GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: idiCORE Subscriptions
Agency: Department of Homeland Security - TSA
Problem addressed: This allows agents and analysts to quickly determine interrelationships and connections, facilitating faster and more accurate investigations.
Purpose and benefits: Significantly enhances efficiency by saving analysts time in forming relative and associate interrelationships. It eliminates much of the guesswork, enabling analysts to focus on critical decisions and actions.
Topic area: Transportation
Development stage: Deployed
First used: 2025-07-17
US federal AI use case: Document Translation Service (DTS)
Agency: Department of Homeland Security - TSA
Problem addressed: Leveraging accurate cross-language translation with the latest Azure AI cloud service
Purpose and benefits: The DTS application allows users to upload a document in a source language, call a Text Translator API specific to the application, and then download a translated copy of their artifacts.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Global Entry Mobile App Traveler AI Question Answering Service
Agency: Department of Homeland Security - CBP
Problem addressed: Travelers submitting a question to the Global Entry support team.
Purpose and benefits: Faster workflow, faster customer response time, reduce the number of people it takes to address customer concerns, less expensive.
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-06-16
US federal AI use case: ChatCBP
Agency: Department of Homeland Security - CBP
Problem addressed: Workforce enablement and efficiency.
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. _x000D_
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Enhancing Decision-Making: Providing quick and accurate access to relevant information will improve the quality and consistency of decisions across the agency. _x000D_
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Increasing Mission Effectiveness: By applying LLM capabilities to critical use cases like hot list review and violation coding, we can enhance accuracy, reduce errors, and improve overall mission success rates.
Topic area: IT
Development stage: Deployed
First used: 2025-07-30
US federal AI use case: Open Source and Social Media Analysis
Agency: Department of Homeland Security - CBP
Problem addressed: The AI is intended to solve the problem of efficiently identifying potential threats and admissibility concerns by quickly analyzing vast amounts of open-source and social media data for security risks to enhance U.S. national security. This tool then presents information to a CBP Officer/analyst for manual review, verification and validation for violations of Title 8 and Title 19 or other laws that CBP is sworn to enforce. The output is not used as the sole basis 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: 2025-01-01
US federal AI use case: Planning Assistant for Resilient Communities (PARC)
Agency: Department of Homeland Security - FEMA
Development stage: Retired
US federal AI use case: Open Metadata
Agency: Department of Homeland Security - CBP
Problem addressed: Metadata catalog identification.
Purpose and benefits: The AI uses a large language model to generate proposed descriptions for field names in our data catalog. The tool itself does not come with Generative AI, but we have built the capability to leverage generative AI for description generation and test case quality generation. Roughly 80% of our data assets in databases do not have descriptions. The use of AI to generate these descriptions would allow us to provide descriptions for our vast data assets. The quality test cases ensure that the data in our systems are accurate, correct, and consistent. These AI tools can be turned on or off.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: AI Security and Monitoring
Agency: Department of Homeland Security - USCIS
Problem addressed: Organizations adopting AI face a number of risks from data leaks, shadow AI, and unsecured outputs.
Purpose and benefits: The gateway will secure enterprise AI use by discovering shadow AI, enforcing guardrails, and preventing data spills. Its benefits include improved compliance, reduced risk, real time protection, and enhanced visibility. Organizations are empowered to securely and efficiently adopt AI.
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: 2025-03-31
US federal AI use case: OCR for Scanning and Cataloging Documentation [Captiva Open Text]
Agency: Department of Homeland Security - USCIS
Development stage: Retired
US federal AI use case: Entity Resolution for Global Trade Data
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of investigators having to manually piece together fragmented global trade and supply chain data from many sources, which makes it difficult to see relationships among entities and identify potential leads in transnational criminal investigations.
Purpose and benefits: This platform improves Homeland Security Investigations' ability to validate existing information, understand complex supply chain networks, and to generate leads in transnational criminal investigations.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Although the use case falls into a presumed high-impact category related to law enforcement investigations, it does not meet the high-impact definition as its outputs do not directly serve as a principal basis for enforcement actions or regulatory decisions. Instead, the AI-generated knowledge graph provides a foundation for further human-led investigation, requiring source validation through separate processes. This delineation ensures that the AI output remains a supportive rather than a determinative factor, disqualifying it from the high-impact AI system category.
