How AI Became ICE’s Best Weapon

AI is helping to build one of the largest, most sophisticated, and most aggressive systems within a democratic state. We usually see agents in tactical or assault teams. But behind the scenes, raids are driven by Silicon Valley algorithms. America's most well-funded law enforcement agency of all time is using next generation technology like location tracking, facial recognition, and artificial intelligence. So far this year alone, ICE has spent extraordinary amounts of money on surveillance equipment and software. Donald Trump handed ICE a massive windfall in his One Big Beautiful Bill Act, making it America's highest funded law enforcement agency with $85 billion to spend. Companies such as Palantir, Motorola, and Paragon Software help track and process data with extraordinary accuracy. Probably the most controversial thing that Palantir Software is being used for right now, I would say, is for immigration enforcement with ICE and other organizations. Even activity on social media or simply being a friend of someone under investigation could place you within the system of suspects. If you've shared a political post or are simply friends with someone on social media who is under investigation, you could automatically end up in the system. Phone tracking, surveillance, and even license plate recognition, all of these capabilities are being powered by a single technology, artificial intelligence. So how AI is enhancing ICE raids? Although today the Immigration and Customs Enforcement Service, better known by its acronym ICE, uses AI tools for its raids, it was not always this way. What once relied on informants, physical surveillance, and manual operations now depends on artificial intelligence capable of analyzing millions of data points in seconds. In addition, it can reconstruct life patterns and anticipate movements with a precision that would have been unthinkable at the beginning of the 21st century. The shift began with sustained investments in software and technological equipment. Reports from digital privacy organizations and federal data indicate that in 2025 alone, ICE allocated nearly $22 billion to data analysis tools and social media monitoring. In the three months prior to the November election, the agency signed $20 million in contracts for new phone hacking, surveillance, and forensics technologies that can be used to spy on and track down the people Trump has vowed to expel from the country. Resources have also been invested in mobile phone forensic extraction, facial recognition, and automatic license plate readers. These technologies reduce operational costs and increase efficiency. Some internal estimates suggest that the use of predictive analytics has reduced the time required to locate a target by approximately 35%. In addition, more than 70% of modern immigration investigations begin with data mining rather than physical surveillance. Behind this transformation lies collaboration with tech companies specializing in large-scale information analysis. For example, one of the most influential is Palantir Technologies, founded in 2003 in Silicon Valley. Predictive analytics tools like Palantir's pull together countless data points. They might use information from an electricity bill, a driver's license, or your social media activity. This company focuses on developing data integration and analysis platforms for governments, law enforcement agencies, and large corporations. Its systems allow the combination of disparate databases, visualization of relationship networks, and the generation of predictive models that guide operational decisions. Since 2010, Palantir has maintained continuous federal contracts and has received more than $113 million in recent funding to develop tools such as Elite, a system designed to optimize selection, detention, and deportation operations. ICE agents have a new high-tech way to zero in on neighborhoods to raid. The report says it's an app called Elite, and it's developed by Palantir, which is a data analytics company used by federal agencies and state and local governments. This system provides real-time visibility into migration movements and operational logistics. Palantir's platforms enable the integration of data from multiple sources such as public records, driver's licenses, and even utility billing. By cross-referencing this data, algorithms can infer current addresses, workplaces, and daily routines with an estimated accuracy exceeding 80%. For example, nighttime electricity consumption can indicate presence in a residence, while vehicle records can confirm mobility patterns. This ability to reconstruct daily life turns data into an operational roadmap, but the central element of this system is the massive availability of information. ICE and other federal agencies have access to a vast data network that includes property records, court histories, and commercial databases. And it is estimated that federal programs could expand the collection of social media identifiers to an additional 33 million people. ICE is creating a surveillance-like state of social media accounts of individuals across America who oppose ICE, who oppose the Trump administration's policies, and also undocumented immigrants in the United States. More than 75% of adults in the United States possess a driver's license, making these databases a key resource for biometric identification. This is complemented by data from mobile applications, which can reveal location patterns with accuracy of up to five meters, enabling the reconstruction of daily trajectories. Facial recognition has also become a fundamental tool in the field. However, this introduces significant risks of misidentification, which is why so many wrongful detentions have occurred in recent months. A 79-year-old business owner says he was body slammed and pinned to the ground by federal agents during an immigration raid despite being a U.S. citizen. Agents can use mobile devices to compare faces captured in real-time with databases containing hundreds of millions of images, including driver's license photos and immigration records. These systems can identify a person in seconds, even in crowded environments. However, technical studies have shown that error rates can be up to 34% higher for dark-skinned individuals and up to 20% higher for women. Another key tool is automatic license plate readers, which capture vehicle plates, date, and location. If you drive there, automatic license plate readers can photograph your car and run it through a database of more than 20 billion scans. In seconds, they know where you live, where you work, everywhere your car has been in the last 30 days. ICE has access to more than 9 billion records, enabling the tracking of historical vehicle movements and reconstruction of mobility patterns with accuracy exceeding 90% in urban areas. For example, if a vehicle repeatedly appears at an address during nighttime hours, algorithms can infer that this location corresponds to the driver's residence. This type of analysis allows the identification of residences without direct surveillance. Mobile phone tracking adds another layer of surveillance. Cell tower simulators, known as Stingrays, mimic cellular antennas and force nearby devices to connect to them. Once connected, they can collect unique identifiers, approximate location, and even call metadata. Although their objective is to locate specific devices, these systems can capture data from hundreds or thousands of phones within a radius of up to 8 kilometers in urban areas. There are cell site simulators, or so-called Stingrays. Put simply, they are fake cell towers and cover whole neighborhoods. When a phone of someone targeted connects, officers can track it in real time. But the tech also tracks everyone else in the area. The analysis of phone metadata allows the identification of contact networks with an estimated accuracy of 87 percent, even without accessing the content of conversations. The integration of this data is consolidated into centralized data lakes that eliminate isolated data between agencies. ICE has proposed integrations with social security records, the Internal Revenue Service, and even public health programs. According to them, the objective is to improve accuracy in determining immigration status. But this centralization would allow algorithms to analyze nearly 170 million profiles simultaneously. However, it also raises concerns about the collection of data from U.S. citizens. Healthcare data is the new gold, apparently. Some say it's practically priceless in the wrong hands, and today the Trump administration is expected to meet with tech executives at the White House. Predictive systems represent the next step in this evolution. ICE uses algorithms that evaluate the probability These models consider variables such as They are estimated to predict absences with accuracy close to 80 percent, influencing decisions regarding preventive detention or electronic monitoring. But digital monitoring also affects U.S. citizens who interact with individuals under investigation, since algorithms may include them in analysis networks due to simple social proximity. But despite their effectiveness, artificial intelligence systems present significant risks. Algorithms may contain biases derived from incomplete or unrepresentative data, misclassify criminal records, or generate false positives in facial recognition with error rates of up to 24 percent. An algorithmic error during a raid can have serious consequences, including unjustified detentions. There was an uproar last month following the killings of Rene Good and Alex Preddy by immigration officers, but videos from across the country have shown months of other violent interactions, including with U.S. citizens. The evolution of ICE raids toward an AI-driven model reflects a broader shift in how governments use technology to exercise territorial and social control. The operational efficiency achieved through algorithms, facial recognition, and phone tracking has created a system capable of analyzing daily life with an unprecedented level of detail. As artificial intelligence evolves, the challenge will be balancing security and mass surveillance without undermining the rule of law.