DHS-2577 – Mobile Fortify | DHS AI Use Case Inventory

Problem Statement

The AI is intended to solve the problem of confirming individuals’ identities in the field when officers and agents must work with limited information and access multiple disparate systems to identify individuals and retrieve existing data relevant to enforcement, investigations, and victim protection activities.

Expected Benefits

The use of AI in this process increases the speed and efficiency of identifying individuals and organizing identity information, supporting immigration enforcement, authorized investigations, and victim protection efforts.

System Outputs

Mobile Fortify runs on a mobile device and can capture facial images, contactless fingerprints, and photographs of identity documents. The application transmits this data to U.S. Customs and Border Protection (CBP) for submission to government biometric matching systems. Those systems use AI-based matching techniques, including facial recognition and fingerprint matching, to compare the captured data against existing records and return possible matches with associated biographic information. The tool also uses optical character recognition to extract text from identity documents to support additional checks. ICE does not own or interact directly with the AI models that perform biometric matching or optical character recognition. CBP owns and operates these models, and Mobile Fortify simply displays the results to ICE users. For additional details on the AI models that support the application, see CBP’s Mobile Fortify AI use case.

Documentation

Operational Date: 5/20/25

Procurement: c) Developed with both contracting and in-house resources

Vendor(s): NEC is the third‑party vendor CBP uses. ICE accesses these capabilities through CBP and does not contract directly with NEC.

ATO: Yes

System Name: TVSI, ATS-C

Data & Code

Training Data: ICE does not own and did not train, test, or evaluate the AI models that power the Mobile Fortify application. See CBP’s Mobile Fortify AI use case for details on the application’s underlying AI models.

PII Involved: Yes

Demographic Variables: None

Custom Code: Yes

Risk Management

Pre-deployment Testing

b) In-progress

Impact Assessment

b) In-progress

Independent Review

d) In-progress

Ongoing Monitoring

b) Development of monitoring protocols is in-progess

Operator Training

b) Development of monitoring protocols is in-progess

Fail-safe

c) In-progress

Appeal Process

c) Establishment of an appropriate appeal process is in-progress

End User Feedback

In-progress

Potential Impacts

In-Progress - potential impacts will be identified during AI Impact Assessment.