First used: 2024-10-10
US federal AI use case: Automated Incident Creation (IT)
Agency: Department of Homeland Security - CBP
Problem addressed: The AI is intended to reduce the time and effort required of a business analyst to manually review contact messages submitted to the Business Connection (BC) and Technical Reference Model (TRM) teams. By evaluating these messages, the AI will determine whether a ServiceNow helpdesk ticket is needed to resolve the submitted question or concern. This automation will streamline the process and ensure timely resolution of user issues.
Purpose and benefits: AI will be used to drive automation via integration with ServiceNow, automatically triaging requests to determine if a ServiceNow helpdesk ticket should be created. The determination and justification will populate a field in the table that stores user contact messages, enabling validation by a business analyst before finalizing ticket creation.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Mobile Device Forensics for Investigations
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of analysts having to manually organize, decode, and review large volumes of complex mobile device data, which makes it difficult to quickly identify information relevant to an investigation.
Purpose and benefits: AI-generated category tags, app artifacts, and origin classifications allow analysts to more effectively prioritize human review of mobile device data that may be responsive to the investigation.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case falls into a presumptive high-impact category under M-25-21 Section 6 (j) due to its role in supporting law enforcement activities, specifically application of digital forensic techniques. However, it does not meet the definition of high-impact AI as outlined in OMB Memorandum M-25-21 because its outputs do not serve as a "principal basis" for decisions or actions with legal, material, binding, or significant effects on an individual or entity's civil rights, civil liberties, or privacy; or human health and safety. The AI capabilities, including classification, decoding, and origin analysis, are designed to assist analysts in prioritizing human review rather than independently determining outcomes. For example, AI-generated tags and app artifacts are suggestions that require manual validation and further investigation before enforcement actions are taken. All outputs are reviewed as part of a broader investigative process before any actions are taken.
US federal AI use case: Media Classifier for Computer and Digital Storage Evidence
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of analysts having to manually sort and review large volumes of media files from digital storage devices, which makes it difficult and time‑consuming to organize evidence and identify potentially relevant material.
Purpose and benefits: By automating the initial classification of large volumes of media evidence, the platform enables Homeland Security Investigations personnel to more efficiently identify and review potentially relevant information, improving the overall effectiveness of digital investigations.
Topic area: Law Enforcement
Development stage: Deployed
First used: 2023-08-29
US federal AI use case: AI-Assisted Resume Screening Tool
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of human bias during resume reviews and the time-intensive process of reviewing candidate resumes.
Purpose and benefits: This solution applies the same review criteria to every candidate’s resume. This reduces human cognitive bias and variability in how HR specialists evaluate candidate resumes. Additionally, this solution speeds up the time-to-hire by reducing the amount of time spent conducting manual candidate resume reviews.
Topic area: HR
Development stage: Deployed
Risk category: High-impact
First used: 2026-01-01
US federal AI use case: Duplicate Contract Detection in Contract Tracking Application
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of inefficiencies and redundancies in contract management.
Purpose and benefits: The use of AI in contract management reduces the time it takes to manually inspect contracts and improves COT's ability to identify duplicative contracts. This implementation will reduce contract costs and aligns with federal procurement consolidation and cost-efficiency initiatives.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Cryptocurrency Analysis
Agency: Department of Homeland Security - CBP
Problem addressed: Identification of transactions that may have been made with a designated entity, company, or location that may be using crypto to circumvent financial reporting. This tool will help CBP identify business and travelers who may be using cryptocurrency to conceal illicit transactions not reported within the U.S. financial system.
Purpose and benefits: Quicker identification of risks related to cryptocurrency accounts to aid in addressing potential admissibility concerns (in lieu of completely manual research of the same accounts).
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-01-01
US federal AI use case: CBP Common AI Service(CCAIS) Image Analysis and Data Correlation
Agency: Department of Homeland Security - CBP
Problem addressed: Reduces the amount of time agents needs to spend analyzing imagery but automatically flagging images for review.
