TBD | The Immigration Data Dragnet
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For the past four months, a team of people from Elon Musk's so-called Department of Government Efficiency has gone from one federal agency to another looking at data. Very early on, we found that they had access to the sensitive payment data system or payment systems within the U.S. Treasury. Vittoria Elliott reports on Doge and Musk's operatives for Wired.

We know from my colleagues reporting that they have gained access at the Social Security Administration. And we also know from documents filed in another lawsuit that when members of Doge were at the Social Security Administration, that they sought access to the SAVE database, which is run by the U.S. Citizenship and Immigration Services. And that tracks people who are in the country legally.

Vittoria and her colleagues also reported that Doge operatives are cross-referencing data from the many agencies of the U.S. government.

Doge also has been accessing the UCIS database, and it appears that they are querying it against Social Security data, IRS data, and also state voting data. There's no one list of what data or systems Doge has accessed. Vittoria and other reporters are carefully piecing together a larger puzzle, based in part on what the government itself is saying.

This week, the Department of Homeland Security, DOGE, and the U.S. Citizenship and Immigration Services announced what they called a comprehensive optimization of one of the country's largest immigration databases for enforcement purposes.

While that kind of data would normally maybe be shared if there was an investigation, it's not like everything is perfectly siloed. It does seem that that access and that data overlay is much greater than it has historically been. The consistent refrain from Musk and his associates is that this is about efficiency. Just listen to Airbnb co-founder and Doge member Joe Gebbia on Fox News last month.

We really believe that the government can have an Apple store-like experience. Beautifully designed, great user experience, modern systems. And sure, in theory, that sounds great. Dealing with government tech systems can be super annoying.

There is a real pain point there. But there's also reasons that these data are siloed, and it is because that's a safety measure. First off, if all your data is in the same place, all you need is one really good hack from a foreign adversary and you're into everything. And then there are the other risks. We already know that the Trump administration wants this data for immigration enforcement. What if they want it for more?

I'm thinking about, you know, RFK Jr.'s recent announcement that they're going to try and get medical records from people to figure out autism. But I don't know that I would want my IRS data combined with my medical records for the purpose of the government to surveil me. I think I would find that extraordinarily scary.

So I don't think that it is purely on the level of saying we want every American to feel like interacting with the government is a pleasurable experience. I think it is also because these types of actions make carrying out whatever presidential agenda you have much easier, even if it comes at the cost of people's privacy. Today on the show, data is power.

It's also the key building block of a surveillance state. I'm Lizzie O'Leary, and you're listening to What Next TBD, a show about technology, power, and how the future will be determined. Stick around.

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That's 20% off any AquaTrue water purifier when you go to AquaTrue.com and use the promo code TBD. I want to back up a little bit and talk about, I guess,

The kind of picture you might be able to build of someone from the data that you and your colleagues have reported that Doge has access to right now. Let's say you're looking at data sets, you're looking at systems. Roughly, can you sketch out who somebody is?

I think it would be entirely possible. And particularly the more that you have to interact with the government, the more likely. So, for instance, say you are a person who grew up in low income housing.

Maybe you have student loans. Maybe you have an undocumented family member or a recipient of public housing of Section 8, Section 9. That means that your information, your Social Security number, maybe your household income, that's part of a government database.

If you have a family member who is an immigrant, documented or otherwise, a lot of times in the USIS database, for instance. You're saying the U.S. Citizenship and Immigration Service. Yes.

So, for instance, information will be in there on an immigrant and their extended family, their sponsors in the country. And then, of course, your social security number is tied to everything. It's tied to employment. It's tied to birth, death, medical records. There's so much that even just knowing someone's social security number can give you access to. I mean, that's why hackers want it, you know?

There are obviously populations that might be more vulnerable than others based on their interactions with the government or their own legal status or immigration status. But the reality is that everyone is vulnerable to some degree of having their information being combined in a way that could give, you know, the government a picture that maybe you don't want them to have.

