In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the importance of data privacy when using generative AI for marketing. You’ll discover the risks of blindly trusting AI vendors and the critical role of transparency in building trust with your audience. Learn why a robust AI responsibility plan is not just about legal compliance but also about protecting your brand reputation and ensuring the long-term success of your marketing efforts. Discover actionable steps for evaluating AI vendors and establishing clear accountability within your organization to mitigate potential risks associated with data privacy.
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Machine-Generated Transcript
What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.
Christopher Penn – 00:00
In this week’s In-Ear Insights, this is part three of our series on responsible AI. Today, we want to talk about one that a lot of people have questions on. This is all about keeping our data safe, protecting sensitive information, and building trust. One of the things that I’ve said for a while is transparency is the currency of trust. The more you can show people what’s happening to them, to their data, etcetera, and how you’re using it, the more easily you can build trust. So, Katie, when you think about the use of generative AI and the questions people have about what’s going on with my data, what have you seen and heard?
Katie Robbert – 00:49
I mean, the things I’ve seen. For those who don’t know, I actually started my career in a highly regulated space where I could at one point recite all 128 encrypted characters of HIPAA because it was such an important part of the work that we did—data privacy. And so you have HIPAA, you have COPPA, you have CCPA, you have GDPR—you have all of these regulations meant to protect someone’s personal information against the internet because it’s ours. We get to choose what we do with it. The challenge that I’ve seen in general is that companies are so hungry to get their hands on that information so that they can target individuals down to their specific behaviors and purchasing patterns. Companies that do that are just irresponsible.
Katie Robbert – 01:52
When you talk about using generative AI, there is a lack of education and understanding about how to use AI responsibly to then also protect. So let’s say you work at a company that does actually deal with personally identifiable information. Let’s say that is what you do and you’re looking to use generative AI. Unfortunately, mistakes happen, and people will sometimes take that PII and put it into an open model—being something that I pull up Google Gemini.com and then I just start giving it my data. That’s an open model because it’s basically training itself on the data you’re giving it, and it’s being accessible to anybody, everybody who’s using it. I mean, I don’t even know where to go with this other than it’s a big deal.
Katie Robbert – 02:55
So, first and foremost, you need to understand as a company what data privacy means to your company. What are your policies? How far will you go to protect the data that you collected above and beyond just clicking the box on pieces of software?
Christopher Penn – 03:16
Well, to no surprise, data privacy follows the five Ps. When you’re using generative AI, what is the purpose of it? What are you trying to do with it? Because it’s very possible, depending on what you’re doing or what you want to do, there is either a hybrid solution that uses private models versus public models. There may be stuff—it may not even be a task that’s well suited for generative AI. You may not even need to use generative AI for that task, depending on what it is. But if you’re not clear on the purpose of it, then the data privacy part is going to be perpetually unclear. So that’s the first part. The second part is, what are the existing processes that you have to abide by?
Christopher Penn – 04:04
One of the reasons that data privacy laws and regulations are in place is because companies in a capitalist environment tend to optimize for the most profitable activities. The most profitable activities are not necessarily the ethical ones because ethics is not legally binding. So we do have, as you mentioned, GDPR and CPRA and HIPAA because governments have said, “Yeah, you’re all behaving badly. We have to protect the rights of individuals, our citizens, and therefore, these are the bare minimums that you must comply with or else.” Then it’s up to individual companies how much additional harm reduction they want to do with customers’ data above and beyond what is legally required. But you’ve got to start in the process section by saying we are required to legally comply with these things, and if we don’t, then we’re going to get our butts sued.
Christopher Penn – 05:00
And this extends to generative AI and how you use it.
Katie Robbert – 05:04
Yeah. And I think that’s, again, sort of one of the misunderstandings—you can’t blame the software. People will try. People have, they’ve said, “Oh, it wasn’t my fault, the software had a—” so they’ll point back to whoever created the software. And this is something, Chris, that you and I have actually done when we’ve gone to events. I recall we walked through the vendor floor at an event—this was maybe four years ago—and people were starting to introduce AI into their systems. And you were asking, “Well, what does that look like? How is it built?” And I apologize if I don’t remember all of the details, but I remember that a lot of the vendors didn’t know what it looked like. And you were asking, “Well, what does it look?
