GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Text Extraction from Uploaded Images
Agency: Department of Homeland Security - TSA
Problem addressed: Without this capability, images are taken during maintenance but are not searchable and the information can not be used in generating details.
Purpose and benefits: By making images into text, they are searchable and can ensure data quality and maintenance ticketing information quality.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: GenAI for Document Summarization and Content Generation
Agency: Department of Homeland Security - CBP
Problem addressed: Workforce enablement and efficiency through the implementation of Generative AI / Large Language Models (LLM)
Purpose and benefits: Improving Operational Efficiency: Automating information retrieval, reducing manual review time, and streamlining workflows will lead to significant time savings and increased productivity for CBP personnel. This translates to cost savings and allows agents to focus on higher-priority tasks. 

Enhancing Decision-Making: Providing quick and accurate access to relevant information will improve the quality and consistency of decisions across the agency.
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-03-01
US federal AI use case: Smartphone Information Forensics Triage
Agency: Department of Homeland Security - CBP
Problem addressed: Quickly translate and summarize the content of text messages, prompting the inspecting employee to review the actual raw text if warranted.
Purpose and benefits: Save worker time by providing an alternative to lengthy bitwise forensic device inspections through the application of an expedient triage tool; reduce the number of higher-level inspections.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Spend Plan Analysis GPT
Agency: Department of Homeland Security - FEMA
Problem addressed: This tool will augment the wider workforce at FEMA to interrogate spend plan data as stored in PBIS/FEMADex to gain quick insights and rapid responses to data calls or requests for information.
Purpose and benefits: The tool will also allow greater insights for the FEMA leadership to understand where FEMA plans to spend their provided budget authority and where it was actually spent.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: Executive Summary GPT
Agency: Department of Homeland Security - FEMA
Problem addressed: Provides the ability for users to upload documents and provide an executive summary on those documents.
Purpose and benefits: Quickly analyze lengthy or complex documents for relevance to FEMA and/or to provide a high level summary for leadership on potential impacts.
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-04-16
US federal AI use case: Technical Resource for Mitigation Programs
Agency: Department of Homeland Security - FEMA
Problem addressed: The FEMA Hazard Mitigation Assistance (HMA) AI solution addresses the challenge of managing complex grant processes that currently rely on manual review of thousands of applications, modifications, and closeout packages. Analysts must manually extract and reconcile data scattered across multiple nonstandardized systems (NEMIS, PARS, PDFs, spreadsheets), risking delays in obligation and closeout of grants. This fragmentation leads to inconsistent compliance determinations, increased audit risk, and inefficient use of limited staff resources. The initial document scan alone takes 45-60 human minutes per modification, with full reviews requiring 1-2 human days, significantly delaying the release of mitigation funds to communities in need.
Purpose and benefits: The AI solution will deliver significant benefits to both FEMA operations and disaster-affected communities. Operationally, it will reduce document review time by 40-70%, saving 15-20 analyst hours per week to prioritize higher-value activities requiring human judgement and stakeholder interaction. The system will enhance compliance through consistent regulatory interpretation, reducing errors and improving financial calculation accuracy. The AI will identify eligibility concerns in real-time, reducing the risk of funding grants that do not align with federal laws, regulations, and executive orders._x000D_
For the public, the AI will accelerate application review, obligation and closeout of mitigation grants, enabling states, tribes, territories, and local communities to implement risk-reduction projects sooner. This faster release of funds directly enhances public safety and disaster resilience while providing a more consistent application experience across regions.
Topic area: Emergency Mgmt
Development stage: Pilot
First used: 2025-07-11
US federal AI use case: Administrative & Productivity Support for IRC Resource Library
Agency: Department of Homeland Security - FEMA
Problem addressed: Long-term disaster recovery requires analyzing large amounts of information stored in multiple locations. IRC typically reviews past recovery plans and projects, available federal and state funding options, considers community needs, and other recovery strategies used in the event. High-level reviews are necessary for FEMA Program Areas (IA, PA, etc.) on potential funding gaps and cost share support. Synthesizing that information from the various storage locations, including other departments, IRC SharePoint, and TRAX poses some challenges.
Purpose and benefits: This use case is designed to solve these challenges by reducing inconsistency in manual research, improving access to historical knowledge, and helping staff quickly identify relevant recovery resources. The purpose of this tool is to assist in the identification of patterns in current and past disaster events. Earlier identification will expedite situational awareness and the development of recovery needs and strategies. It allows the IRC team to define a recovery approach and deliver funding and resources that match a community’s needs more quickly. 

