GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Low Probability of False Alarm (Low-Pfa) Algorithm for on-person screening.
Agency: Department of Homeland Security - TSA
Problem addressed: Increase passenger throughput via improve detection performance and decreasing alarm rates and passengers touch rates by 50%
Purpose and benefits: The purpose is to reduce alarm rates while providing increased passenger throughput and experience.

Utilizes Machine Learning (ML) to improve detection performance while decreasing alarm rates and passengers touch rates. The algorithm is gender agnostic which no longer requires officers to select a passengers gender prior to being scanned. Advanced imaging technology (AIT) throughput and AIT utilization have increased with this new algorithm. Note: Once the algorithm is trained, it is locked down and no longer learning.
Topic area: Transportation
Development stage: Deployed
Risk category: High-impact
First used: 2022-12-12
US federal AI use case: Biometrics Enrollment Tool (BET) Fingerprint Quality Check
Agency: Department of Homeland Security - USCIS
Problem addressed: This effort aims to maximize the number of successful FBI submissions while minimizing the number of fingerprint recaptures necessary. The output is a Numerical Fingerprint Quality score, which is compared against fingerprint quality thresholds (per finger and per set of fingerprints) to align with FBI specifications.
Purpose and benefits: BET assists in determining if the fingerprint taken is good enough quality to pass the FBI fingerprint check process. It provides immediate feedback when a set of prints is likely to be rejected by the FBI by incorporating machine learning models into the BET application. The FBI will not disclose their quality grading criteria for fingerprints, leaving BET with the responsibility of determining quality to prevent unnecessary secondary encounters with applicants.
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. This tool simply identifies whether a fingerprint collected is of sufficient quality to pass the FBI fingerprint check process, ultimately maximizing the number of successful FBI submissions while minimizing the number of fingerprint recaptures necessary. This quality assurance step is one task in a series of adjudication activities but is not determinative of the overall adjudication decision. The tool saves personnel time and resources while enhancing customer experience by helping to ensure that only quality fingerprints are passed forward for matching against the FBI Identity History Summary Check. Results of FBI fingerprint checks are subsequently reviewed by a human as part of the immigration adjudicative process.
First used: 2024-01-01
US federal AI use case: ELIS Evidence Classifier Service
Agency: Department of Homeland Security - USCIS
Problem addressed: The adjudicators and contractors spends too much time sifting through digital evidence documents for relevant information.
Purpose and benefits: To enable end users to navigate directly to the page(s) containing evidence documents of interest instead of sifting through large PDF documents. Evidence tagging intends to accelerate case processing by identifying specific types of documents (e.g., I-589, passport photo spread, marriage certificate) and applying a metadata tag to that document object in ELIS. This way, when a user opens a case with potentially hundreds of pages of evidence documents, rather than scrolling through them one at a time to find a specific document of interest, they have clickable "bookmarks" in the UI generated from these tags that will jump directly to the corresponding page.
Topic area: Service Delivery
Development stage: Deployed
First used: 2020-09-01
US federal AI use case: Non-Intrusive Inspection (NII) 3D Imaging Tool
Agency: Department of Homeland Security - CBP
Problem addressed: Use millimeter wave data to produce human-interpretable 3D images and cue end users to possible anomalies, helping CBP more effectively and efficiently detect contraband in imported mail at a speed that does not disrupt flow of commerce.
Purpose and benefits: Utilizes AI/ML to generate high resolution, rapid imaging of objects behind occlusions; create 3D images for existing processes without significant slowdowns; and provide a novel narcotics detection capability for the inspection of packages.
Topic area: Law Enforcement
Development stage: Pilot
First used: 2025-04-01
US federal AI use case: Automated Data Annotation
Agency: Department of Homeland Security - CBP
Development stage: Retired
US federal AI use case: Case Processing Improvements in FDNS-DS NexGen
Agency: Department of Homeland Security - USCIS
Development stage: Retired
US federal AI use case: Video Analysis Tool (VAT)
Agency: Department of Homeland Security - ICE
Development stage: Retired
Risk category: High-impact
US federal AI use case: Adaptive Risk Model for Inspected Small Passenger Vessels
Agency: Department of Homeland Security - USCG
Problem addressed: A lack of a comprehensive data-driven tool for informing marine inspection policy has left policymakers to make decisions based on qualitative and anecdotal information, resulting in less-than-optimal allocation of limited marine inspection resources.
