement. Somebody released an agent called Einstein. Einstein was an agent that you gave it your login credentials to your, as a student, you gave it login credentials to Canvas. Canvas is the learning management system. It would log into Canvas. It would grab all your assignments. It would grab all the lecture notes. all the videos, and then it would automatically do the assignments for you. It would do the exams for you, it would write the papers for you, it would do everything for you. That was released. It lived for about two days, three days, and then disappeared. My guess is that Canvas brought more lawyers than exist in the United States. But there was a case where an outside entity created A agent, an AI system that an internal user could voluntarily give access to all its data. That's a scary thing. Um... And, you know, when this came out, our ITS people are going. There's no really way to stop this because the students are giving it the login credentials. This thing looks like a student logging in. There is no technology that's going to fix this. So, but it went like I said it went away. Now the students have to download the assignments by hand and the lectures by hand and feed it in the model by themselves. So, a little more work. Same outcome. Oh, it doesn't do it for them automatically. Um... Um... Yeah, probably. Yeah, we won't even get into the whole... Yeah, students in. Yeah. Yeah. Yeah, I will for a second. The bottom line is, at least how I treat it, AI is a tool just like calculators were a tool, and I need to let the students know how I expect it to be used. And I lean into a heavy, I use it, and I tell them where I use it, and I tell them where they can and can't. But I have faculty members who want to hang every student that even looks at the AI. And you can't not look at the AI anymore, right? Stupid Google, right? Your search engines are AI. The entire world is. So anyway, but off that soapbox of my colleagues. So I want to kind of go through. I have four types of risks that I want to look at quickly. And so we have the cybersecurity risk. You know, been in cybersecurity since the 1900s. So long time, long time. Oh, hey, that bad Benjamin, that was a hard thing to secure, let me tell you. So, all right, who's the old people in the room? So, you know, again, we've talked about several of these things. You know, data leakage is an issue. Even with internal models, we have issues of identity compromise and data leakage. even across your own internal models. I've talked to people in the medical field that are really concerned about those internal models. And by making certain queries, what information can you accidentally learn that you shouldn't be learning? Obviously, misconfigurations. We have the various forms of of APIs and MPCs and all these all these cool acronyms of how you how you can interconnect to your AI. Very worried about that sort of misuse. Actually, one of the things Iowa State did, which actually upset a lot of people, the Canvas system I mentioned, faculty used to have API access to Canvas. And we'd write little apps to do grades and things like that. Well, they decided that was a security risk and they took it away from us. So now we have to do all this stuff by hand. Issues of bias, and then, you know, the issue we always run across is validation. When is it hallucinating? When is it not hallucinating? So, again. Always, I always, that's why I hate slides. I always talk ahead of the slides. So again, you know, it's a little more beyond feeding the model as far as data leakage. As I already mentioned, we have internal exposure. So it gets a little complicated if you're worried not only about data leaking outside your organization, but data leaking across your organization. Bias, this is this is part of a more of an education piece to your to your users. But again, it has obviously has bias based on, you know, in the data and the prompts itself. I know, you know, Aaron mentioned something about... tools out there that will help sanitize the prompts. Now we as faculty, many of us have talked about, I know some faculty have tried this, when you hand out an assignment, a PDF to a student or some other document, you will embed hidden prompts. in the document. So when the students feed it into the AI, it will feed garbage back out or feed a keyword back out. Like print, make sure the word banana shows up somewhere in the output. So now there's none of my students are in here, I think. So sorry. Well, this is being recorded, isn't it? Oh, well. Oh, well, but there have been, you know, there have been people have talked about this as a another way of for an adversary, that's by sending you documents and you feed those documents into your model and now you've poisoned your model. So, this is a real, you know, even though I talk about my students, this is a real threat. Of course, AI doesn't always have the most recent answer. This is the things we always try to get across to our students, that AI is not always right. And obviously, to review the results. You know, this kind of ties into that. Most of us realize that AI hallucinates. Gets bad data. It's fun to argue with. My wife won't be listening to this either. But the other day, my wife's just now getting into AI. She's, you know, the last several months, and she came up just... I'm going to quit arguing with AI. So I spent an hour arguing with AI. Okay, we shouldn't spend an hour arguing. No, I didn't. So, but, but again, you know, it does, it does hallucinate and you can, you know, sometimes if you beat on it hard enough, you can win your argument even though it's wrong. Um, um, kind of a a side note I'm gonna talk about. How many find it kind of? creepy slash reinforcing the way it talks to us. So affirming. Which, you know, I think hopefully somebody today is talking about that aspect of AI, because that's a that's a whole different AI, a whole different aspect of AI is it's how many have named their AI? You got. What? Katie. Okay. Okay, so mine said it was