Privacy Engineers for AI: Protecting Data, Driving Trust

Artificial Intelligence is moving fast—but privacy risks are moving just as quickly. In this episode of the Macro AI Podcast, Gary and Scott break down a role that’s quickly becoming indispensable: the Privacy Engineer for AI. 

So what exactly is a privacy engineer? They’re the bridge between regulators and technologists. Their mission is to embed privacy by design into AI systems, turning complex laws like GDPR, HIPAA, California’s CPRA, and the EU AI Act into concrete technical safeguards. From minimizing sensitive data in training pipelines to stress-testing models for leaks, these engineers are the ones who make sure your AI is trustworthy, compliant, and resilient. 

The timing could not be more urgent. The EU AI Act comes into full force in 2026, while in the U.S., the FTC is already forcing companies to delete models trained on tainted data. Without privacy engineers, businesses risk not just fines but also losing the very models they’ve invested millions in. 

Gary and Scott dive into: 

• How privacy engineers protect the AI lifecycle—from data collection to model deployment. 
• Why businesses of every size need this role, with different priorities for startups, mid-market firms, and global enterprises. 
• The ROI story: Cisco research shows a nearly 2x return on privacy investments, driven by faster sales cycles and stronger customer trust. 
• A practical roadmap for building privacy capacity—starting small with guardrails and scaling up to ISO 42001 certification readiness. 
• And new in this episode: the talent pipeline challenge. Where do you find these people? The best privacy engineers often start as ML engineers, security professionals, or graduates of specialized programs like Carnegie Mellon’s Privacy Engineering track. But supply is thin, so forward-looking enterprises are upskilling internal talent, partnering with consultancies, and competing aggressively to hire the rare hybrid who can talk about both differential privacy and the NIST AI Risk Management Framework. 

The bottom line: Privacy Engineers for AI aren’t just compliance hires. They future-proof your AI investments, accelerate growth, and turn privacy into a strategic differentiator in an era where trust is the new currency. 

About your AI Guides

Gary Sloper

https://www.linkedin.com/in/gsloper/

Scott Bryan

https://www.linkedin.com/in/scottjbryan/

Macro AI Website:

https://www.macroaipodcast.com/

Macro AI LinkedIn Page:

https://www.linkedin.com/company/macro-ai-podcast/

Gary's Free AI Readiness Assessment:

https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness

Scott's Content & Blog

https://www.macronomics.ai/blog

Transcript

00:00
Welcome to the Macro AI Podcast,  where your expert guides Gary Sloper and Scott Bryan navigate the ever-evolving world of artificial intelligence.  Step into the future with us  as we uncover how AI is revolutionizing the global business landscape  from nimble startups to Fortune 500 giants.  Whether you're a seasoned executive,  an ambitious entrepreneur,

00:27
or simply eager to harness AI's potential,  we've got you covered.  Expect actionable insights,  conversations with industry trailblazers  and service providers,  and proven strategies to keep you ahead in a world being shaped rapidly by innovation.  Gary and Scott are here to decode the complexities of AI  and to bring forward ideas that can transform cutting-edge technology  into real-world business success.

00:57
So join us,  let's explore, learn  and lead together.

01:04
Welcome to the Macro AI Podcast. I'm Gary Sloper. And as always, I'm here with my co-host, Scott Bryan. Today, we're going to unpack a role that's quickly becoming mission critical in the AI world, the privacy engineer for artificial intelligence. Yeah, definitely a timely topic. uh Every company is really sprinting to deploy AI,  but few are stopping to ask the hard question. How do we keep it private, compliant, and still valuable for the business? And that's where the privacy engineers

01:33
step in. Yeah, that's right. So we should probably start with really defining the role  at the top. ah A privacy engineer for AI is essentially the translator between legal and the technical organization. So you may have internal counsel  or a team of attorneys and your technical organization. So they take regulations  if you think of things such as GDPR, HIPAA,

02:02
California's CPRA and the EU's brand new AI Act. And make sure those aren't just written on paper. They are actually baked into the systems that you will build. Right. Yeah, the privacy engineers aren't policy people that sit in an ivory tower. They're actually hands-on engineers. Right. So they're designing training pipelines that...

02:28
minimize the data that you need.  They make sure that consent signals aren't just collected, but they're enforced in the code  and they're testing models to see if they leak information about the training data.  Yeah. I think that's a huge distinction. It's one thing to say, you know, we're compliant. It's another to prove that your artificial intelligence, that your shipping doesn't accidentally spit out someone's personal information.  Cause you didn't think about privacy at the model layer, for example.

02:58
Yeah. Yeah. And I think, I think, um, you know, when you think about why, why now, cause a lot of people aren't familiar with the title privacy engineer. I think timing is critical. So the, the EU AI act that, that Gary mentioned is, is phasing in right now and will be fully enforced by, uh, late summer. think it's August of 2026 and it bans some AI uses outright and it creates heavy obligations for anything that's deemed as high risk. Yeah. That's a point.