Purpose and benefits: The Small Passenger Vessels Safety Task Force uses machine learning and expert input to build a flexible analysis tool that identifies the main causes of marine casualties and calculates a risk score for each vessel in the largest segment of the U.S.-inspected fleet. By using a logistic regression–based model with basic machine learning, this effort improves how inspectors are allocated, sharpens the focus on higher-risk vessels, and strengthens oversight to improve passenger safety.
Topic area: Admin Functions
Development stage: Deployed
First used: 2021-01-01
US federal AI use case: Automated Name and Date of Birth (DOB) Harvesting Service
Agency: Department of Homeland Security - USCIS
Problem addressed: Adjudicators spend significant amount of time manually harvesting aliases and dates of birth (DOBs) from identity history summary (IdHS) report attached to the ELIS case as part of the Manual Name Harvesting Task during case processing.
Purpose and benefits: To reduce the amount of adjudicative time spent manually harvesting aliases and dates of birth (DOBs) from identity history summary (IdHS) report attached to the ELIS case as part of the Manual Name Harvesting Task during case processing.
Topic area: Service Delivery
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The use case improves case processing efficiency by reducing the amount of time USCIS staff must spend to manually find aliases and dates of birth (DOBs) in existing records of an individual. 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 use case increases efficiency of tasks associated with accurate and timely identification, analysis, and review of biographical information needed for adjudication. The AI outputs are suggested aliases and DOBs related to the individual query, which USCIS staff must review to accept, reject, or ignore the suggested information. The AI outputs reduce the amount of adjudicative time spent manually harvesting aliases and DOBs. The use case increases efficiency of tasks associated with reviewing existing records for adjudicating requests for immigration benefits. Completing such adjudications are not dependent on the use case however lack of this tool would significantly increase human processing times and potentially reduce the accuracy of information consulted during the human review process.
First used: 2022-06-27
US federal AI use case: Automated Realtime Global Organization Specialist (ARGOS) for Company Registration Submissions to E-Verify
Agency: Department of Homeland Security - USCIS
Problem addressed: The goal of the use case is to leverage sentiment analysis in ARGOS to streamline the process and accelerate work for the individual who is researching a company that has submitted its information for registration to E-Verify.
Purpose and benefits: ARGOS sentiment analysis produces a risk score and keyword extraction identifies the keyword category of interest to the VAC MPAs (management and program analyst) for the aggregated open-source information to help quickly identify any pertinent information to aid the MPAs in their open-source investigation of company applications.  This saves potentially thousands of MPA man hours in open-source investigation and creates a single source-of-truth for each MPAs investigation of a company application.  This, in turn, allows for quicker application processing and, if risk of company fraud exists, much faster referral processing time quickening the next-step referral to FDNS for further investigations.
Topic area: Admin Functions
Development stage: Deployed
Risk category: High-impact
First used: 2023-08-12
US federal AI use case: Public Information Compilation for Travel Threat Analysis (Dataminr)
Agency: Department of Homeland Security - CBP
Problem addressed: Provides situational awareness of open-source social media and news reporting to enhance CBP Screening, Vetting and security of the homeland.
Purpose and benefits: This tool significantly reduces the amount of time it takes for users to collect and compile commercially available open-source information when attempting to identify  possible threats related to national security, border violence, CBP facilities, CBP employee safety and other topics with a CBP-nexus involving air, sea, and land travel to and/or from the U.S.
Topic area: Admin Functions
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI outputs do not produce an action or serve as a principal basis for a decision that has the potential to significantly impact the safety of human-life or well-being, climate or environment, critical infrastructure, or strategic assets or resources. The AI output only provides the officer with complied public information.  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.
First used: 2024-11-01
US federal AI use case: Babel
Agency: Department of Homeland Security - CBP
Problem addressed: Babel utilizes AI modules for text detection and translation as well as object and image recognition to provide analysts with possible matches to manually review in a single interface, versus doing multiple manual queries. The output is not singly used for action or decision making.
Purpose and benefits: CBP uses this tool to conduct targeted queries to aid CBP in open source research to monitor potential threats or dangers or identify travelers who may be subject to further inspection for violation of laws CBP is authorized to enforce or administer.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2023-08-29
US federal AI use case: Airship Outpost for Conveyance Identification
Agency: Department of Homeland Security - CBP
Problem addressed: CBP must efficiently and accurately identify and document cross-border conveyances (aircraft, vessels, automobiles).