With faster decision-support for FEMA staff, more consistent analysis across regions, and better use of federal resources to support communities after disasters we are more capable of delivery of the FEMA mission.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Individual Assistance Document Translation
Agency: Department of Homeland Security - FEMA
Problem addressed: This AI use case addresses three inter-related problems: It eliminates delays associated with human translation of documents originally in non-English languages; it reduces the cost of translation from approximately $40.00/document to pennies per document, and the entire document will be translated, providing IA visibility to all the contents (instead of a summary deemed by a contract staff as representative of the original document) and thus making it possible to make fully informed decisions about the survivors’ applications.
Purpose and benefits: AI will be used because it is a technology that can perform direct translation automatically and fast, regardless document length, enabling faster and more accurate case processing, resulting in improved services to survivors and reduced cost to taxpayers.
Topic area: Benefits Processing
Development stage: Pre-deployment
US federal AI use case: Grants Manager Artificial Intelligence ChatBot
Agency: Department of Homeland Security - FEMA
Problem addressed: The chatbot is intended to solve inefficiencies in FEMA’s grants application review process by streamlining access to policy information, reducing the time and effort required for manual research, and simplifying the interpretation of complex policy inquiries. It addresses the need for accurate, consistent, and accessible information to improve decision-making and enhance the efficiency of the PA Program.
Purpose and benefits: The expected benefits include increased operational efficiency, improved accuracy in policy interpretation, and cost savings for FEMA’s mission. By providing quick, standardized responses, the chatbot supports faster and more equitable processing of grants, ensuring disaster survivors receive timely assistance. The system’s analytics and feedback mechanisms allow for continuous improvement.
Topic area: Emergency Mgmt
Development stage: Pre-deployment
US federal AI use case: RTPD Division Services Desktop Automation
Agency: Department of Homeland Security - FEMA
Problem addressed: Our government developers lack the capacity and consistency to produce quality code in a timely manner. Our administrative staff facilitate a multitude of processes that require manual intervention. This requires significant overhead and is prone errors and results in simple steps or actions taking longer than necessary as requests get lost in email or other forms of communication. There is also no audit trail or logging outside of a personal email address or limited access shared mailboxes, making it difficult for others to step in a facilitate activities.
Purpose and benefits: AI Coding Assistants can help identify potential issues with code, help our developers troubleshoot more quickly, and begin complex coding more efficiently.  This will also enable a team of developers to build a consistency across resources and a repository of reusable code segments to speed the delivery of new features and functions.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Public Assistance Workload Projections
Agency: Department of Homeland Security - FEMA
Problem addressed: The use case is predicting recovery program quantities of interest using supervised learning models to include predicting the number of applicants who will apply for Public Assistance, predicting the number of PA projects that applicants will submit, predicting the number of sites that will need to be inspected per PA project, predicting the cost of delivering assistance, etc. Supervised learning models include but are not limited to the use of sample statistics, generalized linear models, decision trees, and deep neural networks for the purpose of predicting unknown quantities.
Purpose and benefits: 1.	For informational purposes: the models will produce predictions for to-be-determined quantities of interest. These quantities are often of interest to Agency personnel in the field, region, and headquarters, as well as DHS, OMB, NSC, and the White House._x000D_
2.	For decisional purposes: in addition to being informative, the model’s predictions are likely to be used for decision making. Projections help inform staffing levels and timing.
Topic area: Emergency Mgmt
Development stage: Deployed
First used: 2018-10-01
US federal AI use case: AI Resume & ATS App
Agency: Department of Homeland Security - FEMA
Problem addressed: Speed and scale: During disasters, resume volume spikes and manual review can’t keep up; Format variability: Resumes arrive as PDFs, Word files, text, and scans, making uniform processing difficult; Consistency: Different reviewers apply criteria differently, leading to uneven shortlists and missed candidates; Traceability and defensibility: Hiring choices must be fast, explainable, and audit ready; Searchability: Teams need to quickly find candidates with specific qualifications (for example, 5+ years of EMS experience); Centralization: Candidate information is scattered across files and events, slowing coordination and handoffs.
