In the time of AI in early March 2025, a whistleblower from the National Labor Relations Board (NLRB) unveiled a startling revelation: the Department of Government Efficiency (DOGE) had accessed the NLRB’s network, extracting sensitive information. By April 7, the Department of Homeland Security (DHS) had secured access to Internal Revenue Service (IRS) tax data. While these events might appear isolated, they signify a broader transformation in the U.S. federal government’s approach to data collection and surveillance.
The Rise of Interagency Data Sharing
Historically, U.S. federal agencies operated with siloed data systems, each maintaining its own repositories. However, recent executive orders, particularly under President Donald Trump’s administration, have dismantled these barriers. DOGE has been instrumental in integrating various government networks, ensuring data accuracy, and promoting responsible data collection and coordination.
This integration facilitates real-time sharing of sensitive information across agencies, enabling the creation of centralized databases that monitor individuals’ interactions with government services. While proponents argue this enhances administrative efficiency, critics warn of an emerging surveillance infrastructure that blurs the lines between public service and law enforcement.
The Role of Private Tech Firms
At the core of America’s expanding surveillance architecture is Palantir Technologies, a data analytics powerhouse whose platforms have become essential tools for federal agencies seeking to centralize, visualize, and act on massive troves of personal data. Founded in 2003 with early funding from the CIA’s venture capital arm, In-Q-Tel, Palantir has steadily embedded itself in nearly every major federal agency involved in national security, law enforcement, public health, and tax enforcement.
Palantir’s flagship systems—Investigative Case Management (ICM) and FALCON—serve as comprehensive data aggregation platforms. These tools compile and cross-reference information from a wide array of sources, including:
- State-issued driver’s licenses
- Social services and welfare databases
- Bank and credit card transaction histories
- School and university enrollment records
- Public health and vaccination data
- Social media activity and geolocation data
These platforms are not just about organizing information—they’re built for prediction, profiling, and enforcement. With sleek dashboards and powerful search tools, Palantir systems enable agents to create detailed profiles of individuals, communities, and trends within seconds.
As of 2024, Palantir had over $1.9 billion in government contracts, with clients including the Department of Defense, Immigration and Customs Enforcement (ICE), the Centers for Disease Control and Prevention (CDC), the Internal Revenue Service (IRS), and the Department of Homeland Security (DHS). According to public procurement records, approximately 60% of Palantir’s revenue in 2023 came from government contracts.
One of the most notable recent developments occurred in April 2025, when ICE awarded Palantir a $29.8 million contract to develop the Immigration Lifecycle Operating System (ImmigrationOS). This system is designed to:
- Monitor self-deportations
- Track visa overstays
- Integrate data from multiple sources including Customs and Border Protection (CBP), the Department of State, and the Social Security Administration
ImmigrationOS represents the next phase in predictive immigration enforcement. By correlating travel records, visa applications, employment histories, and digital footprints, ICE aims to preemptively flag individuals for removal or further scrutiny. While the agency describes the system as a way to improve efficiency and compliance, privacy advocates warn it may result in automated targeting of immigrants without due process.
Palantir’s partnership with ICE stretches back to 2011, when it began supplying the agency with surveillance infrastructure through FALCON, a system originally developed for counterterrorism purposes. Over time, FALCON was adapted for immigration enforcement, allowing agents to:
- Locate undocumented individuals using license plate readers and social media scraping
- Connect family members and associates through relational databases
- Identify potential “threat networks” among immigrant populations
According to the American Civil Liberties Union (ACLU), Palantir’s technology was involved in hundreds of ICE raids between 2017 and 2020, many of which drew criticism for targeting individuals with no criminal records.
Beyond ICE, Palantir has worked closely with the CDC during the COVID-19 pandemic, helping to track vaccine distribution and hospital resource use. In a 2021 deal worth over $440 million, Palantir also developed systems for the Department of Health and Human Services (HHS) to manage pandemic response logistics. These partnerships, while lauded for efficiency, have sparked concern over the normalization of mass health data surveillance.