Purpose and benefits: Outpost uses machine learning to identify the type of conveyance in front of the sensor camera and uses this information to determine where to capture the conveyance's identification (License plate, hull number, tail number, etc). Conveyance identifiers exist in different locations on different conveyance types. By identifying the type of conveyance, the system knows where to focus the capture mission relevant information.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI is not being used for tracking or analysis. It is simply identifying the alpha-numeric values of the conveyances in front of it.
First used: 2023-09-01
US federal AI use case: ELIS Card Photo Validation Service
Agency: Department of Homeland Security - USCIS
Problem addressed: The card photo validation solution was developed to validate each submitted photo against a set of business-defined requirements in near-real-time in order to eliminate rejections/RFEs for the user uploaded photos.
Purpose and benefits: Ensuring beneficiary uploaded photos meet USCIS requirements. This helps ensure photos are correct before making ID cards, saving adjudicator time and avoiding delays.
Topic area: Service Delivery
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case supports beneficiaries submitting an e-filed I-765 via myUSCIS to apply for employment authorization. These applications include a digital ID photo of the applicant, which will be printed on their Employment Authorization Document (EAD) card if the application is accepted. The use case determines whether a user-uploaded ID photo is suitable for use on an EAD card, and notifies the submitter if it detects a potential quality issue with the photo. (SEE DHS CAIO SUPER MEMO FY24) 

---

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 outputs are a real-time advisory message to the submitter of the photo that it is of insufficient quality for card production at which point the submitter may choose to resubmit or not. While adjudication of EADs is not dependent on this use case, this tool enhances efficiency and customer service by minimizing the number of cards failing in production and delays related to subsequent requests for information.  (SEE USCIS REQUEST FOR DHS CAIO DETERMINATION FY24)
First used: 2022-03-15
US federal AI use case: AI Enabled Autonomous Underwater Vehicle
Agency: Department of Homeland Security - CBP
Problem addressed: Customs and Border Protection (CBP) desires to identify potential Items of Interest (IoI) on vessels more quickly, efficiently, and safely.  Provides increased shared situational awareness in real time for CBP and strategic partners, and improves mission planning and agent and officer safety while reducing reactionary gaps.
Purpose and benefits: Customs and Border Protection (CBP) intends to identify potential for Items of Interest (IoI) on vessels through the use of autonomous systems, which will allow CBP to more efficiently and safely increase shared situational awareness, improve mission planning and agent/officer safety, and reduce reactionary gaps.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: Computer Vision application onboard the ROV detects items of interest (IoI).  The ROV also uses AI for collision avoidance. Once an IOI is detected, a bounding box is placed around the suspected IoI in the image and the user is notified of an alert of a potential IOI.  The user then reviews the image and decides if a dive team is required for further inspection. The AI output does not serve as a principal basis for any decisions or actions.
US federal AI use case: Mobile Language Translation Services
Agency: Department of Homeland Security - ICE
Development stage: Retired
Risk category: High-impact
US federal AI use case: Semantic Search and Summarization for Investigative Data
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of searching and extracting relevant information from large volumes of unstructured investigative data.
Purpose and benefits: The benefits of using this AI include increased efficiency, reduced risk of missing valuable information, and enhanced investigative capabilities.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case provides a natural language contextual search and summarization capability against existing Reports of Investigation and other investigative data, producing more relevant search responses and investigative insights in the form of data, information leads or connections that HSI personnel can use to inform investigations. The AI output (search queries and responses) provide investigators with easy-to-read search responses that are accompanied by links to source material for further analysis. Any data used to produce these investigative insights are first obtained through legal means and processes for the purposes of law enforcement investigations and do not significantly impact the categories listed in the definitions of “high-impacting AI.” Personnel may use these insights for law enforcement purposes in ongoing investigations with existing targets to assist in activities such as producing risk assessments about individuals or identifying criminal suspects; however, all insights are reviewed and validated by both personnel and supervisors before being included in any official case management system, and do not serve as a principal basis for enforcement decisions. Any enforcement decisions related to these insights are outcomes of the full Federal Investigation Process involving verifying any insights as evidence.
US federal AI use case: Title III Semantic Search and Summarization for Translated Content
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of efficiently searching through and analyzing large volumes of translated evidentiary data, which can be time-consuming and labor-intensive.