Purpose and benefits: How AI helps: Reads every format: Scans and reads PDFs, Word files, text, and images so all resumes can be processed; Extracts key details: Pulls out skills, certifications, education, locations, roles, dates, and years of experience from free form text; Understands the job: Reads job descriptions, separates must have and nice to have qualifications and applies appropriate weights; Matches and scores: Compares each resume to the job and calculates a clear match score; Ranks candidates: Sorts candidates by score to produce a prioritized shortlist; Highlights strengths and weaknesses: Summarizes where a candidate aligns well and where they fall short; Flags critical gaps: Calls out missing must have requirements (for example, licenses, certifications, clearances, or minimum years); Explains results: Shows the evidence behind each recommendation, what matched and what didn’t; Conversational search (optional): Lets HR ask plain language questions about the candidate pool (for example, “Show EMS candidates with 5+ years in Region 2”); Human oversight: Routes sensitive or low confidence cases to HR/SMEs for review before moving forward.
Benefits: Faster: Processes resumes in seconds and handles very large volumes during surge events; More consistent: Applies the same criteria to every resume, reducing variation across reviewers; Better decisions: Ranked lists with clear reasons improve triage and interview selection; More efficient: Cuts manual screening time so staff can focus on final se-lection and onboarding; Transparent and reviewable: Captures inputs, scores, explanations, and reviewer actions to support audits and continuous improvement.
Topic area: HR
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Individual Assistance (IA) Predictive Models for Program Quantities
Agency: Department of Homeland Security - FEMA
Problem addressed: The use case is predicting recovery program quantities of interest using supervised learning models to include predicting the number of applicants who will apply for Individual Assistance, how many inspections will be issued, and how many units are required for direct housing. Supervised learning models include but are not limited to the use of sample statistics, generalized linear models, decision trees, and deep neural networks for the purpose of predicting unknown quantities.
Purpose and benefits: The models are intended to quickly quantify and reduce uncertainty around key quantities of interest to enable better programmatic decision making, such as workload management, pre-placement of staff, etc.
Topic area: Emergency Mgmt
Development stage: Deployed
First used: 2019-02-01
US federal AI use case: Program Integrity (RRAD-PI) AI Counter-Fraud Enhancement Measures
Agency: Department of Homeland Security - FEMA
Problem addressed: The AI use case aims to further expand and address RRAD-PI’s fraud detection and prevention challenges within disaster recovery programs. Specifically, it seeks to mitigate fraudulent activities, identity theft, and deceptive practices that compromise program integrity, ensuring that resources are allocated efficiently and equitably to eligible individuals and entities.
Purpose and benefits: The expected benefits include:_x000D_
•	Enhanced Fraud Detection: Improved identification of fraudulent activities, reducing financial losses and ensuring program integrity._x000D_
•	Operational Efficiency: Automation of manual processes, such as document review and identity verification, leading to faster application processing and reduced administrative burden._x000D_
•	Proactive Fraud Prevention: Early detection of fraud risks enables timely intervention, minimizing harm and protecting public funds._x000D_
•	Improved Resource Allocation: Ensures disaster recovery resources are distributed to legitimate recipients, fostering public trust in government programs._x000D_
•	Cross-Agency Collaboration: Facilitates secure data sharing across agencies, enabling a unified approach to combating fraud schemes that span jurisdictions._x000D_
•	Public Confidence: Strengthened program integrity enhances public trust in the agency’s ability to manage disaster recovery efforts effectively.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Semantic Search, Summarization, and Data/Spatial Visualization for NCR Watch COP/Dashboard
Agency: Department of Homeland Security - FEMA
Problem addressed: The speed of information, particularly in a developing incident, challenges the capacity of Analysts to collect, sort, source, and validate in real time.  The volume of information required to confirm with confidence requires time to review.  The time focused on these tasks permit less time for moving from basic situational awareness to providing the contextual value to give national capital region (NCR) stakeholders situational understanding, which allows them to fully utilize the information for operational decision-making.  The AI would provide a tool for collection and synthesize of the available data, as well as geospatial context.
Purpose and benefits: Expedited continuous scraping of data from identified official and unofficial open sources and comparison against pre-defined critical information requirements with summarization and sourcing of information for review and approval by the FEMA NCR Watch Analyst for publication improves the efficiency and effectiveness of the NCR Watch by allowing Analysts to focus on consuming and validating information and adding value with context.  The self-designated federal, state, local, and tribal (FSLT) partners in the NCR who receive NCR Watch products would benefit from greater fidelity in provided information and ability to use the information more easily for operational decision-making.  Anticipate the breadth of sources and speed of review through automation to be increased and allow for created contextualization and analysis products by Analysts based on the improved data production, as well as geospatial automation in combination with incident data. Faster Decision-Making: vast amounts of data instantly, enabling rapid identification of critical information for timely responses. Improved Accuracy: potential of reducing human error by filtering irrelevant data and prioritizing actionable insights. Enhanced Situational Awareness: improve the breadth of information analysis and overall awareness of activities in the NCR. Resource Optimization: helps allocate resources efficiently based on real-time needs. Predictive Insights: forecast potential developments, aiding proactive measures, and reduced Information Overload: streamline data, ensuring decision-makers focus on key priorities.