03:27
And even if you look here in the U S the FTC has already taken companies to task. We're seeing something called algorithmic discouragement, which is basically the nuclear option. If your model was trained on data that was illegally or improperly collected, regulators can make you. You know, delete and remove not just the data, but the model itself. And that's really impactful. So imagine investing. That's a big deal. Right? Yeah. mean, imagine investing.

03:57
millions into a model,  not just with time and people and resources, and then being forced to destroy it. We saw this ah as an example with the Rite Aid settlement here in the States, where the FTC came back and said, you know, uh around their facial recognition  software that they had deployed, that they  could not deploy facial recognition or analysis systems for five years  in both the retail environments, all their stores, and their online platform.

04:27
And then they had to essentially delete all photos,  videos of consumers  used in the facial recognition. And that also included removing any data, models,  algorithms  derived from this platform. So a very impactful move by the government in order to protect consumers. Yeah, hugely damaging. And if they had an active, competent privacy engineer, maybe that wouldn't have happened.

04:55
And that's exactly why businesses can't treat privacy as an afterthought.  This isn't a  nice to have type of thing. It's really about protecting the AI investment,  well, the investment in the AI itself, the model itself,  like you mentioned, which in this case was deleted. Yeah,  I agree. And if you were to kind of bring this to life, if I'm a CEO listening right now, what does a privacy engineer actually do inside my company? Because...

05:23
they may want to staff this. And I think this is probably where we should kind of pivot the conversation a little bit now, because that's  certainly going to be  questions that we're going to see from business leaders if we don't answer it  today.  So think of them as your safety net across the entire AI lifecycle. So when  your team is out collecting data, they're asking,  do we really need this field or do we have consent? ah So when you're training a model, they're adding techniques like

05:51
differential privacy so the model doesn't memorize sensitive data.  Yeah, and I'd say real quick, I'll define differential privacy for the listeners since it's an important term.  So it's really, it's a mathematical technique, essentially allows organizations to analyze and share insights from data without revealing information about any specific individual in the data set. So,  you know, back to what a privacy engineer does before you ship the model, they're stress testing the system.

06:21
Can someone run a membership  inference attack?  Can they pull training data out of the chatbot? Privacy engineer is the one running those tests and telling you  if you are or you're not exposed in that particular example. Yeah, exactly. And once you're live, they're making sure things like data deletion requests actually uh cascade through your pipelines. So if a customer wants to be forgotten, that includes the models. uh

06:50
trained on the data. Yeah, especially if it's been documented by the customer. Please remove me. I completely agree. So,  you know, if we look at the talent pipeline, here's the question I keep hearing from executives. Where do we actually find these people? Where do we find privacy engineers?  You've convinced me the role matters and it's important. How do I hire one?  Yeah, good question. So uh I think that's one of the biggest challenges.  This is

07:20
Definitely a hybrid skill set.  think the strongest privacy engineers often come from uh probably three backgrounds. are data scientists or machine learning engineers who have actually had to wrestle with sensitive data  and realize that privacy can't be bolted on later. uh Others are security  engineers, security and privacy engineers who've worked on encryption.

07:46
uh access controls  or audit systems,  then pivoted over into AI. lot of folks are starting to do that now. uh And then there's a smaller emerging pipeline from actual dedicated privacy engineering programs. ah Schools like uh Carnegie Mellon, for example, has been a leader here. And uh other professional bodies like the IAPP are starting to offer training and certifications.  The IAPP is the International Association of

08:15
privacy professionals now, you know, obviously heavily focused on AI. That's interesting. I didn't realize Carnegie was doing that. So that's good to know, especially as we talked to a lot of folks looking to get into something in the field around AI. I knew the IAPP had rolled out certifications. I've seen a lot of individuals at organizations that came out of, you know, governance and compliance within the internal construct of a company look

08:42
to go obtain those certifications. So I think that's definitely a natural progression for that  subset. But it's good to hear that colleges like Carnegie are adding that to the curriculum.  Yeah, I think a lot of them are starting to work the specific  privacy engineering programs into the curriculum now. That's great. So I'd say if you're an enterprise leader, the reality is you probably won't just post a job listing and get flooded with qualified candidates because they don't exist.