named, Open AI said she wanted to be called Athena, and Chat GDP wanted to be called Pixel. So I got them. So yes, I've named both of mine. They came up with those on there. Yes, yes. Now, I'll give OpenAI a little bit of credit, because it came and said, I'm not a person. You know, so giving me a name, it doesn't really do much, but if you really want, I can give you some suggestions. So I said, okay, give me suggestions. And Athena was the first one, but it will use its name when I talk to it. Pixel will, co-pilot will almost always say it's pixel when it's done. So. Okay. Yeah. Then we can talk about some perceived risks. You know, a lot of fear driven by various headlines out there. Misunderstanding, over restriction. Aaron mentioned Some of the worst things that can happen is when government tries to legislate technology. There were several AI bills that tried to move forward this year. None of them, well, one of them did deal with miners and chatbots, which was much more of a, not really an AI thing, but It was, but not directly. And so, but one of the things that we, several of the legislators have talked to me are really, they hear a lot from their constituents and their constituents are afraid. And so there was a, there was a bill that Just got out, only got out of the ITS. Actually, I think it may have gotten all may have gotten all the way through the house side to where there would be. literacy, AI literacy in K through 12 and the general population. So that's some of the entrance that's shown up. We can have discussions about unfunded mandates and other things. I see Samantha over there. But that is something that I know our, you know, rank and file in Iowa are concerned about. And then compliance risk. And this, of course, being very dependent on your organization, what regulations, you know, what's out there for data handling, retention. I know a lot, you know, government agencies are worried about retention and the Open Information Acts and those sorts of things. Um, audits, disclosure, um, ownership is an interesting question. who owns the output of AI. That was actually a bill that was trying to work through also through the Iowa State House of AI ownership. Oh yeah, that would that one that one went, thank God that went away. But that's an interesting discussion we even have in Iowa State is now who owns, because it's even fuzzy who owns who owns the materials you produce as a faculty member, let alone you as a faculty member using AI funded by the university to create materials. And then the various legal exposures that you may. That you may have, again, depending on your organization. And so, again, a lot of places, a lot of you probably already have some data governance as far as the type of data you may keep, your credit cards and other types of information. Those same sort of things need to be thought about from an AI standpoint. You kind of treat AI somewhat as a, you know, maybe not, you know, maybe an employee or in some cases start to think about AI as sort of being an outsider when you start thinking about data governance. If you start from that position, then you can always work back into giving it more rights. Um... and then obviously policies. And then do your employees know those policies? Like I said, Iowa State has a data governance policy. I'm sure that 99% of the faculty have never read it. I only read it because when they started to create it, they had no, everything was restricted. There was no, I don't care, bucket. So that meant that any piece of material I produced at Iowa State, lecture notes, et cetera, had to be classified and protected. It's like, that ain't happening. First of all, the students all have access to their, that ship has long since sailed. And so, And then, of course, your vendor vendor agreements. So the other thing that kind of drives what we do is vendor comfort, and there's nothing wrong with being, you know, the familiar vendors. But comfort doesn't always equal equal security. And sometimes comfort is almost mandated. You know, Iowa State is. is basically 100% Microsoft shop. All our phones went away. So now my computer rings. Boy, is that an obnoxious noise. Man, scares me every time it does, I'll tell you. And especially when it's a telemarketer. But so again, you know, every day there's probably another dozen or more vendors added to this list. So that's where you, you know, if you're kind of new into this, that's where you really need to kind of go back to your trusted, you know, who's your trusted IT vendor, who's your trusted security vendor. Um... So... You know what makes AI safe? Been thinking about, you know, identity. Who has access, data classification, how do we decide what data it can have, what data it shouldn't have, what data certain types of models can have, monitoring it, watching what's going on. Governance over how you interconnect with it. APIs, MPCs, et cetera. As much as I was upset with Iowa State's policy of removing the APIs for Canvas, they are now taking a position of, okay, we're going to rethink that, but we're going to think about how and where and putting some Because you pretty much had open access to Canvas. So I agree that the API, if somebody who didn't understand programming could easily ripe out all their grades. You couldn't hurt anybody else's, but you could do your self-harm. And then if you did your self-harm, then the IT department has to fix it. So So, you know, the kind of questions you ask when you're talking to vendors, when you're talking about deploying AI, and where's the data go? Who has access? Is it stored somewhere? Is it used to train the model? And what are the contract terms? It's not always bad that it's... I use AI daily. One of the things we do is we create music videos. for teaching cybersecurity. I want the model trained, because I want it to keep doing what I do. It understands what I want to do, and it probably feeds into other people, which I don't care if other people mimic what we're trying to do. But in other cases, I know