Purpose and benefits: The AI’s intended purpose is to solve the challenge of efficiently monitoring typically unoccupied or restricted environments for unauthorized human or vehicle presence. It automates the initial detection of such activities, which can be resource-intensive and prone to delays when done manually. The expected benefits include enhanced security of sensitive areas, increased operational efficiency through automation, and the ability to scale oversight and respond more effectively to potential incidents, and better protection of public assets, sensitive environments, and critical infrastructure, alongside more efficient use of public resources in security operations.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case is not yet live in production, and the current ATT only covers for within the CCAIS platform.  The use case needs further review, prior to the ATT for ICAD integration.  The model can identify people, animals, and vehicles as well as extract license plate information, and we will need to determine how exactly the extracted information will be used for operations if it is determined to be feasible.  The AI inventory team will need to review once the AI Use Case is fully defined.
US federal AI use case: TRM Classification Tool
Agency: Department of Homeland Security - CBP
Problem addressed: The system uses Gemini 1.5 to perform multi-label classifications, including product categorization, subclassification, and reasoning for the classification outputs, replacing an existing platform to reduce costs and improve efficiency.
Purpose and benefits: The system uses AI to categorize vendor product capabilities, reducing costs and improving efficiency by replacing a previous system. The classifications are integrated into the Technical Reference Model (TRM).
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: FOIA processing automated redaction (RedactAI)
Agency: Department of Homeland Security - CBP
Problem addressed: This use case leverages AI technology to streamline the identification and redaction of sensitive content in documents related to FOIA requests. Using Vertex AI and other GCP services, the system identifies and categorizes content that may require redaction based on specific controls. This automation is expected to significantly reduce processing times compared to manual redaction, enhancing efficiency in handling FOIA requests. The system provides recommendations for content to be redacted. Users are required to review and manually approved the suggested redactions based on the appropriate FOIA exemptions.
Purpose and benefits: AI is being used to find content that may need to be redacted in documents related to FOIA requests and applicable FOIA exemptions. The benefits are expected to be much faster processing time for the requests as opposed to manually performing the task.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Non-intrusive vessel and object detection tool (Tethered Aerostat Radar System (TARS))
Agency: Department of Homeland Security - CBP
Problem addressed: Detect, identify and classify vessels and objects in the maritime environment
Purpose and benefits: Utilizing AI to automate maritime object detection with real-time outputs to streamline data flows intended to increase efficiency of existing resources and minimize mission critical decision-making timeline.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI will analyze images from the sensors to determine what the item is in the image (e.g. Vessel).  The image location will also be correlated with any publicly available AIS (Automated Identified System) data for maritime traffic. AIS is used as a filtering mechanism in most cases to filter out legitimate traffic.  User is provided with information for detected objects lacking AIS signal. After the alert of a detection, a trained CBP agent or user, reviews the image to identify and classify the activity taking place. This use of AI does not serve as the principal basis for decisions or actions.
US federal AI use case: Speech Assist Virtual Interview Assistant
Agency: Department of Homeland Security - CBP
Problem addressed: Accurate audio processing automation and draft transcription generation to reduce the manual labor intensive associated with transcribing audio interviews and reports, while ensuring Officer-vetted accuracy and nuance.
Purpose and benefits: To enhance operational efficiency by accelerating the processing and analysis of audio data, resulting in both reduced labor burden and compressed workflow timelines. Speech Assist will offer diarized transcriptions of conversations alongside conversation summaries and named entities extracted from audio captured for various CBP mission use cases. The output will be used to automatically draft a document for review by CBP officers and agents. Speech Assist will reduce the time required for the interpretation, and comprehension of key information from audio data, reducing the time required for transcribing audio reports.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Traffic Jam
Agency: Department of Homeland Security - CBP
Problem addressed: Access to a robust data source of potential runaways, missing children, and sex trafficking victims alongside the associated analytics tools to support CBP in our mission to protect the most vulnerable populations.