Katie Robbert – 05:55
How do we—what is the responsibility of the user? What is the responsibility of you?” And the general response that we got was, “Well, once you purchase the software, we are no longer legally responsible for what you do with it. You as the user have to set up all of those guardrails in terms of data privacy, in terms of what it will ingest, what you won’t, so on and so forth.” And that was really surprising, that they were so hands-off on their own software, knowing that people were going to be making mistakes with it.
Christopher Penn – 06:37
It is the assumption that the software is a tool, which is correct, and that the tool in itself does nothing. You can’t constrain every possible use case. It’s like if you manufacture a chainsaw. Yeah, there’s some basic safeties that the thing should have, and then what people do with it after that depends on the user. And there is some level of truth to that. We talked about that back in our episode on Ethics, which you can find on our YouTube channel, where consequentialism is—the outcomes determine whether something is right or wrong. That said, I think you could make a very clear case that saying, “It’s up to the user,” is the least possible work that you could do. And it doesn’t really reinforce what is trustworthy and responsible use of AI.
Christopher Penn – 07:24
You’re a company that says, “Hey, good luck,” is essentially saying, “We accept no accountability. We accept absolutely no accountability whatsoever. We don’t care about fairness. We’ve given you the thing, you’re responsible for fairness, and we don’t know the answers to what’s even under the hood in our software.” So you’ve already failed three out of the four elements in responsible AI. This is not a good start. If you are that company that is making a tool, or if you’re that company buying the tool, you’ve got to go pillar by pillar and say, as we talked about in episode one, what are the values that our company publicly says? “This is what we stand for.” And does our use of generative AI align with those values? Two, when we’re dealing with sensitive data, who is accountable for—
Christopher Penn – 08:15
And surprise, like you said, Katie, it’s everyone’s job. Data security is everyone’s job.
Katie Robbert – 08:22
Everyone.
Christopher Penn – 08:24
Don’t hand your password to people. Don’t keep your password on a sticky note on your monitor. Don’t do all the basics.
Katie Robbert – 08:35
It’s just me at my house who’s going to find out.
Christopher Penn – 08:38
With regard to generative AI, knowing the difference between a public model and a private model—if you’re using ChatGPT, that’s a public model unless you’re using the paid version that is specifically locked down, and it’s a private model somewhat—that is accountability. You, the user, are accountable, but so is OpenAI to some degree to make it easy for people to know when they’re in a safe environment, when they’re not. Fairness is less of a concern with data privacy. That’s more of a thing on bias, which we covered in the last episode. But transparency is 100% about data privacy. What is happening with the data?
Christopher Penn – 09:19
And if you are, as a company that is using AI, as you, as an employee who’s using AI, or you, as a vendor providing software for these AI—if you are not 100% clear, “Here is what happens to your data step by step through the system, and here’s how we protect your data,” then you can’t say that you have transparency, and you will not build trust in the use or the deployment or the purchase of AI in the ecosystem because no one will know what’s happening with their data.
Katie Robbert – 09:53
It strikes me that this is also a really good framework for companies that are evaluating vendors. That’s a question we hear a lot—how do I evaluate an AI vendor? Well, this is a really good framework to start with because if they can’t tell you what role they play, if you go through your AI responsibility 5P framework and you come up to the section of, “Well, what happens if it goes sideways and we get sued?” If the vendor you’re selecting is like, “That’s not on us,” they have to explain what that means and why. And then you, as the company, again, it goes back to how much risk are you comfortable with?
Katie Robbert – 10:36
And so as you’re evaluating things like using large language models, look at the company’s—Claude, Gemini, ChatGPT, whatever it is—but also the software that you’re using that has AI built-in—your CRM systems, your social scheduling platforms, your marketing automation systems—because that is the same exact technology that you’re using when you’re using an open AI, a Gemini, a Claude—there’s no difference in terms of what it’s doing. So taking a look at those vendors and saying, “If I get sued for making a mistake with AI, what role do you play? Because you provided the software, where does that fit in?” And that goes into your AI responsibility plan.
Christopher Penn – 11:28
This is one of the reasons why almost no public AI vendors have achieved things like SOC 2 compliance or ISO 27001 compliance stuff, which are mandatory certifications for a lot of high-risk applications because they can’t explain, “Hey, here’s exactly how this data is going to show up.” Or, “We can guarantee in this environment that your data is never going to be leaked.” It’s one of the reasons why certain three-letter agencies of the US government have not been able to use generative AI because they can’t trust that a vendor is going to keep its word.