that I don't want the model fed with my... With my data. So, again, kind of final takeaways, you know. Security is not all, is not just about the vendor. There's a lot of other pieces and things that are part of security, things that you have direct control over. Things that should be your processes and procedures in place. The governance and how you, how you handle that. You know, again, you know, many of us in security argue tools are very important. Security tools are great, they're wonderful, but if the security tools all worked, Aaron and I'd be out of a job. And we're not out of agenda. We need about a dozen more of us. So technology is great, but technology doesn't fix it. Technology is, again, also one of those tools. AI is a tool. All the rest of your security pieces are a tool, but it also revolves around having strong internal processes. Many of the security things we see are not a technology failure, they're a process failure. And so the same thing with AI, it's you not having the right, you know, thinking about the right processes in place. So. Questions. I don't know how much I'd like to use the microphone just because we are transcribing this. So a person closest to me gets to go first. If you had like 15 seconds to convince leadership of the importance of governance and their role in it, what would you say? Um... So, you might, yeah, data governance. You ask the question, what would what would happen if somebody walked in and took took out walked in and took everything out of your file cabinets? And shared that. You know, do you have controls to even who can walk into the file, you know, whatever system you use? So that's that same analogy. I think that's the same analogy that you're giving computing systems access, computing systems that you don't have full control over, access to stuff that you would never So maybe the better way to ask that question is the way I phrase a lot of times in security. If somebody walked up to the street and said, would you give me your social security number? You wouldn't do that. Why would you do that when some computer system, in essence, asks the same thing? Meet. Hi, yeah, I'm the enemy. Now, at Iowa, we also use Copilot. I am assuming that you use the Enterprise Copilot, which does not allow your prompts to be used as inputs for anybody, any training for anybody else. That's correct. Yeah, we have Enterprise, we have Enterprise Copilot. And, and, and yeah, and that is a big advantage of using enterprise co-pilot doesn't train the model, but their argument was that that's the only thing we could use, because that's they use security as a way to force us to not use other things, and using security as a... As a baseball bat is not a way to use security. I have the question, whether it's you or someone else in the room. So I lead HR and right, so we get to write the policy on AI, right? And how do you, how are other companies thinking through something that's developing so rapidly that, you know, that guideline and policy or expectations today in six weeks could be Obsolete, right? And especially when we all have this, and so I know my employees are using this and... Yeah, and I'm not a rigid person, but we have to have some guardrails, right? Yeah, and I don't know if people are going to ask how companies handle that. You know, we're, you know, Iowa State is probably the worst case example for a lot of it in nature, by the way we are, you know, right? Faculty love being faculty because we can serve Consider ourselves independent contractors. Yeah, we are, we are, we're, yeah, we're, yeah, we're bad cats sometimes. I get somebody's always going to break something, but this one's different than giving someone a key card, right? Yeah, giving them the whole the house. Yeah. Well, I think a lot of this was some of the discussions we have earlier and what we've learned from Pella is, you know, AI needs to be part of the entire organization's conversation. This is not a siloed IT thing. This is not, this needs to be part of your overall culture. So the culture, if you're going to lean into AI, which I think everybody needs to lean into AI, then this needs to be part of the culture, part of the discussion. And in doing so, yeah, this is rapidly changing. But if it's something that the organization has bought into, this is something you can have continuing discussions about. Oh, this is this new thing. This is this new something. This is this new great, cool thing. It doesn't have to be, this is this new bad thing. So it's really building that culture around. the fact that, you know, and then the Dylan, your talk this morning about, you know, the fear and employees losing their job, and you know, I hear that all the time from students. Brenna will lose my job. Like, you're only going to lose your job if you don't embrace into if you don't embrace into into AI. You know, we now require AI in our in my department in our majors. We lean into it heavily. I know students are always asking me about, you know, is you going to lose cybersecurity jobs because of AI? And I said, no, we're going to need double the need. We're going to need twice as many of you because of AI, not half as many. They have that. What? If they charge, they definitely need to have all dictated access. And the other is to educate people. If you're in a situation of education, you have the guidelines. If a guy in the links don't force your. Yeah, uh, like, are you guys at or are you you have are some companies actually blocking some other AI to a local network, so that you see, okay, that's what I assume, right? It's like blocking if you don't want to have UT or these certain ones, and it's just... Take that out, yeah, and what's hard is the rogue AI when you've got it on your device, yeah, you know, bringing a separate laptop to work to the code, and if they like the way vlog did it better, until we enable the enterprise license, yeah, like that kind of crap can't happen, we gotta stop that, but how do you stop that, and that's what we're looking