GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Entity Resolution
Agency: Department of Homeland Security - CBP
Problem addressed: The use case leverages advanced technology to improve the analysis of global trade data and enhance its ability to identify risks within supply chains. By using AI tools to organize and analyze complex datasets, CBP can uncover patterns and relationships that may indicate unethical practices, such as forced labor. This innovative approach supports efforts to ensure compliance with trade laws, protect economic security, and promote fair and ethical trade practices.
Purpose and benefits: Detection research of force labor within supply chain utilizing analytical AI platform.
Topic area: Law Enforcement
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. 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: 2023-05-01
US federal AI use case: Answer Engine
Agency: Department of Homeland Security - TSA
Problem addressed: The Transportation Security Administration (TSA) faces increasing challenges in managing and analyzing large volumes of complex data, which can hinder the effectiveness and efficiency of its security operations. Without advanced tools to streamline data processing and generate actionable insights, the TSA’s ability to respond to evolving threats and optimize operational decision-making is limited. There is a critical need for a scalable solution that can enhance data management and analysis capabilities, enabling TSA personnel to make more informed, timely, and effective security decisions.
Purpose and benefits: TSA aims to enhance its capabilities in managing and analyzing complex data, ultimately contributing to more effective and efficient security operations and optimizing the TSA's operational workflows and support capabilities.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: SEVP Response Center Chatbot - SID (SEVIS Interactive Dialog)
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of handling a high volume of routine inquiries from students and officials, which can overwhelm human agents and delay response times.
Purpose and benefits: AI provides SID the ability to understand voices and its deterministic question and answer workflow (1) enables SID to answer routine caller questions without a help desk agent, and (2) when a help desk agent is required, SID will create a ticket with a caller transcript to reduce the burden on the agent. This frees up the human agents to deal with more complex cases and issues with specific records.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Security Operation Center (SOC) Network Anomaly Detection
Agency: Department of Homeland Security - CISA
Development stage: Retired
US federal AI use case: DHS Asset Assessment Tool
Agency: Department of Homeland Security - MGMT
Problem addressed: Reduces users’ required time, effort, preexisting knowledge of which dataset has which information, and expertise with writing functional SQL.
Purpose and benefits: One of DHS’s largest expenses is their people, their facilities and their assets. The value of the Asset Assessment tool is to improve the efficiency of DHS Property Manager’s analysis and planning process by expediting and automating today’s very manual assessment of facility placement and consolidation. The tool also provides enhanced views of information that were not available previously – like the prediction of facility cost over time for multiple facilities using actual utilization data inputs. The tools provides developer time and ultimately resource cost savings from removing manual SQL development steps from the analysis process. The tool ultimately reduces users’ required time, effort, preexisting knowledge of which dataset has which information, and expertise with writing functional SQL.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Hurricane Score
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of understanding which noncitizens in Enforcement and Removal Operations’ Alternatives to Detention - Intensive Supervision Appearance Program are most likely to abscond.
Purpose and benefits: The Hurricane Score helps officers quickly evaluate substantial amounts of case information across thousands of Alternatives to Detention - Intensive Supervision Appearance Program participants. By surfacing a risk indicator based on observed absconding patterns, it can provide additional insight that might not be apparent from manual review alone. This supports more consistent and efficient case reviews and helps officers allocate case management resources more effectively while maintaining individualized assessments.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2019-02-01
US federal AI use case: ICE Mobile Check-in Application
Agency: Department of Homeland Security - ICE
Development stage: Retired
Risk category: High-impact
US federal AI use case: Supervised Traveler Identity Verification Services (Officer Initiated)
Agency: Department of Homeland Security - CBP
Problem addressed: Biometric Entry processing fulfills a Congressional mandate.
Purpose and benefits: The TVS Biometric matching service is a cloud-based facial biometric matching service that enables CBP to match a passenger’s identity against a trusted source, throughout the travel continuum which improves traveler facilitation and reduces manual identity verification
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2017-09-01
US federal AI use case: Semi-Supervised Traveler Identity Verification Services (Traveler Initiated)
Agency: Department of Homeland Security - CBP
Problem addressed: Biometrically processes travelers on entry.