Purpose and benefits: The Title III Semantic Search and Summarization functionality will augment translation and transcription services by extracting relevant data using machine learning and natural language processing for correlation and semantic search. Results can then be summarized using a large language model, giving users a tool to target relevant data only. This capability accelerates investigative analysis by rapidly identifying persons of interest, surfacing trends, and detecting networks or fraud, saving hundreds of man hours in manual analysis. HSI will use this tool to generate leads, and further action will be required by investigators and analysts as part of the full investigative process before any action is taken against an individual.
Topic area: Law Enforcement
Development stage: Pre-deployment
US federal AI use case: Policy Analyst Assistant
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of the significant manual effort it takes SEVP staff to research and respond to regulatory and policy questions related to foreign students in the F and M classes of admission, as well as schools that are either certified or seeking certification to enroll those students.
Purpose and benefits: SEVP is developing an AI-enabled solution to help policy analysts and other SEVP staff quickly find and summarize information about regulations and guidance for foreign students and SEVP-certified schools. This enhanced capability reduces the time required for manual research, enabling SEVP staff to focus on more complex policy and guidance issues. It also ensures consistent and accurate responses across SEVP functions, improving overall efficiency and effectiveness in supporting foreign students and schools.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: OCFO Response Augmentation Suite
Agency: Department of Homeland Security - FEMA
Problem addressed: FEMA Office of the Chief Financial Officer Generative Pre-trained Transformer (OCFO GPT), Travel Policy GPT and Fiscal Policy GPT are internal Generative AI (GenAI) tools designed to support the FEMA workforce by generating initial responses to various queries. These tools leverage relevant public and internal documents to draft preliminary responses, which are then refined prior to formal submission. FEMA OCFO GPT generates initial responses to questions for the record, leveraging public and internal documents, and provides a preliminary response to the Program Office to use in their formal response to the request. It reduces the data gathering stage, saving analysts 80% of the initial effort. Travel Policy GPT generates initial responses to questions regarding FEMA/DHS Travel Policy, including the JTR, and provides a preliminary response to the travel specialist to use in their formal response to the queries. It improves response times, saving users 80-90% of the time compared to regular engagement with the Travel Service Center. Fiscal Policy GPT provides preliminary responses to questions regarding FEMA/DHS Fiscal Policy and will generate a draft response with references to assist FEMA internal workforce in compliance with established policy. It saves users 80-90% of the time compared to regular engagement with DHS /FEMA OCFO policy and speeds up resolution times.
Purpose and benefits: FEMA OCFO GPT-B is an internal facing GenAI tool to augment the FEMA workforce in generating initial responses to questions for the record and providing a preliminary response to the Program Office to use in their formal response to the request. FEMA OCFO GPT provides draft responses reducing the data gathering stage and providing additional time for analysis, response, and approval. This has reduced the analyst initial level of effort versus individual research by 80% on initial surveys. Travel Policy GPT is an internal facing GenAI tool to augment the FEMA workforce in generating initial responses to questions regarding FEMA/DHS Travel Policy, and providing a preliminary response to the travel specialist to use in their formal response to the queries. The tools provide improved responses saving the end user time versus regular engagement with the Travel Service Center. The tool also allows travelers to ask specific questions that require Travel Service Center engagement, limiting the needed triage and speeding resolution times. Fiscal Policy GPT is an internal facing GenAI tool to augment the FEMA workforce in providing preliminary responses to questions regarding FEMA/DHS Fiscal Policy. The tool provides improved responses saving the end user time versus regular engagement with the DHS/FEMA OCFO Policy. The tool also allows internal users to ask specific questions that require Fiscal Policy engagement, limiting the needed triage and speeding resolution times.
Topic area: Admin Functions
Development stage: Deployed
First used: 2024-02-01
US federal AI use case: USCIS Document Translation Service
Agency: Department of Homeland Security - USCIS
Problem addressed: Reduce the man hours needed for translating evidence documents by leveraging AI document translation technology
Purpose and benefits: Integrate AI and GenAI models trained on relevant subject matter (e.g., immigration law, visa/immigration applications, family/adoption certificates, and other sourced processing materials) to provide fast, accurate translation of written or other digital documents in various languages. Real-time Interpretation: Utilizing AI and GenAI-powered speech-to-speech, speech-to-text translation tools for efficient communication, consultations, and other interactions within DHS. Across all DHS components the need to support language translation and transcription is crucial for operations and adjudications.