The refrain from Doge, and you heard it earlier from Joe Gebbia, is that siloed data and systems are inefficient. But inefficiency is also a form of protection. Protection from someone looking at what government services you receive, whether you get Medicare, what kind of political donations you may have made.

There's all these other ways that they can build a picture of you and data on that level can lead to discrimination across many different things.

Information about your medical history can lead to discrimination. Information about your sexual orientation or your, you know, whether or not you're divorced or any of these things in the same way that we wouldn't necessarily want insurance companies to know those things because they might use that data in a way that farges you more. There's no guarantee that a government is going to look at that data and not use it for something like prostitution.

predicting your outcomes or deciding if you're more likely to commit a crime or investigating you if they think that perhaps you disagree with them. Traditionally, these systems have been kept intentionally separate from one another. And as I understand the Privacy Act of 1974 and the way it's interpreted, it's

The employees carrying out a lot of work around these data sets should not have access to personally identifiable information. But I wonder what your reporting says about what can be seen on a granular level.

You know, reasonably, you're right. There shouldn't necessarily be the ability to see PII or personally identifiable information. But in talking to some people who had previously worked for DHS, for instance, for the story my colleague and I just did, we were told, like, yes, there are sort of these different data sets even within DHS.

Because DHS deals with granting you your green card and it also deals with like homeland security investigations, like possibly looking at people for being threats to national security. So there's the data set.

around the normal process of immigrating, of having to interview your family members or things like that, that is supposed to be kept separate from the data that might be used for enforcement. But in reality, you know, there are all these carve-outs and exceptions for law enforcement. So

HSI, Homeland Security Investigations, or ICE can go to other parts of DHS and say, hey, we need access to this information for an investigation for law enforcement purposes. That's not always the case. Sometimes they need a court order, but there are carve-outs for law enforcement. According to Vittoria, this is by design.

Normally, especially if you're thinking about something like IRS or Social Security Administration, things that would contain really specific data, it is supposed to be an incredibly limited space.

set of people who can access something. It's very much on a need-to-know level. The general rule with data for the government is the lowest level necessary, so the absolute bare minimum you need to do your job. And that can make things really slow. In fact, a lot of the experience, I think, of Americans with what they perceive to be inefficiency in the government is having to work through these systems that don't all click together effectively.

because your data is supposed to be protected by them. Listening to you talk about data sharing, I want to understand how formal the agreements are among and between different agencies. Are we talking about somewhere there exists a written policy saying, we agree to share this with you? Or is this somebody in one department calls up someone else and says, hey, can you look up this guy for me?

So it depends. So, for instance, if you're talking about data within an agency, and I think DHS is a great example because, again, it deals with sort of the regular immigration stuff like you would have with USIS or U.S. Citizenship and Immigration Services, and it also deals with like a law enforcement component.

That data sharing across the agency would probably be much easier. But when you're sharing across agencies, you have these agreements called computer matching agreements, and you also have these things called system of record notices or SORNs. And those actually spell out this agreement.

agency is partnering with this agency, and they're going to be given this kind of access for this reason. And so, you know, if you go on the DHS website, you can see system of records notices, and you can see every single sworn that they have with other agencies to kind of understand what part of the agency is sharing what and with whom. So with the

the exception of maybe some sort of criminal investigations, like if we're just talking about routine access across agencies, which definitely does happen, these are all meant to be documented publicly so that if nothing else, there's a record of what's being shared and across which agencies. Are they being documented publicly? I think Doge is a

distinct departure from how things have normally worked. Despite rulings, by the way, that they should be subject to FOIA and the Federal Records Act. Yeah. But I think the bigger thing, too, is, you know, it's very unusual to see an individual work across four or five agencies at a time, have simultaneous access to four or five

systems across many different agencies at a time. You know, it's not uncommon, for instance, for someone to start at an agency and then get detailed out to another agency. And we've seen some of that with Doge. We've seen, obviously, some exchange between the General Services Administration and the Department of Labor and some other ones. You know, so they are starting to do some of that documentation. But