Purpose and benefits: The TVS Biometric matching service is a cloud-based facial biometric matching service that enables CBP to match a passenger’s identity against a trusted source, throughout the travel continuum which improves traveler facilitation and reduces manual identity verification.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-02-01
US federal AI use case: 3rd Party Traveler Identity Verification Services
Agency: Department of Homeland Security - CBP
Problem addressed: Biometric Exit processing fulfills a Congressional mandate.
Purpose and benefits: TVS is a cloud-based facial biometric matching service that enables CBP, External Partners, and Other Government Agencies (OGA) to match a passenger’s identity against a trusted source, throughout the travel continuum, which improves traveler facilitation and reduces manual identity verification.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2017-05-01
US federal AI use case: Traveler Self-Service Mobile Identity Verification
Agency: Department of Homeland Security - CBP
Problem addressed: The use of the Traveler Verification Service (TVS) in these use cases enables biometric identity verification and facilitates travel.
Purpose and benefits: The TVS Biometric matching service is a cloud-based facial biometric matching service that enables CBP to match a passenger’s identity against a trusted source, throughout the travel continuum which improves traveler facilitation and reduces manual identity verification
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2022-01-01
US federal AI use case: Traveler Identity Verification Services (Vetting)
Agency: Department of Homeland Security - CBP
Problem addressed: The use of the Traveler Verification Service (TVS) enables CBP to enhance the identification of possible threats by leveraging facial recognition technology to identify biometric matches to derogatory records that are not identified through existing biographic targeting and entity resolution mechanisms.
Purpose and benefits: CBP's Traveler Identity Verification Services (Vetting) utilizes facial recognition technology to enhance threat identification by matching travelers' biometrics against records of concern.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2018-12-01
US federal AI use case: Process Efficiency Traveler Identity for Airline Check-in and Bag Drop
Agency: Department of Homeland Security - CBP
Problem addressed: These use cases facilitate identity verification leveraging TVS.
Purpose and benefits: The TVS Biometric matching service is a cloud-based facial biometric matching service that enables CBP, External Partners, and Other Government Agencies (OGA) to match a passenger’s identity against a trusted source, throughout the travel continuum which improves traveler facilitation and reduces manual identity verification.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: This is an option for travelers, not a requirement. If a traveler chooses to use it and the service cannot match a traveler, the traveler may continue check-in/bag drop via another means. CBP does not make any decision or action based on a no-match.
First used: 2018-10-01
US federal AI use case: MiX Phenotyping
Agency: Department of Homeland Security - OHS
Problem addressed: Syndromic surveillance
Purpose and benefits: Electronic medical records can provide key data for health security insights. AI/ML makes it possible to translate these records into machine-readable data, which is the first step to finding these health security insights. AI/ML will then be used to find clinical patterns across the data, such as patterns in symptoms over time and location. AI/ML will also be used to automate the process for detecting new trends (anomalies) in these clinical patterns. These health security insights can alert about potential threats, inform messaging, and provide decision support to medical and public health partners.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: MiX Indicators
Agency: Department of Homeland Security - OHS
Problem addressed: Identification of emerging health threats.
Purpose and benefits: Online news and other open-source media can be used to quickly detect and respond to emerging health security threats. However, it is not practical for people to read and scan all online news stories every day for key terms. AI/ML makes it possible to read, digest and organize news stories to look for key threat terms. AI/ML will also be used to predict normal trends in news stories. This makes it possible to detect when an unusual number of health security threat terms are found in the news. This use of AI/ML helps prepare for, respond to, and protect from potential health security threats.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: MiX MedINT (Medical Intelligence Dashboard and Canvas)
Agency: Department of Homeland Security - OHS
Development stage: Retired
US federal AI use case: One Health Threat Detection and Risk Assessment Platform (OH-TREADS) / Planner
Agency: Department of Homeland Security - OHS
Problem addressed: Early warning, Decision Making, and Situational Awareness
Purpose and benefits: Visuals will be created using a broad variety of data pulled from open-source, public, and non-public sources. AI/ML will make it possible to ingest data from a variety of digital formats, and translate it to usuable information into machine-readable data. The volume of data, and lack of clear data collection standards, requires AI to help merge streams with different data ontologies while facilitating data interoperability with mis-paired data sets. Neither activity is, or can be, practically performed by people. AI/ML will be further used in Planner to generate risk and predictive scores in addtion to displaying relevant information and analyses that help analysts understand health security threats and broadly monitor the health security landscape. AI/ML is meant to provide comprehensive situational awareness for health surveillance and public health response, with target capabilities that: enable global situational awareness of current and potential health risks from a One Health perspective; assist in early warning of health threats by location, facility, and species; aid in rapid identification of health threats at population, facility, and greater geographic resolution; and support data-driven decision making to prevent, mitigate, and respond to health threats.