Christopher Penn – 12:06
And it wasn’t until Meta released the new Llama model where an organization like the CIA, for example, could build it on their own hardware in a basement—five basements deep somewhere in Langley, where there’s a machine that’s not connected to the internet at all. They can run generative AI in that environment because they can guarantee the privacy, the security, the—making sure that only people who have access, who are supposed to have access to that system, can. Companies are the same way. One of the things you need to be aware of is what are the requirements for data privacy that your company has, period, AI or not. And then once you know that, you can then say, “Okay, well now, of the different AI solutions that are out there, which ones meet these non-negotiable requirements?”
Katie Robbert – 12:56
And that’s not to say you can’t trust software, you can’t trust vendors. Your responsibility is to be very clear on your expectations of them, and their responsibility is to meet those expectations. And if they don’t, they need to understand what that means for everybody. So it’s not that you can’t trust software, it’s not that you can’t trust the vendors. The responsibility lies on that transparent communication of what that looks like. So if we suddenly started dealing with a lot of HIPAA data or PII or PHI or whatever, we would need to then evaluate all of our vendors to say, “Our business model has changed. We’re now doing the following things. Can you meet our expectations?” And if they say no, great, we need to go find one that does.
Katie Robbert – 13:52
But going into it just assuming that you’ll be fine is a really dangerous game because you do not want the FDA on your ass. Like, trust me, you don’t.
Christopher Penn – 14:06
And this goes to, the A and the T of RAFT—accountability and transparency. Who is accountable throughout the value chain from the moment you get a piece of data to the moment that data is processed in some way? Every component, every vendor, every system has to have a trail of accountability to say, “This is exactly what’s happening.” There’s some amazing software out there that could do incredible things. But if you can’t say with certitude, “Here’s what’s happening to the data along the way,” you can’t use it. It’s one of the reasons why we remain staunch business partners with IBM because IBM’s WatsonX AI system has accountability built into it, so granular that if someone ever subpoenaed the system, they’re like, “Oh, here’s the 28 CDs of data of the trail.”
Christopher Penn – 15:02
But that’s what’s needed to be able to provide ironclad trust. I can account for this piece of data from beginning to end.
Katie Robbert – 15:11
Now I can sort of hear the collective sighs and eye-rollings of, “But I’m just using it to create social media posts. I’m just using it to create blog content around SEO best practices. Why do I have to get so in-depth with it? I’m not using PII. I’m not using—I don’t need data privacy.” That’s not true. Even if you’re just using generative AI to write a blog post about best practices for SEO, you still need to make sure you know what’s happening with the data because I’m going to be honest, if you are giving Gemini a prompt, say, “Write me a post about SEO best practices,” your post is going to suck because it’s generic because it’s not taking into account your expertise.
Katie Robbert – 16:14
So this is where you start to get into data privacy because you, if you’re doing it right, need to be giving the large language model information about your expertise, your company, your case studies—all of that falls under data privacy. What is the machine doing with the information you’re giving it? Now, go ahead, give it a prompt and say, “Write me an SEO best practices post.” You and a million other people are going to have the exact same content, which is bad SEO, which is totally ironic. So when you start actually giving the large language model information to work from, that’s where, even if it’s not PII, that’s where you fall under the umbrella of data privacy. Do you want someone who isn’t you to know what you know, to know that information, to know those case studies about your clients?
Katie Robbert – 17:08
That’s what you have to decide.
Christopher Penn – 17:10
And to that very excellent point, I would add having that transparency and that accountability. Here’s the data we fed to the model, and it produced this output. If someone comes back to you later and says, “I think you plagiarized from me,” you have that lineage, you have the proof. “Here’s all of my stuff that I made, that I gave to the model that produces things.” Perhaps it’s you that plagiarized because I have documentation, I have proof, I have a chain of evidence that says, “Here’s the data that I use as my source data that created this post, and I can prove that I got this. This is where this comes from. This is where this comes from.” It’s risk mitigation. It’s the same as any form of audit trail.
