In March of 2025, an informant from the National Labor Relations Board shed light on an unexpected increase in potentially sensitive information departing from the agency’s network, right after personnel from the Department of Government Efficiency (DOGE) were given database access. Just a month later, in April, the Department of Homeland Security was granted access to Internal Revenue Service tax information. Although these events might seem disconnected, they are both indicative of recent changes regarding the formation and functionality of federal government data repositories.
The use of personal information, collected by the U.S. government for services such as tax filings, health care registrations, unemployment benefits, and education subsidies, is no longer limited to its original purpose. Gradual change is being seen as this data, initially collected to streamline health care and administer public services, is now repurposed for surveillance and law enforcement applications. It is now routinely shared among different government agencies and with private entities, morphing the essentials of public service into a control mechanism.
Data once constrained within independent bureaucracies is now dynamically exchanged through a system of interdepartmental agreements, outsourcing contracts, and business collaborations established over the years. These data-exchange operations commonly happen outside the public eye, propelled by reasons rooted in national security, anti-fraud efforts, and digital upgrade programs.
The shifts in the governmental structure are subtly transforming it into an integrated surveillance mechanism, equipped to oversee, forecast, and highlight behavior on an unprecedented scale. Executive decrees are working towards eliminating impeding institutional and legal hurdles to complete this extensive surveillance network.
DOGE’s mission, outlined by an executive order, is to augment collaboration between agency systems, ensure data authenticity, and supervise the collection and synchronization of data in a responsible manner. Accompanied by another executive mandate calling for the destruction of government data silos, interoperable systems are being built by DOGE to facilitate real-time access to sensitive cross-agency data and create a centralized public database.
These changes, while portrayed as administrative simplifications, are laying the foundation for nationwide surveillance. Certain agencies are resorting to third party vendors and data brokers to side-step direct restrictions. These middlemen also amalgamate data from various sources including social media, utility companies, or grocery stores, putting together comprehensive digital sketches of individuals, strikingly without their consent or court supervision.
Such practices extend the authority of the government in a manner that poses challenges to traditional conventions of privacy and consent. The advancements in artificial intelligence have only added fuel to this shift. Predictive algorithms now sift through voluminous data to calculate risk, detect irregularities, and identify potential threats.
Online enrollment records for schools, housing applications, utility usage, and even social media data, all harvested through contracts with data brokers and tech corporations, are consumed by these systems. As these systems are machine learning-based, their functions often are proprietary, unfathomable, and dodge substantial public scrutiny.
Often, these systems generate inaccurate results, arising from erroneous, fictitious, or irrelevant responses. Even minor inconsistencies in data can trigger significant implications such as job termination, benefits denial, or unfair targeting in law enforcement operations. On being flagged, it is seldom that individuals find a transparent course to dispute the conclusions of the system.
Actions such as participating in public affairs, applying for credit, seeking calamity aid, or soliciting student loans now add to one’s digital history. That data can be later processed by government organs to disallow access to aid. Data gathered with good intent could end up becoming a tool to justify monitoring someone. The increasing reliance on private contractors in the process continues to blur the border between public governance and corporate surveillance.
The use of artificial intelligence, facial recognition, and predictive profiling systems lack regulation and have a higher impact on less-affluent individuals, immigrants, and people of color, who are frequently singled out as risks. Originally conceived for benefits authentication or emergency response, these data systems now contribute to expansive surveillance networks.
The implications are huge herein. Initially aimed at noncitizens and suspected fraudsters, the system could easily be extended to cover everyone in the country. It isn’t a mere data privacy issue but a broader shift in governance approach.
Administrative systems are now being used as tools to track and forecast human behavior. In this emergent model, surveillance is weak, accountability minimal, and AI is being used to interpret behaviors at scale without the need for direct questioning or verification.
The risk here is universal. Despite these technologies being first introduced at society’s fringes—against migrants, welfare recipients, or those marked as high-risk—there’s negligible containment for their scope. As this infrastructure expands, it infiltrates deeper into citizens’ lives.
Each form submission, logged interaction, and device usage deepens an individual’s digital profile, often discreetly. The foundation for omnipresent surveillance is already in place. The only variable that remains is the extent to which this system will be permitted to advance.