Topic area: Emergency Mgmt
Development stage: Pre-deployment
US federal AI use case: Large Language Model (LLM) Guided Data Dictionary Generation
Agency: Department of Homeland Security - FEMA
Problem addressed: This LLM-generated data dictionary eases the burden of metadata documentation on the data stewards when integrating their data into FEMADex by creating field definitions.
Purpose and benefits: By utilizing this LLM-generated data dictionary, it automatically creates field definitions, saving data stewards significant time and effort during data onboarding.
Topic area: IT
Development stage: Pilot
First used: 2025-09-01
US federal AI use case: Facial Recognition for National Security and Transnational Criminal Organizations
Agency: Department of Homeland Security - CBP
Problem addressed: USBP works to address challenges in identifying individuals who may be associated with national security concerns or transnational criminal organizations, especially in situations where traditional identification methods are unavailable or insufficient. To support this effort, USBP utilizes facial recognition technology to generate investigative leads by providing visually similar photos of unidentified individuals. These leads serve as a starting point for analysts to conduct further investigation. No enforcement action is taken based solely on the leads generated by this tool. All potential identifications undergo thorough investigation and validation to ensure accuracy and compliance with established standards. This approach reflects USBP's commitment to responsibly leveraging technology to enhance national security and combat transnational criminal activities.
Purpose and benefits: The intended purpose of this facial recognition technology is to assist USBP agents in addressing the challenges of identifying individuals who may be linked to national security threats or transnational criminal organizations. This capability enhances USBP's ability to develop investigative leads in cases where traditional identification methods are unavailable or insufficient. The benefits include improved efficiency in generating leads, enhanced support for national security and criminal investigations, and the ability to address complex threats more effectively.
Topic area: Law Enforcement
Development stage: Pilot
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI utilizes publicly available images and performs a comparative search against other open-source images to identify potential matches. Results produced by the AI can only be utilized for investigative leads and cannot be utilized as a determining factor for any enforcement action by CBP personnel. Therefore, in instances where the AI outputs assist in identifying leads, CBP personnel must then use internal CBP or USG data prior to making any law enforcement decisions or actions. The outputs of the AI do not serve as the principal basis for a high-impact decision or action.
First used: 2025-10-09
US federal AI use case: Mobile Fortify
Agency: Department of Homeland Security - CBP
Problem addressed: The "Mobile Fortify" application utilizes CBP's facial comparison or DHS's fingerprint matching to quickly verify subjects of interest during operations.
Purpose and benefits: Utilizing facial comparison or fingerprint matching services, agents/officers in the field are able to quickly verify identity utilizing trusted source photos.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-05-01
US federal AI use case: Risk Management Framework (RMF) Automation
Agency: Department of Homeland Security - USCG
Problem addressed: Provides feature to help users understand complex security and privacy controls into plain language, monitoring of system/security, and generates documents such as FedRamp packages
Purpose and benefits: RMF Automation will cut ATO processing time by more than half by handling repetitive tasks so compliance teams can focus on strategy and still make final decisions. It will speed up documentation and assessments, provide near real-time risk insights, and help collect and manage security evidence to demonstrate compliance.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: FLIR 280 HD
Agency: Department of Homeland Security - USCG
Problem addressed: Rapid detection of distressed persons in the water.
Purpose and benefits: Enhanced ability to detect persons in the water to more rapidly deploy life-saving resources.
Topic area: Other
Development stage: Pilot
Risk category: High-impact
First used: 2024-09-15
US federal AI use case: Real-Time Language Translation Services
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of ICE personnel often lacking timely access to interpreters in offline or low-connectivity environments, making it difficult to communicate with individuals who speak little or no English during operations.
Purpose and benefits: These tools help reduce delays caused by language barriers in the field and support clearer two-way communication during routine, special, and emergency operations. They reduce reliance on ad hoc workarounds when interpreters are not immediately available and allow personnel to focus more on mission activities rather than the logistics of basic communication.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: AI-Assisted eDiscovery Search
Agency: Department of Homeland Security - ICE
Problem addressed: The use case addresses inefficiencies in traditional document search and retrieval processes, such as time-consuming manual searches, the complexity of crafting SQL queries, low relevance and accuracy in search results, difficulties in digesting lengthy content, and the absence of summarization tools. These challenges hinder users' ability to efficiently access and understand critical information.