Topic area: Health and Medical
Development stage: Pre-deployment
US federal AI use case: Digital Records Manager (DRM) User Assistance Chatbot
Agency: Department of Homeland Security - ICE
Problem addressed: The AI tool, the Digital Records Manager (DRM) User Assistance Chatbot, is designed to address the problem of efficiently searching and gathering information within the Investigative Case Management (ICM) system used by Homeland Security Investigations (HSI). Specifically, it provides immediate, on-demand assistance to investigators by allowing them to pose natural language questions about using the DRM application for case and media management.
Purpose and benefits: This is an AI records search tool to help investigators more efficiently search and gather information. 

The Digital Records Manager (DRM) User Assistance Chatbot is intended to increase user efficiency by providing answers to commonly asked question without the need to manually refer to documentation or submit a help desk ticket.  A reduction in the volume of submitted help desk tickets is expected as a result.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: AI Assisted Compromise Email Detector (AACED)
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of the extensive manual effort required to review emails for signs of cyber compromise.
Purpose and benefits: The use case was developed to assist ICE SOC in reviewing a collection of emails between ICE personnel and Microsoft that were part of Emergency Directive 24-02.  The use case provides a faster mechanism to the SOC analysts to determine indicators of compromise, reducing the level of effort for these individuals’ analysis exponentially.  To assist the analysts, Named Entity Recognition (NER) was used to detect PII and other associated keywords to increase analyst productivity, and reduce time required to analyze emails.
Topic area: Cybersecurity
Development stage: Deployed
First used: 2024-06-01
US federal AI use case: Intelligent Document Processing
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of the manual effort required to validate and extract data from forms.
Purpose and benefits: Business units within ICE leverage these services to automate repeatable, time-consuming processes such as invoice processing, and form entry validation and extraction. This platform will provide Optical Character Recognition and machine learning models to verify, extract, and classify information from ICE forms. Using AI to provide information extraction for these processes saves ICE personnel a significant amount of time while improving data quality and enabling automation.
Topic area: Admin Functions
Development stage: Deployed
First used: 2019-06-01
US federal AI use case: Cybersecurity Threat Management, Detection, and Response
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of detecting and responding to cybersecurity threats in a timely manner.
Purpose and benefits: These AI capabilities provide security analysts with modern tools to identify and respond to threats much more quickly than previously possible, minimizing potential damage to systems and data.
Topic area: Admin Functions
Development stage: Deployed
First used: 2021-03-16
US federal AI use case: Translation and Transcription for Investigative Data
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of the time-consuming process of translating and transcribing data for investigative purposes.
Purpose and benefits: HSI investigators often encounter data from various sources, including legal and administrative processes, enforcement actions, and open-source materials, in languages other than English. To unlock the value of this data, it must be translated into English before further analysis can be conducted. The Translation and Transcription Service leverages neural machine translation (NMT) models for text translation and automatic speech recognition (ASR) and deep neural network (DNN) models with normalization for voice-to-text transcription. This innovative approach enables users to quickly triage large datasets and identify key information relevant to investigations. Any data deemed critical for court proceedings is then submitted to certified human translators for final review, ensuring that government resources are allocated efficiently and only used for necessary translations and transcriptions.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case provides more efficient data processing for HSI personnel. The AI output may be used to produce investigative insights in the form of data, information leads or connections that HSI personnel can use to inform investigations, but the output itself is data preparation and organization so HSI personnel can produce those leads when combining the AI output with the personnel’s expertise and other relevant investigative data and information. 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 law enforcement action or decision. Any enforcement decisions related to these insights are outcomes of the full Federal Investigation Process involving verifying any insights as evidence (including validating AI-translated material by a certified interpreter), presentation to a U.S. Attorney’s Office and potentially a District Court judge, decision to prosecute, judicial review, and trial and sentencing.