Purpose and benefits: Traffic Jam leverages AI for facial recognition purposes and to draw together similar data points from a large pool of data in support of human trafficking and exploitation investigations.  Manually combing through available data takes an extensive amount of time and resources, with often less than desirable outcomes.  The AI solution in Traffic Jam is able to quickly identify the most relevant information and possible matching for immediate review, allowing officers and analysts to focus limited time in a high-tempo operational environment in the most effective manner possible.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-09-29
US federal AI use case: Business Automation and Improved Search Ability with AI
Agency: Department of Homeland Security - CBP
Problem addressed: The Business Connection (BC ) and Technical Reference Model (TRM) teams have identified areas in which business automation can accelerate use for both business analysts in making determinations and improving the ability of end-users to search for the information that they need more quickly.
Purpose and benefits: AI will be used to drive automation via integration with ServiceNow, automatically classifying products, suggest alternatives, validate the vendors headquarters and operations, summarize meeting notes, and improve searching capabilities.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Acoustic Signature AI for Gunshot Detection
Agency: Department of Homeland Security - CBP
Problem addressed: The AI will be used to make confidence determinations between non gunshot acoustic activity and actual gunshot activity to reduce false alerts to agents monitoring the User Interface.
Purpose and benefits: Gunshot and UAS detection notifications can be used by Agents for enhanced situational awareness in their area of operations. By utilizing this AI learning technology, they will have high confidence of alerts of a detection, with the specific type, as well as a pinpointed GPS location within 3-meters.
Topic area: Law Enforcement
Development stage: Pilot
Risk category: Presumed high-impact (determined not high)
Impact assessment: This system detects sounds that are associated with sounds of gunfire. Only events classified as High Confidence are then sent to users as a detection. Users then review the audio of the event to determine the accuracy of the detection and to review the location of the detection on an associated map. If the audio review is deemed relevant by agent and/or system users, agents may be sent to investigate further and the message is sent to field personnel of potential gunshot activity. It may be warning of activity south of the border where notifications made enhance safety of officers/agents in the region.  Any further actions taken will be based on what the agents encounter when they arrive on scene. The AI associated with this system is not a principle basis for a decision or action.
First used: 2025-09-08
US federal AI use case: Blockchain Analysis
Agency: Department of Homeland Security - ICE
Problem addressed: By utilizing this AI-powered blockchain analysis platform, investigators can uncover hidden connections across blockchain networks, detect illicit activities, and significantly reduce the time required for manual analysis, enhancing HSI’s ability to combat transnational crime effectively.
Purpose and benefits: The use of AI within TRM Labs improves HSI’s ability to uncover hidden connections across blockchain ecosystems, detect illicit behaviors, and reduce the time required for manual analysis.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Despite falling into a presumed category of high-impact AI, this use case does not meet the definition because the AI’s outputs serve primarily as inputs to an investigative process rather than making legally binding or material decisions itself. HSI investigators review, validate, and contextualize the AI-generated outputs before integrating them into any official case management system. Enforcement decisions and outcomes arise from a full investigative process that includes judicial review and other safeguards, thereby distancing the AI outputs from direct impact on civil liberties or privacy
First used: 2022-08-16
US federal AI use case: Mobile Fortify
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of confirming individuals’ identities in the field when officers and agents must work with limited information and access multiple disparate systems to identify individuals and retrieve existing data relevant to enforcement, investigations, and victim protection activities.
Purpose and benefits: The use of AI in this process increases the speed and efficiency of identifying individuals and organizing identity information, supporting immigration enforcement, authorized investigations, and victim protection efforts.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-05-20
US federal AI use case: Enhanced Lead Identification and Targeting
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of unstructured, hard‑to‑read address information in records like rap sheets and warrants, which makes it difficult and time‑consuming for Enforcement and Removal Operations officers to extract accurate addresses and build usable enforcement leads.
Purpose and benefits: The integration of AI enhances data extraction capabilities and decreases the time spent on manual data normalization tasks. This provides Enforcement and Removal Operations officers with higher-quality leads and enables them to make better-informed decisions.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: While ELITE provides actionable data to ERO officers, its outputs are limited to normalized address data and do not serve as a principal basis for decisions or actions with legal, material, binding, or significant effects on individuals. ERO officers review and validate the AI-driven outputs before determining actions, ensuring human oversight and additional verification steps. Furthermore, enforcement decisions are based on the full investigative process, which includes human analysis and validation of the source of AI outputs. As such, the AI system's role is limited to data extraction and normalization, rather than serving as a primary basis for enforcement actions.