Purpose and benefits: The intended purpose of this AI use case is to optimize document search, retrieval, and summarization processes by enabling users to interact with data conversationally and efficiently. The benefits include improved accuracy and relevance in search results, reduced time spent analyzing large datasets, simplified access to complex information, and enhanced decision-making through concise summaries and precise outputs.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AI-Powered Developer Tools
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of time‑consuming manual developer tasks, including debugging code, writing database queries, and analyzing system metrics, which slows application development and makes it harder to quickly identify and fix issues.
Purpose and benefits: By streamlining routine coding tasks and surfacing useful system insights, AI-enabled developer tools increase developer productivity and support faster, higher-quality delivery of software across ICE.
Topic area: IT
Development stage: Deployed
First used: 2025-04-15
US federal AI use case: Open-Source Intelligence for Investigations
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of analysts having to manually search and make sense of vast amounts of multilingual, multimodal publicly available online data, which makes it difficult to efficiently identify relevant identifiers, high‑risk content, and patterns needed to support Homeland Security Investigations (HSI) investigations.
Purpose and benefits: These AI tools significantly reduce the time and effort required to sift through large datasets, improve the ability to uncover relevant information, and enhance the overall efficiency and effectiveness of HSI’s investigative operations.
Topic area: Law Enforcement
Development stage: Deployed
First used: 2023-09-01
US federal AI use case: Customs Broker License Exam - Proctor Support
Agency: Department of Homeland Security - CBP
Problem addressed: Detect potential cheating during the Customs Broker License Exam.
Purpose and benefits: The model supports remote proctoring of the exam and ensure the integrity of the testing process, by ensuring the exam is conducted under secure conditions, preventing cheating or fraud, while also verifying the identity of exam takers to confirm they meet the necessary requirements.
Topic area: HR
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: This is not a high impact case because this would only be used in for examinees taking the Customs Broker License Exam and not used in requests for federal services, processes, and benefits to include loans and access to public housing. This is just a feature that the CBP exam vendor uses to detect cheating during the exam.
First used: 2023-03-31
US federal AI use case: Integrated Defense and Security Solutions (IDSS)
Agency: Department of Homeland Security - CBP
Problem addressed: This system provides assistance in detecting high-risk parcels in the international commerce space
Purpose and benefits: The system improves the screening efficiency and accuracy of contraband detection in international express consignment and mail inspection.
Topic area: Law Enforcement
Development stage: Pre-deployment
US federal AI use case: Advanced Analytics for X-ray Images (AAXI)
Agency: Department of Homeland Security - CBP
Problem addressed: Reduction of low risk empty commercial vehicles.
Purpose and benefits: AAXI aims to address the problem of anomaly detection in empty commercial vehicles entering the United States at land border ports of entry. The AI models achieve this goal by encoding past X-Ray images of vehicular border crossings in a semantically meaningful way and comparing the current crossing to detect differences amongst the images to identify anomalies. Benefits include enhancement of the capability of humans to consistently detect items of interest/concern present (and possibly concealed) in vehicles crossing into the United States, and increased clearance rate at border crossings so that vehicles operating safely and lawfully may pass through the border faster.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: Advance RPM Maintenance Operating Reporter (ARMOR)
Agency: Department of Homeland Security - CBP
Problem addressed: Advance Radiation Portal Monitor (RPM) Maintenance Operating Reporter (ARMOR) project provides predictive maintenance of RPMs, detecting issues with the equipment before the issue causes the screening lane to be inoperable
Purpose and benefits: ARMOR will shorten time to service/repair/maintenance of radiation portal monitors by two weeks. ARMOR will allow better distribution of resources (travel, spare parts, etc.) and expected cost decrease could be 25-50%. Through decreased outage time, and prediction of equipment degradation, ARMOR will increase radiological/nuclear (R/N) security on US borders.
Topic area: Admin Functions
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: ERNIE
Agency: Department of Homeland Security - CBP
Problem addressed: ERNIE is used to analyze Radiation Portal Monitor (RPM) data to enhance the detection of radioactive materials. It provides a more efficient review of stream of commerce radiation portal monitor data and provides real-time risk assessments and alerts for potential threats
Purpose and benefits: The model enhances threat detection and prioritizes high-risk targets, improving operational efficiency and national security.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2017-10-01
US federal AI use case: RAPTOR (Rapid Tactical Operations Reconnaissance)
Agency: Department of Homeland Security - CBP
Problem addressed: Provide Tactical Domain Awareness for CBP Agents, making law enforcement efforts more efficient.