It's very uncommon that you would have someone who is accessing all of these things simultaneously across multiple different agencies. And we have seen that many different times with Doge. So there was the executive order on March 20th, I believe, where President Trump said there was this sort of ending data silos thing.

executive order. And that was really giving Doge the go-ahead to combine some of these data sets and systems that maybe would not normally have otherwise been interacting. And then on April 5th, DHS actually struck an official agreement with the IRS to use tax data to search

for immigrants for enforcement. So, you know, I think we are seeing some of these become public and more formalized, but then we're also seeing this informal mingling, which is having people working across these agencies simultaneously, having insight into these data sets simultaneously, where that would never have been possible before. When we come back, how the consequences of sharing that data will impact everyone, including you.

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Last week, The Washington Post reported that the Social Security Administration entered the names of some 6,000 largely Latino immigrants into a database that it uses to track dead people, which effectively kind of erases their ability to work legally in the U.S., receive benefits. It just seems to show the kind of power that comes with the ability to read and write data.

Yeah, I think that's true. And I think it's very telling, for instance, that some of the earliest people hired into Doge were not people with extensive government experience. They were technical people, often young. Silicon Valley loves sort of young, scrappy people who are going to work long hours. They really emphasize people who had a specific technical skill set because

Because they wanted people who could go in and access this data and play with it, analyze it. We're still finding out what they're doing with it. But if our reporting is any indication, it seems like a big goal is to be able to combine it across agencies in a way that we've never seen before.

There was so much reporting, including by you and your colleagues, in the early days of Doge where it was just becoming clear they had access to this and that and, you know, this agency, that agency. Is it fair to say that now we are turning from access to intention, access to the application of this data?

Yeah, I do think that that is what we're seeing, right? Because initially, when we were seeing them access all these different systems, you know, we had sort of inklings again, you know, the the.

lawsuits around the Treasury indicate how that might have been used around USAID. We have a sense that they were looking for employee details at OPM or the Office of Personal Management, you know, to be able to conduct these mass firings. We sort of had early inklings based on the actions that were taking place at those moments, but I think there wasn't necessarily a clear

sense of what was going on with this sort of wide ranging access. And now it's only starting to become a bit more clear. And I think, again, that the immigration space is where we're seeing it first. That doesn't mean it will be the only space. I think it is, again, just the most one of the more pressing parts of the president's agenda.

There's another story that you and your colleagues have worked on, which talks about building an immigration OS, basically a surveillance platform, for lack of a better term, where ICE would work alongside the company Palantir, which has been an ICE contractor for some time. Can you explain what that might do? Because it

incredibly all-encompassing. So it was interesting, actually, because

because we had people tell us that there were these efforts to combine data across DHS in a way that was sort of had never really happened before. And then, you know, shortly after my colleague Caroline published that story. So it's unclear if that sort of immigration enforcement OS is what people referring to or if there are other initiatives that we aren't yet aware of that are also part of this.

But again, it's combining information from UCIS and

Again, ICE particularly and particularly within DHS, there has sort of been a culture of more information sharing just because these are both nested within DHS. So that's a little easier to do in terms of scooping up all that data than it would otherwise be to go across an agency. But I think it is concerning because, again, one of the things that experts have pointed out to us is that

My name is Vittoria. It's not a very common first name in this country. It's Italian. And the number of times that I have accidentally been entered into a system as Victoria is innumerable.