Topic area: Service Delivery
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: CISAChat
Agency: Department of Homeland Security - CISA
Problem addressed: Currently, retrieving and synthesizing content from hundreds of government documents is a slow, manual process. CISA users need the capability to answer questions, generate summaries, and produce textual responses efficiently using a broad range of information sources—from CISA publications to DHS mandates. AI can streamline and accelerate this workflow by automating information extraction and response generation.
Purpose and benefits: Currently, multiple CISA program offices are using contractor staff to review pre-production content and other internal materials to develop summaries, key themes, and improve clarity. Leveraging a Generative AI solution improves internal agency Customer Experience (CX) and saves staff time.
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-05-06
US federal AI use case: Draft Tailored Summaries of Media Materials for Different Publication Channels
Agency: Department of Homeland Security - CISA
Problem addressed: This AI capability accelerates the process of drafting summarized content for CISA’s published products.
Purpose and benefits: The Large Language Models (LLMs) will summarize information from historical publications and pre-production documents intended for external publication.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Relocatable Multi-Sensor System
Agency: Department of Homeland Security - CBP
Problem addressed: Border security, detection of Small Unmanned Aircraft Systems (SUAS), and multi-sensor fusion.
Purpose and benefits: The system uses advanced sensor technology to differentiate valid items of interest (IOI), such as unmanned aircraft systems and humans, from other detections such as animals or other environmental objects. By integrating radar and other sensor data, the system filters out false alarms, ensuring more accurate identification of potential IOI. This capability enhances CBP's ability to focus on legitimate risks while minimizing the time spent on non-threatening activities, improving operational efficiency at border and security checkpoints.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: Sensors and CUAS capabilities to support significant events. The AI combines sensor data from RF detections / radar/ and infrared / electro optical cameras.  It combines all feeds and provides detections on the user interface back to users. Fully standalone and airgapped system. Looking to identify non-RF drones. System does not have any mitigation capability, system cannot autonomously mitigate an aircraft, it only detects and provides a digital track on the GUI/map for further user investigation. This use of AI does not serve as a principal basis for decision or action.
US federal AI use case: JES and Appropriations Insight
Agency: Department of Homeland Security - MGMT
Problem addressed: Convert plaint text statements in Congressional reports to machine-readable tasks that can be managed in Outlook, Jira, or other project tracking software.
Purpose and benefits: Purpose: Converts scanned financial tables from PDFs into structured, machine-readable data while maintaining multi-year spending relationships.
Benefits: Eliminates manual data entry, reduces errors, and significantly speeds up the process of consolidating historical financial data from legacy documents.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: CFO Navigator
Agency: Department of Homeland Security - MGMT
Problem addressed: Quickly and accurately accessing and understanding DHS Chief Financial Officer (CFO) data.
Purpose and benefits: Provides authorized staff with an intuitive, conversational interface to query and analyze DHS CFO financial data and reports.
Benefits: Democratizes access to financial information, reduces time spent searching through documents, and enables quick self-service analytics without specialized database knowledge.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AI to generate testable synthetic data
Agency: Department of Homeland Security - CBP
Problem addressed: Improve integration with trade partners and reduce implementation time of new capabilties.
Purpose and benefits: The purpose of using AI to generate test data for trade partners is to create more realistic data for trade partners to use for testing their systems before releasing new ACE capabilities.  Currently there are many data issues where test data does not accurately reflect real production data which results in unrealistic failures when testing which result in wasted time and resources tracking down false positive errors during testing.  By providing trade partners with more realistic test data the expectation is that testing times will be shorter and enhancements and capabilities can be delivered quicker.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Anomaly Detection COV Structure
Agency: Department of Homeland Security - CBP
Problem addressed: The Anomaly Detection Algorithm (ADA) models are intended to solve several key problems for U.S. Customs and Border Protection (CBP) related to the screening of passenger and cargo vehicles. Improving the detection of anomalies and contraband, enhancing efficiency in image review, enhance human capability to consistently detect items of interest or concern, addressing high traffic volumes and resource constraints, and supporting the analysis of complex inspections.