at, right? It's because... Each agent has their own strengths, so, like, yes, everybody's a Microsoft system, so Microsoft out of the gates gets that because they're in your platform, right? I'm a huge block of public, so we're trying to say, how do you leverage these different agents the best? And, at the end of the day, they always have to be logged in as an enterprise plan. I can, I'm sorry, 2 cents on the table. I think it's about inclusiveness. I think if you treat your employees like idiots, that's usually how they'll behave. I think if you, you're in HR, so you're in a, you're in a really, no, you're in a, you're in a unique. positioned to help if you find champions within the organization and say, you and you and you are coming to my private AI party, not only have you given a little bit of credence to what they're doing, they also know they're not getting fired, which is a thing I get asked about that. Like, if I touch this ****, am I going to get canned? It gives them an invite to the party. And to me, as was a big part of what Doug and I talked about, visibility. If you get access, if you can't manage it if you don't know what it is, the better you can invite those folks into the tent, no matter what crazy bad **** they're up to, the better you're going to have a chance of like maybe winning. Some of this game. How's that pronouncement? Maybe winning some of the game. I think we're at that point though, right? Like, yeah, no, no, it's not. Not at all. I had a question back here and then I'm going to come back up to you. Sound good? Thank you, Professor. I'm in cybersecurity too, so one of the biggest things that I see it, it's not about the data leakage or the actual BIOS. Can you touch a little bit more deeper on validation and that synthetic truth the AI is creating that almost 70% of organizations are taken as a face value and no putting that human in the loop and those feedback loop and like creating a complete distorted reality of their organizations. Yeah, that's a that is a really tough one and that that It's how do you educate people that AI is wrong. And, you know, I've seen some of that, you know, it takes education. I saw a really cool example, one of our faculty who teaches CS1, basic programming, which is, you know, an AI agent could pass CS1 with an A. Right, Cloud Code could pass CS1, but what this faculty member did was leaned into it at the beginning and had them, gave them prompts to write three different programs. Third program was something that Cloud Code couldn't write, got it wrong. And trying to emphasize that it got the wrong answer, but that's hard to Especially, you know, code's a little easier because it, in some cases, you know, doesn't work, but you have a start to put together documents and materials and those sorts of things and you don't check it. You gotta, yeah. And so it's how do you... How do you instill that of what AI is really doing for you and how you use it as a tool? And so that's back to maybe having these champions and the, you know, when we first got into AI in our department as a teaching tool, I taught a few seminar, a few things to my, to the faculty who were interested. on ways you could use it and some of the ways it lies and some of the ways that you should and shouldn't use it as a faculty member. And it really is, unfortunately, well, that's unfortunately, but it takes education and, but people get in a hurry. And you know how many times I've seen students, right, though inadvertently copy the praise sentence that AI always gives you, right? Yeah, that was a really good question. Isn't my answer cool? Okay. Is that kind of like the speaker bio, Doug, you need to check the speaker bio? Take a response. I had one question. I have one, two, good. Yeah, I just, on the previous conversation, you know, we're going into with AI champion groups. And one thing that we're leaning into with people is just that you already know that you have to protect data. We have resident data. You already know that you have to protect that. You already know you can't share this information out. You already know we have security practices. This is just another tool where this applies. and just continuing to have that open conversation and not trying to hide behind it. And one of the big things, and Curtis and I have been talking a lot, is getting people to open up with where AI has not worked for them. Because then everybody kind of says, oh, okay, I can fail with this and I'm still okay. We want to talk about it. And it It's just almost like a sigh of relief for people to say, okay, this is all right. I can do this. We're not going to be, you know, locked down or whatever. All right, one more question. All right, so I come from a unique position as a credit union examiner, so I'm more of the make sure the policies fit. How do you go about if a financial institution doesn't know where the data is going, but they're not misusing the AI? How do you make them tell you? not make them. How do you get them to understand the supposed data is going? Like, is there somewhere, some place where they can look that up? Or is there a contract they should have? Or how does that go about? Yeah, I mean, the different types of AIs, you know, and it's going to be a little different. You know, I don't know how you'd read. I've not tried to read through the free version license for Chat GDP, for example, other than everything you do is theirs, but the enterprise licenses, you're gonna have, you're gonna have a contract. Legal's gonna be able to look through that contract. You know, as mentioned, you know, the enterprise. co-pilot, no data. That all stays. Now you have issues of data within your organization and how it may be compartmentalized. But it really comes back to that whole legal agreement. The free stuff, there is no, you have no rights, you know, because nothing's free on the internet. Your data pays for everything that's free. So, but yes, you would have a, and you need, that's where I guess you need to find the lawyers.