Christopher Penn – 17:56
If you’re using generative AI just to summarize your existing stuff, you have that chain of evidence to prove to anyone who asks, “Yeah, this is actually mine. And oh, by the way, because it’s a fully derivative work, I retain the copyright on it even though a machine summarized it.”
Katie Robbert – 18:16
What are the odds that companies are actually going to take these steps?
Christopher Penn – 18:23
The odds increase with lawsuits.
Katie Robbert – 18:28
That’s the unfortunate truth.
Christopher Penn – 18:30
Yeah. It wholly depends on lawsuits and how much risk a company is willing to take. And so the 5P framework, the RAFT framework—all these frameworks are mitigation strategies so that you can go to your CFO or your COO or your chief legal counsel and say, “Here’s the good faith efforts we are making to prevent risk in the first place. We want to be able to say we use AI responsibly.” Yes, it’s a nice marketing piece of language, but fundamentally— “Chief Legal Counsel, here’s how we’re making your life easier by averting or reducing the work that you’ll have to deal with in a lawsuit because we’ll be able to say, ‘Here is the lineage, here’s the providence, we can prove that we did the thing.'” And then they’re like, “This is great. I love you.
Christopher Penn – 19:25
I’m going to buy you a cup of tea for this because this makes my job easier.”
Katie Robbert – 19:32
I think the point that you’re hitting on is, as the use of generative AI increases, like any software, you should probably have a legal representative. You don’t have to have a whole legal team on retainer. A lot of lawyers will work one project at a time. But it is for any piece of software. Generative AI is just another piece of software. Have someone with legal expertise review the terms and conditions and the agreement that you’re signing up for when you’re using this software. Don’t use generative AI as your legal stand-in. As we’ve discussed in previous episodes, there’s a lot of bias, and generative AI is biased towards itself. And so that’s something to be aware of.
Katie Robbert – 20:27
So, as you are integrating AI into your organization, as you are using new pieces of generative AI software, or quite frankly, any software, have someone with a legal degree review what you’re signing up for. Make sure they understand the services that you offer, the data that you’re dealing with, what your data governance is, what your data collection processes are. Make sure that all of that information is covered, and let them be the ones to say, “Okay, this is how much risk we can live with. These are the things that we can and can’t do. These are the types of software we will and won’t allow into the company.”
Christopher Penn – 21:14
And that goes nicely into the accountability. Accountability is a team sport, and the more people who are on the team, the less risk you have individually.
Katie Robbert – 21:27
Yes, but everybody has to be fulfilling their expectations. So when you say the less risk you have individually, that’s only sort of true because if you have decided that you are suddenly not going to pull your weight, there’s a huge liability on your head, and you’re bringing the entire team down. So do what you say you’re going to do. It’s as simple as that. Just do the thing. Don’t be lazy about it, just do it.
Christopher Penn – 22:05
Yes, but all other things being equal, you want more people, you want more eyes on the problem. And particularly you want more qualified eyes on the problem. Hence our ticker here that says, “We are not lawyers; this is not legal advice.” You do want people who are qualified, looking at the problem and sharing their experiences, sharing their points of view, and helping you with the accountability and the transparency when it comes to data privacy. With generative AI, it will just go better for you if you’re able to include the people who frankly will have to clean up the mess if things go sideways. And that is probably the most important aspect. So when it comes to data privacy, use the 5Ps, use the RAFT framework, and focus on accountability and transparency.
Christopher Penn – 22:58
You will get better results that way than just winging it and hoping that things go your way. If you’ve got thoughts about how you are showing accountability and transparency in your use of AI, pop on by our free Slack group. Go to Trust Insights AI Analytics for Marketers, where you and over 3,500 other marketers are asking and answering each other’s questions every single day. And wherever you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/TIpodcast, where you can find us in the places that podcasts are served. Thanks for tuning in. I will talk to you next time.
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Trust Insights (trustinsights.ai) is one of the world's leading management consulting firms in artificial intelligence/AI, especially in the use of generative AI and AI in marketing. Trust Insights provides custom AI consultation, training, education, implementation, and deployment of classical regression AI, classification AI, and generative AI, especially large language models such as ChatGPT's GPT-4-omni, Google Gemini, and Anthropic Claude. Trust Insights provides analytics consulting, data science consulting, and AI consulting.
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