Purpose and benefits: RAPTOR will significantly increase domain awareness and the agency’s ability to engage in intelligence-driven operations.  The AI capability acts as a force multiplier and saves personnel from analyzing video feed from a stationary camera and manually noting all boat identifiers, improving their ability to respond quickly to potential threats and gather critical intelligence for law enforcement and border control operations.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI system processes data from radar, infrared sensors, and video surveillance to detect and track suspicious activities along U.S. borders. By incorporating AI-powered vessel registration, aircraft tail number, license plate, and object detection RAPTOR significantly boosts domain awareness. If the OCR can capture vessel or license plate information, that information is run through CBP Super Query and if derogatory information is returned or other information from the imagery (high number of gas cans in the image) the information is sent through SMS or email to the field for response to the potential activity. Agents log into RAPTOR and reviews for accuracy/validity of image to avoid any investigation of the wrong boat.  Neither the AI or output of AI serves as a principal basis for a decision or action. Human review takes place prior to any final decision to act, and then personal interaction leads to any follow-up decisions.
US federal AI use case: Credential Authentication Technology with Camera System (CAT-2) and AutoCAT (CAT-2 in an e-gate form factor)
Agency: Department of Homeland Security - TSA
Problem addressed: Improving the detection of imposters.
Purpose and benefits: The Transportation Security Administration (TSA) uses AI-based, one-to-one (1:1) and one-to-many (1:n) facial matching technologies at some checkpoints to assist human reviewers with traveler identity verification. The purpose and expected benefits of the technology include increased speed and accuracy of identity verification at the checkpoint while improving detection of imposters.
Topic area: Transportation
Development stage: Deployed
Risk category: High-impact
First used: 2023-09-01
US federal AI use case: PreCheck Touchless Identity Solution
Agency: Department of Homeland Security - TSA
Problem addressed: Assist human reviewers with traveler identity verification.
Purpose and benefits: TSA is using Facial Comparison to verify a passenger’s identity at its security checkpoints locations using the CBP Traveler Verification Service (TVS). This process streamlines passenger identity verification, increasing the speed of security checks while maintaining a high degree of safety for all passengers and crewmembers.
Topic area: Transportation
Development stage: Deployed
Risk category: High-impact
First used: 2018-10-01
US federal AI use case: Geospatial Damage Assessments
Agency: Department of Homeland Security - FEMA
Development stage: Retired
US federal AI use case: Autonomous Surveillance Tower
Agency: Department of Homeland Security - CBP
Problem addressed: CBP's limited manpower constrains its ability to manually monitor all areas of the border at all times. To account for this limitation, surveillance and sensor systems assist in monitoring the border.
Purpose and benefits: AST machine learning assisted system is augmenting the U.S. Border Patrol by enhancing the capabiltiies of individual users when carrying out the domain awareness mission. 
The expected benefit is to have ability of single person to monitor magnitude greater area than could be done with conventional CCTV or human surveillance. The ultimate outcome for the agency and the public is greater availability of the agents to solve and address more complex tasks and allow for better strategic/tactical deployment of existing resources and personnel.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI outputs do not serve as a principal basis for a decision or action concerning a specific individual or entity that has a legal, material, binding, or similarly significant effect on that individual’s or entity’s civil rights, civil liberties or privacy, equal opportunities, or access to or the ability to apply for critical government resources or services. The AI provides alerts when it detects the presence of an IoI (i.e., persons, vehicles, animals) in the image frame. With regard to persons, this computer vision application is trained to determine if the object in the image frame is a person with a certain of level of confidence and not another object that may be shaped similarly to a person. After the alert of a detection, a trained agent or user, reviews the image to identify and classify the activity taking place. The AI merely alerts to the presence of an item it was trained to detect. This is not a biometric system and does not identify or track specific individuals.
First used: 2020-01-01
US federal AI use case: Facial Recognition for Investigations of Child Sexual Exploitation and Abuse
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of identifying unknown victims and offenders depicted in child sexual abuse material.
Purpose and benefits: The tool helps more quickly identify previously unknown victims and offenders who might not be discovered through manual investigative methods alone. By highlighting potentially relevant photographs or profiles across publicly available online images, it can accelerate victim identification and rescue efforts and support the disruption and prosecution of offenders who might otherwise remain undetected.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2020-12-01
US federal AI use case: Consular Consolidated Database (CCD) Facial Recognition (FR) On Demand Report (VISA Only)
Agency: Department of Homeland Security - DHS
Problem addressed: On-demand facial recognition services.