So when I went to the DMV the first time to get my driver's license when I was 16, I had the nice lady at the DMV tell me I'd spelt my name wrong and helpfully correct it for me. And then I had to start the process again. Yeah, my name has an apostrophe. Computers do not know what to do with me. Exactly. So I think the thing that we should really be concerned about is this sort of idea that like

Data is fallible. Matching different data sets across different agencies and obviously like something like a social security number hopefully helps with that. But these things are all imperfect. You know, they are liable to have clerical errors that could really result in harmful things. I mean, if you think about the situation with Kilmar Abrego Garcia, like the government lawyer who was then dismissed yesterday.

admitted essentially that it was a mistake. And I think we should really not, as much as these data sets and these systems are incredibly powerful and they need to be treated with care and protected, I also don't think we should overestimate their accuracy. There are errors and problems in government information all the time.

And the idea that somehow we would be firing lots of employees, removing humans from the loop of some of the most sensitive systems and processes, and then combining data across agencies without necessarily being totally sure about

who that could target, I think leaves so much room for error. You know, immigration attorneys were sent letters saying that they had to self-deport. And I'm sure that was probably a clerical error that their name was in the system in some capacity, probably associated with one of their clients. And whatever tool that DHS was using to send out that message was

just didn't pick up on that. And if you can think of that at a massive scale, even small amounts of errors could cause irreparable harm to many people. You know, we have been talking largely about immigration. Is it too bold a question to say this might be starting with immigration, but is that where it ends? I think...

History and other countries are great examples for the answer to that question. I think about what we're seeing in Turkey right now. A favored presidential contender to run against Erdogan is currently facing a tax investigation.

I think immigration is something that's sort of at the fore of this administration's agenda. It's also been something that has historically been popular with MAGA supporters. But the reality is that once you have access to the sensitive data in the IRS, you're

You can investigate an American citizen as easily as you can investigate an immigrant. And once you're breaching the norms around how that data should be used to begin with, I think there is very little friction in pushing it further. And that doesn't mean that we've seen them do that yet, but that certainly could be conceivable with the access that they have.

All of this brings to mind something that billionaire Larry Ellison, the Oracle founder and prominent Trump backer, said last year, that AI-powered surveillance systems would usher in an age where, quote, citizens will be on their best behavior. I think the thing is that, like,

We have seen in Silicon Valley a willingness to break rules, a willingness to allow their tools to be used in anti-democratic ways.

a willingness to sort of push the limits and apologize later. Move fast and break things. Yeah. And I think when you are doing that on a government level, first off, 90% of startups fail. I don't know that we can ride out the eventuality of the U.S. government failing. I don't love that. And I think the real thing is this. When people talk about AI or any of these tools, what they're really talking about

in many ways, is efficiency. That's why they're so excited to introduce tools like GSAI into the General Services Administration, which my colleagues had people there tell them was not particularly better than an intern. But it's this sort of idea that, yes, there will be surveillance, but also this sort of idea that the thing can run on its own. And so I think even before

We pick apart what Doge is doing. The really big question that I don't think they've managed to really answer is efficient for whom and for what purpose? And yeah, does combining all these data sets make immigration enforcement, quote unquote, more efficient? Yes, it makes it more automated. It makes, quote unquote, targets easier to find. Does that make it accurate? Does that make it just right?

When we talk about so much of this, it's that justice, fairness, justice.

Dealing with human beings and creating systems that cater not just to the way Silicon Valley works, but systems that truly work and cater to everyone are not going to feel shiny and Apple store efficient. They're probably going to be slower and clunkier, but that's OK because not everything needs to run like a business. Not everything is a market cap.

Victoria Elliott, thank you so much for your reporting and for talking with me. Yeah, thank you for your time. Victoria Elliott is a reporter for Wired. And that is it for our show today. What Next TBD is produced by Patrick Fort. Our show is edited by Evan Campbell. Slate is run by Hilary Fry and TBD is part of the larger What Next family.

And if you're looking for another great Slate show to listen to, check out Thursday's episode of What Next? A union leader makes the impassioned case that we should all go on strike. All right, we will be back on Sunday with another episode about what the heck is going on with the government weather services. I'm Lizzie O'Leary. Thanks so much for listening. I'm Leon Nafok, and I'm the host of Slow Burn Watergate. Before I started working on this show,

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