Purpose and benefits: CBP is seeking Anomaly Detection Algorithm (ADA) models capable of operating on CBP systems to enable rapid screening of commercially owned vehicles (CoVs). The objective is to develop a suite of algorithms that enhance CBP's Non-Intrusive Inspection (NII) image analysis, improving the detection of anomalies and contraband. These algorithms are intended to assist CBP officers in efficiently reviewing images, with a particular focus on identifying concealed contraband and anomalies in passenger vehicles and cargo conveyances. The implementation of ADA models will enhance human capability to consistently detect items of interest or concern, including concealed objects, in vehicles entering the United States. Additionally, these algorithms will enhance throughput efficiency at ports of entry, enabling the expedited processing of compliant vehicles while maintaining robust security standards.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: Computer Vision is intended to detect anomalies in non-intrusive inspection images. The images are then shared with officers who review the detections within the images (represented as a polygon). If the officer feels physical review is required, the vehicle is moved to secondary inspection for more thorough review by an officer. The output of the AI does not serve as a principal basis for a decision or action.
US federal AI use case: Anomaly Detection Homogenous Cargo
Agency: Department of Homeland Security - CBP
Development stage: Retired
US federal AI use case: Anomaly Detection POV Structure
Agency: Department of Homeland Security - CBP
Problem addressed: The Anomaly Detection Algorithm (ADA) models are intended to solve several key problems for U.S. Customs and Border Protection (CBP) related to the screening of privately owned vehicles (PoVs). Improving the detection of anomalies and contraband, enhancing efficiency in image review, enhance human capability to consistently detect items of interest or concern, addressing high traffic volumes and resource constraints, and supporting the analysis of complex inspections.
Purpose and benefits: CBP is seeking Anomaly Detection Algorithm (ADA) models capable of operating on CBP systems to enable rapid screening of passenger and cargo vehicles. The objective is to develop a suite of algorithms that enhance CBP's Non-Intrusive Inspection (NII) image analysis, improving the detection of anomalies and contraband. These algorithms are intended to assist CBP officers in efficiently reviewing images, with a particular focus on identifying concealed contraband and anomalies in passenger vehicles and cargo conveyances. The implementation of ADA models will enhance human capability to consistently detect items of interest or concern, including concealed objects, in vehicles entering the United States. Additionally, these algorithms will enhance throughput efficiency at ports of entry, enabling the expedited processing of compliant vehicles while maintaining robust security standards.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: Computer Vision is intended to detect anomalies in non-intrusive inspection images. The images are then shared with officers who review the detections within the images (represented as a polygon). If the officer feels physical review is required, the vehicle is moved to secondary inspection for more thorough review by an officer. The output of the AI does not serve as a principal basis for a decision or action.
US federal AI use case: CBP Careers Bot - Leo
Agency: Department of Homeland Security - CBP
Problem addressed: The Natural Language Processing (NLP) chatbot is intended to help visitors to the CBP careers site navigate complex and extensive career resources quickly and easily. By providing a guided, interactive experience, the chatbot simplifies access to the most relevant career information, on a user-by-user basis. Additionally, the chatbot can direct users to CBP recruiters and recruitment events, strengthening the agency's recruitment network and fostering more direct engagement with prospective candidates.
Purpose and benefits: Visitors to the U.S. Customs and Border Protection (CBP) careers website can engage with a Natural Language Processing (NLP) based chat bot to help access CBP career related information and drive users to take the next action such as contacting a recruiter, attending a career event, or applying for a CBP career to help access CBP career related information and drive users to take the next action. These data-driven responses will allow for more natural, conversational interactions to increase usability and accuracy in provided information.
Topic area: Service Delivery
Development stage: Deployed
First used: 2025-09-30
US federal AI use case: Computer Vision for Aerial Detection of Land and Open Water Items of Interest
Agency: Department of Homeland Security - CBP
Development stage: Retired
US federal AI use case: AI for Software Delivery
Agency: Department of Homeland Security - CBP
Development stage: Retired
US federal AI use case: Optical Counter - UAS Detection
Agency: Department of Homeland Security - CBP
Development stage: Retired
US federal AI use case: CBP Employee Experience
Agency: Department of Homeland Security - CBP
Problem addressed: Parsing through voluminous amounts of qualitative and quantitative data from recruit/applicant/employee survey data. The technology provides actionable intelligence for senior leaders to better improve the recruit/applicant/employee experience, thereby increasing both yield rates and resiliency.
Purpose and benefits: CBP Employee Experience is intended to ingest, interpret, and operationalize employee experience data originating from survey results and operational data to deliver real time insights related to the experience of USBP recruits, applicants, and employees. These metrics inform HRM leadership of opportunities for process improvement in order to meet congressionally mandated hiring targets and retain a qualified workforce.