Purpose and benefits: DHS Components use the Facial Recognition (FR) on Demand report (Visa only) to combat fraud by benefit applicants whose fingerprints are not in IDENT but who may have photos in the Department of State's (DoS) Consular Consolidated Database (CCD) that predate the fingerprinting of visa applicants.
Topic area: Admin Functions
Development stage: Deployed
Risk category: High-impact
First used: 2019-04-01
US federal AI use case: AI Interview Simulator for Officer Training
Agency: Department of Homeland Security - USCIS
Problem addressed: By simulating realistic applicant responses to officer questions, the AI Interview Simulator enables the RAIO trainees to refine their interview techniques without requiring any additional resources from experienced officer trainers or peers.
Purpose and benefits: The AI Interview Simulator mimics live interviews to provide an analysis of the type of responses elicited in a mock interview.  This training platform accelerates the competency of the interviews by providing a format for the trainees to practice soliciting testimony from applicants through a chat-based user interface.
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-09-08
US federal AI use case: Commercial Generative AI for Text Generation (AI Chatbot)
Agency: Department of Homeland Security - DHS
Development stage: Retired
US federal AI use case: Commercial Generative AI for Image Generation
Agency: Department of Homeland Security - DHS
Development stage: Retired
US federal AI use case: Automated Item of Interest Detection
Agency: Department of Homeland Security - CBP
Problem addressed: The software is designed to analyze photographs and video feeds captured by field imaging equipment for review by U.S. Border Patrol (USBP) agents and personnel. Using proprietary software, the system processes and annotates images to identify whether they contain human subjects, animals, or vehicles. The system is designed to incorporate future enhancements that expand its detection capabilities and to improve accuracy based on user feedback.
Purpose and benefits: The software analyzes images and video that are taken by operationally deployed equipment, which are then fed into CBP systems for review by USBP agents, Office of Field Operations (OFO) officers, and other CBP users. It provides quick identification of people either crossing into the U.S. at a time and place other than designated for entry, circumventing security at a port of entry, or those already inside the U.S. trying to elude capture, as well as the ability for human operators to quickly determine if subjects in an image are, in fact, human.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI outputs do not serve as a principal basis for a decision or action concerning a specific individual or entity that has a legal, material, binding, or similarly significant effect on that individual’s or entity’s civil rights, civil liberties or privacy, equal opportunities, or access to or the ability to apply for critical government resources or services. The AI runs on video and images captured from lawfully deployed technologies used to support the U.S. Border Patrol mission between Ports of Entry. The AI provides alerts when it detects the presence of an IoI, such as persons, vehicles, animals in the image frame. With regard to persons, this computer vision application is trained to determine if the object in the image frame is a person with a certain of level of confidence and not another object that may be shaped similarly to a person. After the alert of a detection, a trained agent or user, reviews the image to identify and classify the activity taking place. The AI merely alerts to the presence of an item it was trained to detect.
First used: 2020-01-31
US federal AI use case: User Entity and Behavior Analytics (UEBA) for Security Operations (SecOps) Anomaly Identification
Agency: Department of Homeland Security - USCIS
Problem addressed: User Entity and Behavior Analytics (UEBA) assists USCIS Security Operations (SecOps) in identifying behavioral anomalies that most likely indicate malicious intent or heightened risk associated with user identities and endpoint hosts accessing the USCIS network. The analytics provide risk scoring, which helps USCIS SecOps to prioritize highest risk incidents first.
Purpose and benefits: UEBA's purpose is to review USCIS system logs to determine when an entity is performing actions that are anomalous. An entity can be classified as a workstation, server or an internal USCIS system account. The UEBA ingests logs from systems to perform analytics based off of models that are manually created and maintained. UEBA uses the models to apply a risk score to the entity which the risk score is then used to create a case (or ticket) for Security Operations analyst review. The AI reviews the action of the analyst to adjust the risk scoring for future events. Output would assist in prioritizing cyber events for further manual investigation.
Topic area: Cybersecurity
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case collates information provided by or authorized to be collected by an employee as part of network security process. The collated information supports decisions about staffing priorities but is not used to make security-related or employment-related decisions.
