Urgent.News

the world's headlines, one feed

Editions

Tech

ICE to Pay LexisNexis Millions for Data to Feed to Palantir

LexisNexis sells a massive amount of data to law enforcement, including ICE. New records indicate ICE wants to feed that data into a Palantir system.

ICE to Pay LexisNexis Millions for Data to Feed to Palantir

The Immigration and Customs Enforcement (ICE) agency plans to pay LexisNexis millions of dollars for continued access to data that will be used to feed into a Palantir platform, according to newly published procurement records. This data will assist ICE’s Enforcement and Removal Operations (ERO) in deportation efforts. The records reveal the types of information ICE intends to process through the Palantir platform.

As reported by 404 Media in January, ELITE, a system developed by Palantir for ICE, helps identify neighborhoods to target by compiling data from private suppliers and government agencies. In July, ICE arrested more than 51,000 people, many of whom were apprehended at airports. The new data agreement will support "all aspects of ICE screening and vetting, lead development, and criminal analysis activities," and include identifying potential fraud before it can materialize and detecting crime involving immigration and customs law exploitation or attempted exploitation.

ICE anticipates paying LexisNexis $6.7 million for this service. LexisNexis has provided data to ICE across multiple administrations, and this new procurement record confirms the agency's intention to continue access to LexID and Accurint Virtual Crime Center. LexID and Accurint are tools that combine a vast amount of data from over 10,000 sources to help law enforcement locate individuals.

The document specifies that the database must be able to interface with ICE applications such as the Palantir platform, PenLink, and ICE Data Analytics. PenLink, a company ICE contracts with, offers social media monitoring tools and Webloc for tracking mobile phone locations in entire neighborhoods. While the specific details of how LexisNexis' data will be integrated into ELITE or other Palantir platforms are unclear, the document lists several AI-related requirements, including an AI-driven identification system and a facial recognition system capable of high-accuracy facial matching across diverse sources. LexisNexis, Palantir, and ICE have not yet responded to requests for comment.

Written by urgent.news from 404 Media's reporting — not their text. Machine-written — it may contain errors, so check the original before relying on it.

Read the original at 404media.co →

More in Tech

Where exactly is the card in this photo? Image segmentation model inside a maxed-out lambda container

A crooked phone photo goes in, a clean straight card image comes out - no GPU, and nothing running when nobody uploads. It runs on the biggest lambda AWS sells, and the biggest is not the same as…

  • BiRefNet-General-Lite model used for image segmentation
  • Memory constraints addressed by reducing container memory and resizing images
  • Model chosen for efficient CPU-based segmentation in Lambda container

JWT auth without the confusion

JWT auth without the confusion JWTs are everywhere, but they're often misunderstood. Let's strip away the jargon and see what they actually are, how they work, and how to use them safely in your apps.

  • JWTs consist of three segments: header, payload, and signature
  • Authentication involves login, token creation, and token inclusion in requests
  • Avoid storing tokens in localStorage due to XSS vulnerability

Building Resilient Real-Time Systems: WebSockets, Redis, and High Availability Architectures

Originally published on tamiz.pro . Building real-time systems that are not only fast but also resilient and highly available is a critical challenge in modern software architecture.

  • WebSockets provide persistent bidirectional communication for real-time applications
  • Redis serves as a message broker with Pub/Sub mechanism for state and messaging
  • High availability achieved through horizontal scaling, Redis Sentinel, and clustering