First used: 2024-02-01
US federal AI use case: Contract Requirement Automation
Agency: Department of Homeland Security - TSA
Problem addressed: The AI solution is intended to solve the problem of labor-intensive manual creation and management of procurement requirement documents at TSA. The solution provides the TSA user with contextually accurate outputs (primarily in the form of automated document generation and recommendations for procurement documentation) tailored to specific requirements. The platform also provides contextual recommendations during the document creation process to ensure completeness and compliance with procurement requirements.
Purpose and benefits: Eliminates the manual burden and reduces errors in document creation, where staff previously spent excessive time writing requirements to meet specific procurement needs from scratch. Increases documentation accuracy, consistency and standardization. Improves management and verification of requirement documents. Cost savings represent another major benefit, as the platform reduces labor costs associated with manual document creation and management. By streamlining the procurement process, the agency can complete more procurement actions with existing resources, maximizing taxpayer dollars. The automated tool also reduces the time spent on repetitive tasks, allowing for better resource allocation.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: TSA Case Handling Platform
Agency: Department of Homeland Security - TSA
Problem addressed: The tool saves case workers manual time to download, order, and compile reports.
Purpose and benefits: The tool saves case workers manual time to download reports, order, and compile. The case workers can then use their time to work on cases rather than administrative tasks. Newton POC has the potential of streamlining collection processes related to cases, and creating custom reports from various materials during the case managers interview process, producing a centralized tool to manage and control all steps within each case.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Automated Field Data Collection
Agency: Department of Homeland Security - TSA
Problem addressed: The proliferation of screening technologies has increased the number of field data collection events necessary to characterize system performance. Currently, TSA does not have a solution to gather operational data without deploying physical teams. Addressing this gap presents an opportunity to achieve significant field efficiencies through automation and enhance wait times communication.
Purpose and benefits: The AI will analyze screening environments via CCTV footage and extract passenger processing times of various steps within the screening processes. Enabling AI to extract and visualize this data will enable TSA to make data informed decisions while testing or deploying new screening equipment, identify anomalies, establish real-world rates and standards, and reduce or eliminate TSA’s need to deploy data collection teams, resulting in real-time data collection and significantly reduced computational time of findings.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Plan of Day Staff Optimization
Agency: Department of Homeland Security - TSA
Problem addressed: TSA must deploy a dynamic solution capable of locally optimizing allocated employee staffing while load balancing available equipment and projected passenger throughput to ensure wait times do not exceed the threshold.
Purpose and benefits: Plan of Day will automate TSA screening staff optimization.
Topic area: Transportation
Development stage: Pre-deployment
US federal AI use case: Airport Throughput Predictive Model
Agency: Department of Homeland Security - TSA
Problem addressed: This use case is a predictive model for passenger volume to help with airport staffing.
Purpose and benefits: This project was to create a predictive model for the passenger volume using the Security Operations throughput count from checkpoints to help with airport staffing.
Topic area: Admin Functions
Development stage: Deployed
First used: 2024-04-01
US federal AI use case: DHS-Chat
Agency: Department of Homeland Security - MGMT
Problem addressed: DHS personnel need a reliable solution to quickly access accurate information, process documents, and support work tasks within the DHS Workspace. Existing workflows are often time-consuming and inefficient, and there is a need to streamline operations while maintaining compliance with security requirements.
Purpose and benefits: This is a chatbot based on a Large Language Models (LLM) for internal DHS employee use - It's like chat-GPT for DHS but approved for use with non-classified but internal information (this includes FOUO (For Official Use Only) CUI (Controlled Unclassified Information) due to its improved security compared to publicly available chatbots. 

This tool is able to dynamically create written content through text prompts submitted by the user. Approved applications of this tool to DHS business include generating first drafts of documents that a human would subsequently review, conducting and synthesizing research on open-source information and internal documents, and developing briefing materials or preparing for meetings and events.
Topic area: Admin Functions
Development stage: Deployed
First used: 2024-12-12
US federal AI use case: User and Entity Behavior Analytics (UEBA)
Agency: Department of Homeland Security - MGMT
Problem addressed: The Continuous Vetting Analytics Service (CVAS) sub-system in the Vetting Identities for an Enterprise Workforce (VIEW) system of record will solely be used to aggregate information provided by or authorized to be collected by an DHS applicant and then presents the aggregated information in a structured manner and present it to personnel security adjudicative specialist to help them prioritize workload and improve review processes.