First used: 2025-06-07
US federal AI use case: Semantic Search for Digital Forensics Data
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem that traditional keyword searches and manual review make it difficult to find relevant evidence across large, mixed-format digital datasets, especially when important material does not contain the exact search terms.
Purpose and benefits: This tool will help Homeland Security Investigations quickly find relevant evidence within large, complex digital datasets, reducing time spent on manual review. This supports faster, more effective investigations and better use of limited investigative resources, ultimately enhancing public safety.
Topic area: Law Enforcement
Development stage: Pre-deployment
US federal AI use case: Draft Report Generation and Formatting for Investigations
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of contract managers having to manually review large numbers of contract records to spot possible duplicates, which makes it easy to miss overlapping agreements and contributes to redundant spending.
Purpose and benefits: The use of AI in contract management is expected to reduce the time required for manual contract review and improve the ability to identify duplicative contracts. This implementation supports efforts to reduce contract costs and aligns with federal procurement consolidation and cost-efficiency initiatives.
Topic area: Law Enforcement
Development stage: Pre-deployment
US federal AI use case: Named Entity Resolution for Investigative Data
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of investigators having to manually identify and normalize entities such as names, locations, and other selectors in large volumes of investigative text data, which is slow and makes it difficult to run accurate, consistent searches.
Purpose and benefits: The benefits of using this AI include increased search accuracy, enhanced data analysis capabilities, and the ability to handle domain-specific entities more effectively.
Topic area: Law Enforcement
Development stage: Pre-deployment
US federal AI use case: CodeGen
Agency: Department of Homeland Security - CBP
Problem addressed: Workforce enablement of AI developers.
Purpose and benefits: The primary purpose is to provide code generation support for CBP developers, with the anticipated benefit of significantly increasing their efficiency and productivity. By automating repetitive coding tasks and suggesting optimal solutions, this initiative aims to free up developers to focus on more complex and strategic projects, ultimately accelerating the delivery of critical work.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: CounselAI
Agency: Department of Homeland Security - CBP
Problem addressed: CounselAI will minimize the manual replication of work product, strengthen CBP’s ability to provide consistent responses across the agency’s sizable litigation portfolio, and enable OCC to better defend against legal challenges.
Purpose and benefits: CounselAI will allow OCC to be more efficient and effective. The LLM uses existing work product to help identify similar legal challenges and generate successful draft language for use in litigation. The LLM will also save users time with its search and summarization features.
Topic area: Law Enforcement
Development stage: Pre-deployment
US federal AI use case: ICE Enterprise AI Assistant
Agency: Department of Homeland Security - ICE
Problem addressed: This use case aims to (1) address cybersecurity concerns related to ICE personnel using externally hosted commercial chatbots and (2) solve the issue of out-of-the-box LLMs lacking tailored capabilities for ICE, such as the integration of internal ICE artifacts.
Purpose and benefits: By providing an ICE-owned and agency-wide solution, the ICE Enterprise AI Assistant addresses cybersecurity and privacy concerns associated with external AI tools, while enhancing employee efficiency and supporting the ICE mission. The tool advances ICE’s AI adoption and is expected to improve information access and increase productivity across the organization.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Open-Source Intelligence for Lead Identification and Targeting
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of traditional manual open‑source searches missing relevant identifiers or connections in large volumes of online information.