Perhaps most controversially, Palantir has collaborated with the IRS to uncover potential tax fraud. According to a 2022 ProPublica report, Palantir’s software enabled the IRS to target low-income taxpayers at disproportionately high rates, based on algorithmic risk scores that critics say reproduce racial and economic bias.
In sum, Palantir has evolved from a Silicon Valley startup into a critical node in the U.S. surveillance state. Its tools are not only shaping how government agencies gather and interpret data—they’re redefining the boundaries of privacy, civil rights, and public accountability in an increasingly automated era.
AI and Predictive Surveillance
The growing use of artificial intelligence (AI) in government surveillance has drastically transformed the way authorities monitor, predict, and respond to perceived risks. Far from simply collecting data, modern surveillance systems now actively interpret and act on it, thanks to the integration of predictive algorithms—complex models designed to forecast human behavior by analyzing patterns in massive datasets.
These AI systems pull from a wide array of personal and public information sources. Data such as school enrollment records, housing applications, utility usage patterns, healthcare records, and even social media posts are fed into algorithms designed to detect anomalies or predict future events. For example, a sudden drop in electricity usage combined with frequent online searches about eviction laws might be flagged as a potential indicator of housing instability or fraud. Similarly, posts expressing political dissent or certain religious beliefs might be misinterpreted as signs of radicalization or unrest, depending on how the algorithm is trained.
This shift from passive observation to active prediction gives authorities a powerful—yet deeply controversial—tool for anticipating problems before they happen. The goal is often framed as efficiency and safety: catching criminal activity in its early stages, targeting welfare fraud, or preventing terrorism. However, such intentions are complicated by the opaque nature of the technology itself.
Most predictive AI systems are developed by private tech firms and operate as proprietary “black boxes.” That means the logic behind their decisions is often hidden from both the public and the very government officials using them. These systems are rarely subject to independent audits, and the data they use to make judgments can be flawed, biased, or incomplete. When an AI misinterprets data—an error often described as an “AI hallucination”—the consequences can be devastating. A person might be wrongly flagged as a security risk, denied housing assistance, or lose employment opportunities based on nothing more than an algorithm’s faulty assumption.
What makes this even more troubling is the lack of transparency and accountability. Most individuals affected by such decisions have little to no insight into why the action was taken, let alone any meaningful way to challenge it. Legal frameworks around AI decision-making are still underdeveloped, and recourse mechanisms—when they exist at all—are slow, costly, and often ineffective.
Furthermore, these systems disproportionately affect marginalized communities. Since the datasets used to train predictive algorithms often reflect existing societal biases—such as those based on race, class, or geography—the technology can end up reinforcing discrimination rather than eliminating it. In practice, this means that low-income individuals, immigrants, or people of color are more likely to be surveilled, flagged, and penalized by AI systems, further entrenching cycles of inequality and exclusion.
In short, while AI offers unparalleled efficiency in surveillance, it also raises profound ethical and civil liberties concerns. The technology’s ability to predict behavior is seductive, but without robust oversight, transparency, and accountability, it risks transforming the promise of safety into a permanent state of suspicion and control.
The Erosion of Privacy and Civil Liberties
The increasing reliance on data sharing and AI-driven surveillance poses significant threats to privacy and civil liberties. Information initially collected for public services is now repurposed for monitoring and enforcement. This shift disproportionately affects marginalized communities, including low-income individuals, immigrants, and people of color.
The line between public governance and corporate surveillance is becoming increasingly blurred. As government agencies depend more on private contractors for data analysis and infrastructure, accountability becomes more diffuse, and oversight mechanisms weaken.
The transformation of the U.S. federal government’s data practices reflects a broader trend toward pervasive surveillance. While these systems are often justified in the name of efficiency and security, they carry profound implications for individual rights and societal norms. As technology continues to evolve, it is imperative to critically assess and address the balance between innovation and civil liberties.
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