Purpose and benefits: Enhanced Threat Detection: Identify patterns in user behaviors that deviate from normal baselines, signaling potential insider threats or security risks. Continuous Risk Assessment: Move from static vetting to a continuous vetting process by monitoring real-time activities and interactions with secure information.
Improved Incident Response: Enable rapid responses to high-risk behaviors, escalating alerts for timely intervention by security personnel."
Topic area: Admin Functions
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The CVAS sub-system inside the VIEW system of record is not a high-impact use case because the Al's output from CVAS does not actually "serve as a principal basis for" the relevant type of agency action or decision. The collated information supports decisions about staffing priorities but is not used to make security-related or employment-related decisions. The use case also does not perform workplace monitoring or surveillance.
First used: 2023-07-08
US federal AI use case: Burlington Finance Center Voice Bot
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of the manual and time-consuming process of handling routine bond inquiries.
Purpose and benefits: Identify and verify the caller, retrieve the status of the bond, and share the bond status with the requester and/or answer administrative FAQs.
Topic area: Service Delivery
Development stage: Pre-deployment
US federal AI use case: Hazard Mitigation Assistance Chatbot
Agency: Department of Homeland Security - FEMA
Problem addressed: Understanding of Hazard Mitigation Assistance (HMA) programs and data analysis of HMA data. The tool will lead to a consistent and accurate understanding of the programs and contribute to a standardization of project reviews.
Purpose and benefits: Leverage advanced Artificial Intelligence (AI) capabilities to address challenges faced by Hazard Mitigation Assistance (HMA) staff, enhancing productivity for day-to-day functions.
Topic area: Admin Functions
Development stage: Pilot
First used: 2025-08-18
US federal AI use case: Recovery and Resilience Resource (RRR) Portal
Agency: Department of Homeland Security - FEMA
Development stage: Retired
US federal AI use case: OCFO Code Assist GPT
Agency: Department of Homeland Security - FEMA
Problem addressed: Code Assist Generative Pre-trained Transformer (GPT) is an internal facing Generative AI (GenAI) tool to augment the FEMA workforce in generating and troubleshooting existing queries in established query languages (e.g., SQL, Java, COBOL). Users enter the language they are querying, and the Code Assist GPT then provides a proposed query based on the elements provided. If the query is unsuccessful, the tool maintains the session, allowing users to prompt for enhancements until expected results are achieved. At the end of the session, all prompts and queries are removed, and no data is stored outside of the active session. The tool provides improves query generation and rapid iteration, saving users 80-90% of the time compared to custom query development. It also supports various computer languages to assist the data analytics community.
Purpose and benefits: Saving users 80-90% of the time compared to custom query development, rapid iteration which includes error resolution to query complex data sets and multiple computer languages (COBOL, SQL, Python, etc)
Topic area: Admin Functions
Development stage: Deployed
First used: 2025-04-11
US federal AI use case: Digital Processing Procedure Manual (D-PPM)
Agency: Department of Homeland Security - FEMA
Development stage: Retired
US federal AI use case: API Security Vulnerability Technology
Agency: Department of Homeland Security - CBP
Problem addressed: Discover, ingest, and analyze APIs to create and run thousands of custom attack scenarios against every build prior to production. Catch security vulnerabilities as early in the software development life cycle (SDLC) as possible. Educate and empower security and dev teams on sound API security strategies.
Purpose and benefits: With the necessity of leveraging application programing interfaces (APIs) for applications across the enterprise, this AI technology is intended to run thousands of custom attack scenarios against APIs on a continuous basis. This will help to identify potential security vulnerabilities prior to production level deployments, and enable the enterprise to develop essential remediations, if required.  Additionally, the techonlogy is intended to provide continuous monitoring on APIs to provide real-time alerts on new potential vulnerabilities.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Cyber Threat Detection
Agency: Department of Homeland Security - CBP
Problem addressed: Cyber deception is a proactive cybersecurity strategy that involves creating a network of deceptive elements, such as decoys to mislead and divert potential attackers. By strategically deploying these deceptive artifacts across an organization’s network, cyber deception aims to confuse attackers and delay their progress.
Purpose and benefits: Cyber deception is used alongside other cybersecurity measures to enhance overall security posture. Cyber deception not only enhances threat detection but also provides valuable insights into attacker behavior, aiding in the development of more effective defense strategies and minimizing the risk of successful cyberattacks. Cyber deception technology plays a crucial role in enhancing cybersecurity defenses by enabling organizations to detect threats faster and decrease attacker dwell time.