Purpose and benefits: The platform’s AI capabilities 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 ICE enforcement operations.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case falls into a presumptive high-impact category due to its role in supporting law enforcement activities, specifically in identifying individuals who pose risks to community safety or violate U.S. immigration laws. However, it does not meet the definition of high-impact AI as outlined in OMB Memorandum M-25-21 because its outputs do not serve as a "principal basis" for decisions or actions with legal, material, binding, or significant effects on an individual or entity's civil rights, civil liberties, or privacy; or human health and safety. The AI modules, including risk extraction, image analysis, language detection, and AI chat, are designed to augment traditional investigative processes by providing structured annotations and insights for analysts to review. These outputs are explicitly described as supporting tools that require human validation and integration with other government data holdings before any enforcement action is taken. Furthermore, the AI system operates as a supplementary tool, consolidating and organizing information to enhance efficiency, but does not independently produce outcomes that directly affect civil rights, civil liberties, or privacy. The safeguards in place, including human validation and adherence to established legal standards, ensure that the AI outputs remain supportive rather than determinative, confirming that this use case does not meet the high-impact definition.
US federal AI use case: PDF Intake (PDFI) for myUSCIS
Agency: Department of Homeland Security - USCIS
Problem addressed: Scanned PDFs submitted through MyUSCIS must be validated against form-specific business rules related to both the overall document and the contents of specific fields. Constructed a service that can process a scanned input document and return all information pertinent to these validation rules in a consistent structure (JSON) to a user-facing ELIS microservice. The GenAI powered library utilizes Amazon Bedrock – Anthropic Claude 3.7 Sonnet V1 Foundation Model to extract data from PDF forms.  The service provides ability to submit forms online through MyUSCIS UI to Lockbox instead of via mail.
Purpose and benefits: Develop a service that can extract relevant fields from a scanned PDF submitted through MyUSCIS and build a JSON as an output to the ELIS microservice. The new service will utilize AWS Bedrock provided foundation model. It is an engineering solution that minimizes development time to add new forms or form revisions with high accuracy.
Topic area: Benefits Processing
Development stage: Deployed
First used: 2025-07-23
US federal AI use case: Private Artificial Intelligence (AI) Tech Hub (PAiTH)
Agency: Department of Homeland Security - USCIS
Problem addressed: USCIS staff currently lack access to AI tools that can accelerate routine knowledge work while maintaining the security, compliance, and privacy requirements specific to USCIS operations. Existing commercial AI solutions cannot access USCIS-specific documents and data, cannot be customized to immigration-specific workflows, and pose data security risks. Staff spend significant time on tasks that AI could assist with—such as legal research, document drafting, language translation, code generation, and regulatory compliance checks—but have no approved internal AI capability. Additionally, different organizational roles have vastly different AI assistance needs (e.g., attorneys need legal citations; developers need code; contracting officers need FAR guidance, budget officers need access to sensitive internal fiscal data), requiring a solution that adapts to the user's function rather than providing generic responses. PAiTH will also promote USCIS innovation by enabling testing of a variety of large language models and AI platforms, while informing approaches on prompt generation, cost containment and workforce literacy.
Purpose and benefits: The purpose of PAiTH is to provide USCIS employees with an internal, secure AI assistant that delivers role-specific support for knowledge work tasks. The system will offer six persona-based assistants aligned to core USCIS job functions, each trained and prompted to provide relevant, accurate responses for that role's responsibilities. 
Intended benefits of PAiTH include: Increased Efficiency: Staff can quickly obtain research, draft language, translations, and technical guidance without manual searching through documents or external research; Role-Specific Accuracy: Persona-based responses tailored to each user's organizational function (legal, contracting, development, etc.) provide more relevant and useful outputs than generic AI; Data Security and Compliance: Internal deployment protects PII and sensitive USCIS data while maintaining compliance with federal security and privacy requirements; Controlled Access to USCIS Knowledge: AI can leverage USCIS-specific documents, policies, and data sources that are inaccessible to commercial AI tools, keeping sensitive information within USCIS boundaries; Standardization: Consistent AI-assisted workflows across the agency reduce variability in research and drafting quality; and Cost Savings: Reduces time spent on routine knowledge tasks, allowing staff to focus on complex decision-making and judgment-based work.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: ICE Terminology and Data Asset Discovery Chatbot
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of the time-consuming process of manually searching internal ICE resources for dataset information to create logical data definitions.