First used: 2024-08-17
US federal AI use case: Commercial Generative AI for Code Generation
Agency: Department of Homeland Security - DHS
Development stage: Retired
US federal AI use case: TSA Contact Center Virtual Assistant
Agency: Department of Homeland Security - TSA
Problem addressed: The AI use case is designed to enhance customer service accessibility, improve operational efficiency, and provide valuable data insights to support TSA's mission and decision-making processes:
1. 	Limited Accessibility and Availability: The chatbot addresses the challenge of providing timely and accessible responses to public inquiries, especially outside the TSA Contact Center's operational hours.
2.	Increased Demand for Customer Support: By automating responses to routine inquiries, the chatbot mitigates the strain on TSA staff caused by growing demand for customer support.
3.	Resource Constraints and Fiscal Responsibility: The chatbot reduces the need for additional human resources to handle routine inquiries, thereby improving operational efficiency and fiscal responsibility.
4.	Lack of Data-Driven Insights: By capturing and analyzing customer interaction data, the chatbot enables TSA to gain insights into customer needs, improve public information, and prioritize innovation and transformation efforts.
5.	Consistency and Accuracy of Information: The chatbot ensures that responses to public inquiries are consistent, accurate, and aligned with the TSA's knowledge library.
Purpose and benefits: The three goals for TSA's Virtual Assistant Chatbot are:
1.	Enhanced Accessibility and Timeliness: By providing immediate responses both within and outside the TSA Contact Center's (TCC) operational hours, the Virtual Assistant improves ease of access for the public seeking answers to common questions.
2.	Data-Driven Innovation and Transformation: The chatbot captures customer inquiries, providing valuable data to inform innovation and transformation priorities across the agency.
3.	Improved Fiscal Responsibility: Leveraging automation to address increasing demand mitigates the need for additional resources, thereby enhancing fiscal responsibility.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Vessel Detection
Agency: Department of Homeland Security - CBP
Problem addressed: The system uses AI-enhanced technologies and analytics to improve maritime detection and tracking in areas with significant trade and recreational water vessel activity. The system increases situational awareness and responsiveness to potential threats, assisting human operators in identifying and classifying vessels of interest. Agents can define a search area with specific criteria, which is transmitted to sensors. Detected images are analyzed by AI algorithms that filter, detect, and categorize objects into Items of Interest (IoI) or other objects. IoIs are shared across detection systems and tracked seamlessly across multiple sensors, while non-relevant objects are excluded. This approach enhances efficiency in detecting and addressing IoIs, particularly during high-traffic periods, by providing alerts and tracking information to human operators.
Purpose and benefits: Current surveillance technology does not have machine assisted classification of targets on screen, making it harder for human operators to decipher legitimate from illegitimate traffic in times of high volumes of legitimate traffic.  Project intends to support human operator identification and classification of potential illicit vessels.  Benefits would be more efficient detection and resolution of IOIs, especially during times of high-volume traffic.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The computer vision application scans a large grid within the viewshed of the high-definition camera. It then attempts to detect small vessels detecting entering and exiting small waterways in and around the area the camera is deployed.  System is calibrated to detect to identify inbound and outbound traffic.  When it detects a vessel it sends and image to the cloud for user review and determination if any follow-up action is required.  The AI output does not serve as a principal basis for decisions or actions related to the definition of High-Impact AI.
First used: 2025-07-28
US federal AI use case: Unified Processing/Mobile Intake
Agency: Department of Homeland Security - CBP
Problem addressed: The need to use every available resource to identify individuals who may pose a threat to national security or may be members of terrorism or transnational criminal organizations.
Purpose and benefits: The objective is to facilitate the swift and accurate biometric identification of individuals encountered by CBP, thereby expediting their processing.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2022-03-01
US federal AI use case: Cyber Threat Analysis (Recorded Future)
Agency: Department of Homeland Security - CBP
Problem addressed: The system uses the Recorded Future's platform to streamline CBP Cyber Threat Intelligence (CTI) operations by automating the identification and analysis of relevant cyber threat activity and leverages AI/ML to transform unstructured text into structured data using natural language processing, classify events and entities to prioritize threats, forecast events through predictive modeling, and represent structured knowledge using ontologies. It enables analysts to rapidly assess vulnerabilities in CBP’s IT environment, identify adversary data, and generate cyber risk assessments, providing actionable intelligence to enhance efficiency and support Security Operations and Cyber Risk Management investigations.
Purpose and benefits: Cyber Threat Analysis quickly populates query results when searching against adversary tactics, techniques, and procedures, establishing a threat scorecard. This service can also provide cyber risk scorecards for third party vendors, companies, and organizations.
Topic area: Law Enforcement
Development stage: Deployed
First used: 2024-06-20