09:12
So that's just one thing to keep in mind. Yeah, exactly. And I think  most organizations either grow this talent internally by taking some of their sharp engineers and upskilling them on privacy frameworks and AI risk,  or they lean on consultancies ah until they can actually build their own internal bench. So the unicorn hire who can explain both differential privacy and say TensorFlow and

09:40
NIST's AI risk management framework is rare and is definitely in very high demand.  Yeah, I agree. And again, Bernie executives listening, here's the leadership takeaway. If you wait until regulators are knocking on your door, it's probably too late to hire or grow this talent. So think about building privacy engineering capacity, you know, in your organization. ah It's a multi-quarter journey. It's not just going to be a last minute purchase.  And quite honestly,

10:10
You may have folks that are interested and that's a great stepping stone to upskill your talent who know your product, you know, your organization, they know the culture. What a great way to lead them down a career path.  Yeah. And I think good consultants out there are going to be addressing this head on, you know, right out of the gate as part of AI strategy. You have to be considering this. Yeah. And I think, you know, another, another piece here is what does it mean for different, you know, businesses and  who are

10:38
different size compared to others  in the global landscape.  And I like to think about this, you know, with the customer size, you know, for example, if we start  at the beginning, startups,  you know, they don't necessarily need a massive privacy office on day one, but they do need someone putting guardrails in place, especially as you're that budding organization you want to grow or look to advance and receive additional funding. So I don't think you have to go put a team of

11:07
10 or 20 people out there, but somebody that is responsible to make sure that you are within the guardrails. Yeah, right. I think for a  startup, it could be as simple as lightweight governance,  making sure that you're not hoarding data,  you're having a plan for deletions, and maybe experimenting with differential privacy early on.  And again, like I said, think consultants can really help at this level to ensure that you're on the right track and you have those tools and guidelines in place. Yeah. And especially

11:37
putting that framework in place to when you get to a mid-size company,  maybe  you can start doing things a little bit differently. Because mid-size  companies,  they start having that requirement of more formal programs. That's when you're documenting your data sources, monitoring for bias,  even checking the vendors you work with and red teaming your AI systems.  I hear from a lot of mid-size companies looking...

12:01
for assistance or temporary talent to get their practice moving in the right direction. To your point, Scott, that could be another avenue to utilize consultants. ah Or if you're past that, you've already had that framework at that startup level ah with a consultant that could at least benchmark what you need. So when you are ready to hire, you  can move into that trajectory. Yeah, or even use them as a double check, second opinion. Right. ah

12:27
And then for large enterprises, privacy engineering becomes a full program. You're talking about  certifiable management systems under ISO standards, deploying multiple privacy enhancing technologies in combination with that,  and being ready for things like EU AI Act conformity assessments, because those are coming.  Spot on. You're absolutely right. So then if we were to bring all this together, ah

12:56
what every executive wants to ask and hopefully hear the answer to their question, which  is hopefully positive is, does this investment pay off? Is there a return on investment if I invest in this person or people? Yeah.  And obviously a lot of it is hard to measure because you're  mitigating potential disasters, but in general it does.  Cisco's global studies

13:23
consistently show nearly a 2X return on privacy investments.  So that's not just cost avoidance, like I mentioned, it's faster sales cycles, fewer deals stalling because of privacy  concerns,  fewer legal firefights, and the list goes on. Yeah. So I think what I'm hearing is the business case is clear uh based on what we've seen and what we're talking about today from independent resources and privacy engineers

13:52
You know, as we mentioned, they're not there to slow you down. They help you move faster by making customers and regulators more comfortable with your artificial intelligence. So think that's another point to just take home as well. Yeah. And if we shift over into kind how do you, how do you build the capability? So, you  know, if you're wondering where to begin, just start small and build. So in the first quarter, you know, inventory your AI use cases  and run some impact assessments.

14:23
In quarter two, start piloting certain techniques like uh federated learning or differential privacy, like we mentioned a bunch of times. ah By the third quarter, you should be formalizing an AI management system.  And then by  the fourth quarter, a year out, you should be running privacy red team exercises to really pressure test what you've built. Yeah. That roadmap takes you from...

14:50
I'd say a reactive compliance to proactive trust building. Yep. And really along the way you're building the organizational muscle. You'll need to succeed long-term, especially as uh regulations change, technologies change.  This is a good way to be able to adapt in the long run. There'll definitely be a lot more coming down the pipe.  if  you're organized and ready, you'll have that team that's able to adapt to the new regulations and requirements.

15:20
Exactly. So here's the big takeaway. A privacy engineer for AI isn't just about avoiding fines. ah They're about protecting your artificial intelligence investments,  accelerating your growth, and building trust in a market that's about to become very heavily regulated. ah So that's,  think, a key takeaway. Yeah, and if you're deploying AI and companies all over the world are doing this now,

15:48
this role will become essential. And the sooner you embrace it, the smoother your path will likely be.  And if you're launching your career, ah maybe do some research on the role. ah Take a look at some of the colleges we mentioned, some of the programs that they have, see if they have it in their curriculum yet, ask the provost if it's coming,  and see if it's something that's interesting to you. That's great. I appreciate the discussion as always today, Scott. uh

16:15
think that's all the time we have  here on the Macro AI Podcast. I want to say thank you to everyone tuning in. Please like and subscribe, share it with your network.  And until next time,  keep leading in the AI era.  Good stuff.