Topic area: Cybersecurity
Development stage: Pre-deployment
US federal AI use case: Forced Labor Virtual Consultant
Agency: Department of Homeland Security - CBP
Problem addressed: Customs and Border Protection (CBP) enforces U.S. laws against importing goods made with forced labor by analyzing extensive data and addressing complex global supply chains. CBP aims to leverage advanced tools to protect U.S. economic security and uphold its leadership in combating forced labor.
Purpose and benefits: The system aims to support Customs and Border Protection (CBP) analysts by integrating internal forced labor databases with preloaded trend analysis data, enabling rapid risk identification and report generation. It complements CBP’s data science team by providing faster access to critical information, enhancing enforcement efficiency.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Multi-media Insight Tool
Agency: Department of Homeland Security - CBP
Problem addressed: The AI system solves the challenge of analyzing large volumes of surveillance footage and audio data efficiently. It enables Customs and Border Protection (CBP) to conduct real-time searches for objects, sounds, language, and events, detect anomalies, and track items of interest across multiple video streams concurrently. By providing outputs like real-time alerts and visual annotations, the system enhances situational awareness, streamlines investigations, and supports faster decision-making to improve border security and operational efficiency.
Purpose and benefits: The system aims to enhance CBP's ability to monitor and analyze surveillance footage from existing CBP camera technology, enabling real-time detection of anomalies, and tracking of Items of interest within the video frame. It will improve situational awareness, streamline the object and event identification process, and support faster, more accurate decision-making, ultimately enhancing security and operational efficiency.  Essentially, this technology will allow users to review significant amounts of historical imagery to identify objects, scenes, and activities of interest and reduce the manual burden of searching multiple video streams.
Topic area: Admin Functions
Development stage: Pre-deployment
US federal AI use case: Position Description Generation and Evaluation
Agency: Department of Homeland Security - CBP
Problem addressed: Federal agencies often face challenges in creating accurate, consistent, and well-structured position descriptions (PDs) due to limited resources, the time-intensive nature of the classification process, and varying levels of expertise among HR staff and Hiring Managers. Inaccurate or poorly written PDs can lead to misclassification, legal disputes, grievances, and difficulties attracting qualified candidates, ultimately impacting workforce quality and agency performance.
Purpose and benefits: ClassifAI is designed to streamline and enhance the position description (PD) creation and classification process by leveraging generative AI to produce drafts of accurate, consistent, and standards-compliant PDs. By reducing administrative burdens, improving PD quality, and minimizing classification risks, ClassifAI enables agencies to optimize workforce management, attract top talent, and achieve greater operational efficiency with fewer resources.
Topic area: HR
Development stage: Deployed
First used: 2025-05-16
US federal AI use case: Source Code Development Tool
Agency: Department of Homeland Security - CBP
Problem addressed: The tool uses generative artificial intelligence (GenAI) powered by large language models (LLMs) and coding foundation models to assist with software development. The AI serves as a coding assistant, allowing users to create, refine, and complete software projects through natural language commands and queries. It generates functional software code, streamlining the development process, reducing manual effort, and improving efficiency.
Purpose and benefits: The tool is designed to enhance software development efficiency by enabling end users to create, refine, and complete projects more quickly through the use of a generative artificial intelligence (AI) coding assistant. By automating portions of the coding process, the tool reduces development time, optimizes workflows, and improves overall productivity.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: ESEC Inquiry (STORM) Summarization
Agency: Department of Homeland Security - MGMT
Problem addressed: This use case is intended to decrease the time it takes to enter incoming requests into Storm. By summarizing the request, analysts are able to more quickly take action and assign to relevant parties. It will also lead to future capabilities to draft responses to those requests.
Purpose and benefits: ESEC-STORM AI employs Generative Artificial Intelligence (Gen-AI) technology to automate the creation of document summaries. This advanced system facilitates the automatic integration of these summaries into the System of Tracking, Operations, and Record Management (STORM), thereby optimizing the management of correspondence and information requests.