Purpose and benefits: The chatbot is expected to help the Data Management Team quickly locate relevant SharePoint information and assist in reviewing and refining data element information. By reducing the time spent searching for information and creating logical data definitions, the chatbot enables personnel to focus on higher-value tasks.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AskTSA
Agency: Department of Homeland Security - TSA
Problem addressed: The AI is intended to address several challenges within the AskTSA customer service process. These include reducing the time it takes for agents to respond to inquiries, improving the accuracy and consistency of responses, and streamlining the categorization of incoming inquiries. Additionally, the AI would help identify areas for improvement in the virtual assistant’s performance and provide actionable insights to enhance its effectiveness. By automating repetitive tasks like categorizing inquiries, the AI would allow human agents to focus on more complex issues, ultimately improving efficiency and customer satisfaction.
Purpose and benefits: The AI’s intended purpose is to enhance the efficiency, accuracy, and overall effectiveness of the AskTSA customer service process. It would assist human agents by automating routine tasks such as categorizing inquiries and summarizing customer concerns, enabling faster and more consistent communication with the public. Additionally, the AI would analyze interactions with the virtual assistant to identify areas for improvement and recommend adjustments to ensure it provides accurate and helpful responses. By streamlining workflows and providing actionable insights, the AI would support TSA’s goal of delivering high-quality, timely, and reliable customer service.
Topic area: Service Delivery
Development stage: Deployed
First used: 2024-12-16
US federal AI use case: Lexis Nexis
Agency: Department of Homeland Security - TSA
Problem addressed: Identity verification for passenger screening and bolster transportation security.
Purpose and benefits: Lexis Nexis assists TSA Analysts in identity verification for passenger screening and bolster transportation security.
Topic area: Transportation
Development stage: Deployed
First used: 2024-09-23
US federal AI use case: SITE Group Subscription Services
Agency: Department of Homeland Security - TSA
Problem addressed: This tool assists TSA with identifying known and unknown threats to the U.S. and international aviation and surface transportation systems.
Purpose and benefits: AI-integration within Site group enhances its capabilities in monitoring and analyzing extremist activities online.
Topic area: Transportation
Development stage: Deployed
First used: 2025-09-20
US federal AI use case: CBP Link
Agency: Department of Homeland Security - CBP
Problem addressed: CBP Link utilizes liveness to ensure the photo that TVS it utilizing to conduct facial matching to CBP holdings is of a live person and not a photo of a 2-D image and well as validate the presence of a specific user when collecting geolocation to validate that person is in the required location.
Purpose and benefits: CBP Link uses liveness detection and TVS uses facial recognition to compare live or uploaded images with CBP's database, enabling real-time identity verification. This automation streamlines border processes, enhances accuracy, and reduces fraud.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-06-16
US federal AI use case: Service Now Predictive Intelligence Tool
Agency: Department of Homeland Security - TSA
Problem addressed: This solves the issue of submitting duplicate or very similar Projects, Demands and Ideas.
Purpose and benefits: This tool assists in preventing wasted time in working through Projects, Demands, or Ideas that have already been processed.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: License Plate Reader
Agency: Department of Homeland Security - USSS
Problem addressed: To identify license plate information quickly, efficiently, and accurately from low-quality imagery, advanced image processing and machine learning techniques are typically employed. Need to be able to enhance the clarity of the image, correct distortions, and extract relevant details, even in challenging conditions such as low resolution, poor lighting, motion blur, or obstructions.
Purpose and benefits: The purpose of identifying license plate information from low-quality imagery is to enable accurate and efficient vehicle identification for applications such as law enforcement, traffic monitoring, parking management, border security, and access control, ensuring operational efficiency and enhanced security.
Topic area: Law Enforcement
Development stage: Deployed
First used: 2025-01-07
US federal AI use case: Extended Automated Name Harvesting (eANH)
Agency: Department of Homeland Security - USCIS
Problem addressed: OIT is developing a solution that systematically extracts text from evidence documents, identifies aliases and DOBs from the extracted text.
Purpose and benefits: Since users no longer need to read through the entire set of case evidence, which is often hundreds of pages, this should decrease case processing time while retaining the same or better performance.
Topic area: Benefits Processing
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: No. 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 the 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.