Topic area: Admin Functions
Development stage: Deployed
First used: 2023-10-01
US federal AI use case: Thermal Power Generation with Geoseismic IoI Detection and Classification
Agency: Department of Homeland Security - CBP
Problem addressed: Overcomes the following problems: 1) the cost of replacement batteries, 2) the time it takes agents to constantly replace batteries, and 3) the problem of revealing the UGS location when replacing the batteries.
Purpose and benefits: Utilize seismic sensor data to determine Item of Interest in deployed locations. Increases situational awareness in austere environments and reduces need for battery replacement due to self-charging.
Topic area: Law Enforcement
Development stage: Pre-deployment
US federal AI use case: Underwater ROV
Agency: Department of Homeland Security - CBP
Development stage: Retired
US federal AI use case: Wellness and Physical Fitness Application
Agency: Department of Homeland Security - CBP
Development stage: Retired
US federal AI use case: Passive Body Scanner
Agency: Department of Homeland Security - CBP
Problem addressed: Identify anomalies in body heat, assisting CBP officers to detect concealed weapons and contraband, allowing for efficient processing of travelers
Purpose and benefits: PBS is intended to enhance situational awareness in pedestrian traveler processing to aid  CBP officers in observing potentially dangerous objects or contraband in a timely manner and pursuant to CBP’s border search authority.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2023-09-29
US federal AI use case: Unmanned Aircraft Collision Avoidance
Agency: Department of Homeland Security - CBP
Problem addressed: The use case solves the problem of navigating complex environments autonomously while ensuring obstacle avoidance in real time. By relying on AI-based 3D scanning functions instead of GPS, the system enhances safety and precision in drone operations, reducing the risk of collisions and enabling efficient, reliable use in diverse mission scenarios. It addresses the challenge of maintaining situational awareness and operational accuracy during unmanned aircraft missions, providing pilots with visual alerts to prevent potential collisions.
Purpose and benefits: The platform operates on video feed only which in turn activates the obstacle avoidance on the aircraft where the AI capabilities are housed.  The system supports the streamlined intake process while maintaining the accuracy and reliability of identity verification.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case avoids collisions for small unmanned aircraft systems. It operates a video feed that activates the obstacle avoidance features. The obstacle avoidance capability assists the pilot on the ground to avoid colliding the unmanned aircraft with objects such as man-made structures, vehicles, trees, wires, or other objects in the projected flight path. The pilot receives a visual alert on the hand controller, indicating a possible collision and in some cases the aircraft will slow down, change direction to avoid the obstacle, or stop.
First used: 2022-10-01
US federal AI use case: Verification Match Model
Agency: Department of Homeland Security - USCIS
Problem addressed: By consolidating these into a single, unified Verification Match Model within a separate microservice, the use case aims to improve the accuracy of responses and reduce the need for manual review. ML plays a key role in the continuous improvement of these models, ultimately reducing the need for manual case reviews.
Purpose and benefits: Leveraging AI in the USCIS verification matching process of known records across systems is beneficial because it streamlines existing USCIS review by 1) improving associated system accuracy, 2) reducing human-error by automating person-and-record match scoring, and 3) matching at a higher volume than traditional tools or manual processes can capably achieve.
Topic area: Admin Functions
Development stage: Deployed
Risk category: High-impact
First used: 2024-05-22
US federal AI use case: Intelligent Document Processing (IDP) for I-539 Form Digitization
Agency: Department of Homeland Security - USCIS
Problem addressed: Before the use case, all pages of a I-539 application were scanned and stored as a single document in the content management system, delaying adjudication and not meeting National Archives and Records Administration (NARA) standards. The tool uses a learning model to identify, classify, and separate individual documents into their component parts for storage.
Purpose and benefits: IDP for the I-539 makes use of an AI-enhanced tool to identify, categorize, and create separate images for each document type submitted as part of the 539 benefit application. Prior to implementation of this use case, all pages of a 539 application were scanned and stored as a single document in the content management system.  The benefit is reduced case processing time for adjudicators by identifying and classifying supporting documents for ease of use. An additional benefit is to bring digital images into compliance with NARA standards.
Topic area: Admin Functions
Development stage: Deployed
First used: 2024-11-04
US federal AI use case: Sentiment Analysis -FOD Field Offices Complaints and Reviews
Agency: Department of Homeland Security - USCIS
Problem addressed: The AI solution helps USCIS Field Offices efficiently analyze and categorize large volumes of complaints by using machine learning to identify sentiment trends (positive, negative, or neutral) in public feedback.