Topic area: Admin Functions
Development stage: Deployed
First used: 2024-12-13
US federal AI use case: LIGER Generative AI Toolkit
Agency: Department of Homeland Security - MGMT
Problem addressed: LIGER® for FPS will enable FPS users to employ the power of a Large Language Model (LLM) against non-public and sensitive Agency documents to save time and effort conducting time-intensive tasks, such as draft documents to include: Position Descriptions (PD), Statement of Work (SOW) for contracting actions, Professional emails and workforce announcements, Public Affairs stories and releases, and Law Enforcement operations orders; Summarization of large documents; Proofreading and providing feedback or suggestions on written work; conducting Policy analysis to include: Policy comparison and compliance verification, building textual process maps based on policy, and identifying contradictory or outdated policy; Budget forecasting and spend plan analysis; Assisting with code generation, review, and debugging; machine language translation; brainstorming ideas of projects or processes; and information/data retrieval from document libraries. The system will also be used to automate manual processes related to generation of templated documents.
Purpose and benefits: FPS personnel spend considerable time creating documents from scratch or manually searching large volumes of documents to retrieve information, identify responsibilities, and find discrepancies or outdated passages within policies. Use of LIGER® is expected to substantially reduce time and associated costs in generating new draft documents such as Statements of Work, Law Enforcement operations orders, updated Position Descriptions, and other documents using previous similar examples, reviewing large volumes of information for outdated policy or discrepancies with new DHS policy or Executive Orders, and summarizing large single documents or document collections. LIGER’s ability to securely handle sensitive information and return responses based on custom document collections offers advantages through the ability to securely handle Controlled Unclassified Information (CUI) such as LES and FOUO data which is not suitable for other GenAI applications. Additionally, because LIGER® cites sources, users can rapidly find where specific information from the generated narrative can be found in source documents.
Topic area: Admin Functions
Development stage: Pilot
First used: 2025-08-26
US federal AI use case: Facial Recognition for Locating Vulnerable Populations
Agency: Department of Homeland Security - ICE
Problem addressed: The use case addresses the challenge of identifying and locating members of vulnerable populations, such as unaccompanied minors who have crossed the border, whose identities and locations are unknown to law enforcement.
Purpose and benefits: The facial recognition service reduces the time personnel spend manually searching for images online and helps them discover potentially relevant photographs or profiles that they might not otherwise find. This can improve the speed and effectiveness of efforts to identify and locate vulnerable individuals and support appropriate protective or assistance measures.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-01-23
US federal AI use case: Facial Recognition for National Security Investigations
Agency: Department of Homeland Security - ICE
Problem addressed: This use case addresses the challenge of identifying individuals of interest in authorized national security investigations.
Purpose and benefits: The facial recognition service helps investigators reduce the time needed to manually search for images and associated information online. By surfacing potentially relevant images that might otherwise be missed, it can improve the speed and effectiveness of national security investigations and allow investigators to focus their efforts on analysis, corroboration, and case-building.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-01-23
US federal AI use case: Facial Recognition for Investigations of Transnational Criminal Organizations
Agency: Department of Homeland Security - ICE
Problem addressed: The problem this use case solves is the challenge of identifying unknown individuals involved in transnational criminal activities, such as violent crimes, drug trafficking, human smuggling, and financial fraud.
Purpose and benefits: The facial recognition service reduces the time investigators spend manually searching for images online and helps them discover potentially relevant photographs or profiles that they might not otherwise find. This can improve the speed and effectiveness of investigations into complex transnational criminal networks while allowing investigators to focus on analysis, corroboration, and case-building.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-01-23
US federal AI use case: Purchase Card Worksheet Analysis
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of reviewers having to manually check each purchase card line item against numerous laws, policies, and contract terms, which is time‑consuming and increases the risk of missed compliance issues.
Purpose and benefits: The AI module is expected to help accelerate compliance checks, streamline processes, and assist in identifying potential risks, while continuously improving through a human-in-the-loop feedback system. This integration is intended to help procurement actions better align with relevant legal and policy requirements.
Topic area: Procurement and Finance Mgmt
Development stage: Pre-deployment
US federal AI use case: Incident Management Workforce Deployment Model (depmod)
Agency: Department of Homeland Security - FEMA
Development stage: Retired
US federal AI use case: Individual Assistance (IA) & Public Assistance (PA) Projections
Agency: Department of Homeland Security - FEMA
Development stage: Retired
US federal AI use case: AI-Assisted Audio/Video Redaction
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of the labor-intensive process of redacting audio and video evidence.
Purpose and benefits: The AI-Assisted Audio/Video Redaction tool is used to reduce the manual effort required to redact audio and video evidence used during an investigation and subsequent legal proceedings.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: The AI-Assisted Audio/Video Redaction use case does not meet the definition of a High-Impact category under OMB M-25-21 due to its narrowly defined scope, reliance on human oversight, and non-decision-making role. This tool is designed to assist Homeland Security Investigations (HSI) by partially automating the redaction of audio and video evidence, such as detecting and obscuring faces, objects, or sensitive information (e.g., license plates, PII), to protect individuals’ identities during investigations and legal proceedings. Importantly, the tool does not perform 1:1 facial matching or identification, and its outputs are strictly limited to redactions, which are subject to comprehensive human review and editing before finalization. This ensures that the AI’s role is supportive rather than determinative, with no direct impact on investigative decisions or legal outcomes.
First used: 2024-07-01
US federal AI use case: Natural Language Search for Legal Case Management
Agency: Department of Homeland Security - ICE
Problem addressed: The AI is intended to solve the problem of Office of the Principal Legal Advisor (OPLA) personnel having to craft complex searches and manually review large volumes of case documents in its OPLA Case Management System to find relevant information.
Purpose and benefits: The AI-enabled search capabilities will enable Office of the Principal Legal Advisor (OPLA) users to more efficiently search and extract relevant information from the OPLA Case Management System. This enhancement improves efficiency, saves time, and enables OPLA personnel to focus on higher-value tasks, ultimately supporting more effective case management.
Topic area: IT
Development stage: Pre-deployment
US federal AI use case: USCIS Speech Translation Service
Agency: Department of Homeland Security - USCIS
Problem addressed: Provide efficient communication, consultations, and other live applicant interactions in USCIS offices.
Purpose and benefits: Reduce the manpower needed to verbally communicate with applicants and ensure that they are directed in an accurate and efficient manner.
Topic area: Service Delivery
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: AI-Enhanced ICE Tip Processing
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of the time-consuming manual effort required to review and categorize incoming tips.
Purpose and benefits: The use of AI in this process enables the Tip Line team to more quickly identify and action tips recommended for urgent case categories. Additionally, the introduction of a BLUF field saves time by providing analysts with a high-level understanding of a tip before they review its details.
Topic area: Law Enforcement
Development stage: Deployed
First used: 2025-05-02
US federal AI use case: Dark Web Threat Intelligence for Cyber Investigations
Agency: Department of Homeland Security - ICE
Problem addressed: This use case intends to solve the problem of quickly identifying and summarizing relevant cyber threat data from the dark web, which can be difficult and time-consuming for analysts.
Purpose and benefits: The summarized information helps analysts quickly identify threat actors, trends, and illicit platforms, enabling them to prioritize their investigative efforts. The data analysis and extraction techniques connect related information across data holdings and generate metadata to help analysts review and search results. The translation capability helps analysts identify non-English data responsive to an investigation and saves time otherwise spent translating non-responsive data.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: This use case provides a more efficient way to process, review, summarize, translate, and analyze dark web data for use in HSI investigations. The AI output may be used to produce investigative insights, but the output itself is supporting information for HSI personnel to use for ease of review, for further analysis, and to produce leads. Insights derived from translated data allow investigators to identify the most relevant data that, if deemed critical for court proceedings, can be submitted to certified human translators for final review. Personnel may use investigative insights for law enforcement actions such as 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 any law enforcement action or decision. Any enforcement decisions related to these insights are outcomes of the full Federal Investigation Process involving verifying any insights as evidence (including validating AI-translated material by a certified interpreter), presentation to a U.S. Attorney’s Office and potentially a District Court judge, decision to prosecute, judicial review, and trial and sentencing.
First used: 2024-08-01
US federal AI use case: Geographic Current Events Real-Time Alerting Tool
Agency: Department of Homeland Security - TSA
Problem addressed: TSA is able to analyze and process large volumes of data to evaluate critical events faster and more effectively.
Purpose and benefits: To provide real-time, actionable information by leveraging advanced artificial intelligence (AI) and machine learning algorithms to aggregate and summarize large amounts of publicly available data from social media, news, and other sources. The benefit of this product is that it produces real-time alerts based on a geographic location to include, predictive insights, detection of early signals of significant events, trends, or crises.
Topic area: Transportation
Development stage: Deployed
First used: 2025-01-01