US federal AI use case: Enterprise WiFi
Agency: Department of Homeland Security - USSS
Problem addressed: Reduce the manual investigation/intervention to configure, monitor and troubleshooting issues.
Purpose and benefits: Reduced time to resolve reported network issues and outages.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: Border Infrastructure Center of Excellence AI (BICE AI)
Agency: Department of Homeland Security - CBP
Problem addressed: The BICE AI system automates the creation of precise legal descriptions, parcel reports, and boundary maps from geospatial data, reducing manual effort, errors, and time. By leveraging generative AI, it ensures accuracy, compliance, and efficiency, enabling real estate professionals to focus on higher-value tasks and strategic decision-making.
Purpose and benefits: The intended purpose of the BICE AI is to automate the generation of precise legal descriptions from geospatial data. It's designed to act as an expert assistant for real estate professionals. The expected benefits are increased efficiency, accuracy, and compliance in creating legal documentation. By automating this process, the system frees up specialists to focus on higher-value tasks, reducing the reliance on manual drafting and review by subject matter experts.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: License Plate Capture and Analysis
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of time-consuming manual reviews of license plate images and data, which makes it challenging for investigators to identify relevant vehicle movements and patterns.
Purpose and benefits: The AI capabilities reduce the need for manual review of large numbers of license plate images and logs. By streamlining plate reading and providing flexible search and summarization tools, the system helps investigators more quickly identify potentially relevant vehicle movements and patterns that might otherwise be missed, thereby improving the efficiency and effectiveness of investigative work.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-09-19
US federal AI use case: Global Maritime Intelligence
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of investigators having to manually piece together fragmented maritime activity data from many sources, which makes it difficult to see relationships among vessels, shipments, and ports and identify potential leads on illicit maritime activity.
Purpose and benefits: The use of AI in this process helps Homeland Security Investigations quickly identify potential threats, improves the efficiency of intelligence operations, and enables faster responses to maritime risks that would be difficult to detect through manual analysis alone.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case does not meet the definition of "high-impact" as outlined in Section 5 of M-25-21 because its outputs do not serve as a "principal basis for decisions or actions that have a legal, material, binding, or significant effect on rights or safety." Instead, the AI system generates intelligence reports and risk assessments that are used to support human decision-making. Analysts review the outputs and initiate follow-on actions after validating the source data, such as inspections or investigations, ensuring that the AI's outputs are not the sole or principal basis for these decisions. 

While the use case aligns with a presumed high-impact category due to its critical role in maritime safety and law enforcement, it does not meet the stricter definition of high-impact because its outputs are advisory and produce leads that must be validated as part of the investigative process prior to action being taken.
US federal AI use case: Land Border Integration
Agency: Department of Homeland Security - CBP
Problem addressed: The Land Border Integration system addresses the need for efficient and accurate data capture and processing during vehicle inspections at land-border Ports of Entry (PoEs). The system is designed to detect and interpret alphanumeric values from license plates captured by cameras, integrating with License Plate Readers (LPR) to classify license plate numbers, their country, and state of origin. Additionally, it leverages facial recognition technologies to analyze vehicle occupants, enhancing situational awareness for border officers.

By utilizing artificial intelligence (AI) technologies and edge-based processing, the system minimizes reliance on centralized systems and enables real-time video stream analysis. This ensures timely and actionable insights, allowing officers to vet travelers more efficiently without manually entering information. The solution supports critical operations by improving the speed and accuracy of data processing, enhancing operational effectiveness, and streamlining the inspection process.
Purpose and benefits: The intended purpose of the Land Border Integration system is to enhance the efficiency and accuracy of data processing during vehicle inspections at land-border Ports of Entry (PoEs). By leveraging artificial intelligence (AI) technologies, the system captures and interprets visual data, including license plate information, vehicle classification (make, model, and color), and occupant identification through facial recognition, in real time.

This edge-based AI solution supports situational awareness and operational decision-making by providing timely and actionable insights to officers. It minimizes reliance on centralized systems, streamlines the vetting process, and reduces the need for manual data entry, enabling officers to focus on critical tasks. The system improves operational effectiveness, enhances border security, and facilitates efficient traveler processing.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2021-04-12