How ICE Uses Surveillance Tech in Mass Deportations

Deportations and a digital toolkit

President Trump made mass deportations a central campaign promise. In the first eight months of his administration that pledge translated into roughly 350,000 removals — about 200,000 attributed to ICE, more than 132,000 by Customs and Border Protection, and nearly 18,000 self-deportations. Alongside boots on the ground, ICE has invested in powerful digital systems to find, identify, and prioritize people for enforcement.

The surveillance stack explained

• Clearview AI facial recognition: ICE has contracted Clearview for multiple tools and licenses. The company scrapes billions of images from the open web to enable near-instant identification of faces from photos or video — a powerful matchmaker for enforcement operations.
• Paragon phone spyware: ICE signed a multimillion-dollar contract for Paragon’s proprietary mobile intrusion tech, which can harvest data from targeted devices. The deal was briefly paused under a review of commercial spyware policies but has been reactivated, raising questions about deployment timelines and oversight.
• LexisNexis public records and analytics: For years ICE has relied on LexisNexis databases for background checks and investigative leads. Public records and commercial data are aggregated to build dossiers and flag 'suspicious' activity before infractions occur.
• Palantir analytics and case management: Palantir supplies an Investigative Case Management database and reportedly an "ImmigrationOS" product that can fuse location, visa status, biometric traits, and other attributes to build filters and operational lists for apprehension.

Taken together, these systems move enforcement from ad hoc to data-driven. That can mean faster identification and more effective operations — but it also concentrates risk. When facial recognition, device-level spyware, comprehensive public records, and advanced analytics are combined, errors, bias, or misuse can scale rapidly and affect entire communities.

Why this matters beyond enforcement

There are practical and ethical consequences. False positives from facial matches can lead to wrongful stops. Phone spyware can sweep unrelated personal data. Aggregated commercial data can criminalize routine community behavior. And opaque analytics make it hard for courts, advocates, and the public to understand or contest targeting decisions.

Civil-rights groups, local governments, and technology teams face hard choices: push back, demand audits and transparency, or try to shape safer use. Tech vendors, whether startups or long-standing data brokers, must also decide where to draw ethical lines — as Paragon and others have faced scrutiny in recent high-profile cases.

A practical path forward

Organizations preparing for or responding to this reality should combine technical audits, impact assessments, and playbooks for governance. That means testing the accuracy of biometric matches, reviewing data retention and sharing policies, and modeling downstream harms from analytics-driven prioritization. Transparency and legal oversight are critical to prevent mission creep.

QuarkyByte’s approach is to treat surveillance stacks as systems engineering problems: map data flows end-to-end, quantify where errors and bias can enter, model operational impacts, and create measurable controls. For governments, that can mean policies that preserve investigative capability while limiting overreach. For NGOs and firms, it means practical audits and scenario simulations to reduce exposure and protect communities.

As enforcement becomes more digitized, the debate about what technologies should be used — and who should have access to them — will only intensify. Policymakers, technologists, and the public need clear evidence, accountable processes, and technical guardrails to ensure that tools meant to protect do not instead produce widespread harm.