Purpose and benefits: This system indicates the positive or negative feelings people express in their feedback to the U.S. Citizenship and Immigration Services (USCIS).

Survey results categorized to specify the sentiments as positive negative or neutral tone in an excel dashboard.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: CBP Translate
Agency: Department of Homeland Security - CBP
Problem addressed: Assist officers and agents with immediate interpretation needs when human translators are not available.
Purpose and benefits: CBP Translate enhances efficiency by expediting questioning when immediate interpretation is needed. It ensures clear communication, minimizes misunderstandings, and offers immediate accessibility via mobile and web platforms. This improves operational flexibility and creates a smoother experience for travelers.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2019-08-07
US federal AI use case: Passenger Security Assessment Model
Agency: Department of Homeland Security - CBP
Problem addressed: This model aids CBP to efficiently identify security risks, especially related to narcotics interdiction, by providing real-time risk assessments that overcome limitations of traditional, time-intensive methods. The model solves the problem by providing CBP personnel with real-time risk assessments and actionable recommendations integrated into existing systems. By analyzing data not typically accessible during initial processing, the model enhances the ability to detect smuggling indicators and prioritize high-risk individuals or vehicles for further inspection. This improves the efficiency and effectiveness of border security operations, enabling CBP to better safeguard the nation while maintaining the flow of legitimate travel and trade.
Purpose and benefits: This model is designed to support CBP personnel in quickly recognizing crossings that may warrant additional scrutiny, thereby enhancing border security and safety.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2013-04-01
US federal AI use case: Cargo Security Assessment Model
Agency: Department of Homeland Security - CBP
Problem addressed: This use case addresses the challenge of efficiently identifying and mitigating risks associated with cargo shipments entering the United States. With the high volume of shipments processed daily at ports of entry, it is essential to detect potentially high-risk shipments, such as those that may pose security threats, without causing delays to legitimate trade and commerce. This use case uses advanced data analytics and machine learning to enhance the ability to evaluate and prioritize shipments for further review, ensuring that flagged cargo is inspected appropriately while maintaining efficient cargo processing operations.
Purpose and benefits: AI/ML Models identify high risk shipments to aid CBP officers in detecting narcotics smuggling threats, identifying candidate shipments for review and referral for inspection at CBP Ports of Entry (POEs).
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2011-12-01
US federal AI use case: Illicit Trade
Agency: Department of Homeland Security - CBP
Problem addressed: The use case is designed to improve the identification and prioritization of high-risk inbound cargo shipments that may violate trade regulations. Using advanced AI and machine learning models, the system enhances risk assessment processes, helping CBP personnel more effectively detect suspicious shipments and potential compliance issues. By analyzing historical data, risk attributes, and employing predictive modeling, the AI supports CBP in streamlining enforcement actions and improving the accuracy of targeting shipments for additional review and screening. This approach helps optimize resource allocation and strengthens CBP's ability to enforce trade regulations efficiently.
Purpose and benefits: The model identifies high-risk shipments to support CBP personnel in managing their workload associated with detecting threats and selecting candidate shipments for review and additional screening.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2023-07-25
US federal AI use case: Conversation Training and Feedback Simulator
Agency: Department of Homeland Security - TSA
Development stage: Retired
US federal AI use case: OTA Automated Passenger Screening Gate System
Agency: Department of Homeland Security - TSA
Problem addressed: Increase efficiency and security of Automated Passenger Screening
Purpose and benefits: The purpose is to manage passenger flow while reducing human interaction by using AI to assess body positioning and automatically initiate the screening process when the passenger is in the optimum position.  Provide positive control of passengers transitioning from the Non-Sterile to the Sterile side of the checkpoint via the QPS201 On-Person Screening (OPS) system, enhancing the security of the checkpoint environment and improving efficiency.​
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Electronic Evidence/Video Recording Transcription and Summarization Tool
Agency: Department of Homeland Security - TSA
Problem addressed: This will cut investigative processing time significantly, while also increasing accuracy.
Purpose and benefits: The Transportation Security Administration (TSA) uses body-worn cameras that incorporate artificial intelligence (AI) technologies as a part of the underlying software which transcribes and translates video footage. The technology provides rapid access to Law Enforcement Officer (LEO) and Investigative data, through transcription.  This will cut investigative processing time significantly, while also increasing accuracy.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact