DHS Management Directorate – AI Use Cases

Use Case Name: Spending Analysis and Budget Execution Risk (SABER) Model 

Use Case ID: DHS-418 

Use Case Summary: This model analyzes historic Treasury Information Executive Repository (TIER) data reported under the DATA Act to identify past spending trends for Treasury Account Symbols (TAS), object classes, Programs, Projects, Activities (PPA), and Disaster Emergency Fund Codes (DEFC).  It predicts accounts at risk of being severely under-spent or overspent. The outputs include the degree to which current spending patterns match prior trends for all accounts, the likelihood that spending will not reach budgeted amounts, and accounts at risk of reaching budgetary ceilings too quickly. 

Use Case Topic Area: Mission-Enabling (internal agency support) 

Deployment Status: Pre-deployment (Acquisition and/or Development)  

Safety- and/or rights-impacting? No 

Face Recognition/Face Capture (FR/FC)? No 

Use Case Name: AdaptiveMFA 

Use Case ID: DHS-419 

Use Case Summary: Adaptive Multi-Factor Authentication (AMFA) will collect DHS user behavior data to better understand how users’ access DHSAuthPortal applications. By considering the authentication context data during the authentication process, AMFA introduces additional intelligence into identity flows. Using this data, DHS can detect anomalies and adapt security and authentication policies to enhance the security to DHS systems. The data will be visible to the Department's centralized Security Information and Event Management (SIEM), which can be used for larger correlation with other identity systems.  

The outputs are risk ratings HIGH, MEDIUM, and LOW for each authentication attempt, which can be configured to require stricter access control policies. 

Use Case Topic Area: Mission-Enabling (internal agency support) 

Deployment Status: Pre-deployment (Acquisition and/or Development)  

Safety- and/or rights-impacting? No 

Face Recognition/Face Capture (FR/FC)? No 

Use Case Name: JES and Appropriations Insight 

Use Case ID: DHS-419 

Use Case Summary: This system for internal financial efficiency converts scanned financial tables from PDFs into structured, machine-readable data while maintaining multi-year spending relationships. It processes scanned PDF documents containing financial tables to extract and reconstruct funding tables. The system employs computer vision techniques to identify and extract relevant tabular data from the scanned images. Natural Language Processing (NLP) and Large Language Models (LLMs) are then utilized to analyze the extracted text and numerical data, identifying, and linking related multi-year spending amounts across different tables and sections of the document. The output includes a comprehensive, structured funding table that consolidates the multi-year spending information, enabling users to quickly access and analyze the financial data spanning multiple years. This automated approach streamlines the process of gathering and organizing financial information from scanned PDF documents, reducing manual effort, and improving the efficiency of financial analysis tasks. 

Use Case Topic Area: Mission-Enabling (internal agency support) 

Deployment Status: Pre-deployment (Acquisition and/or Development) 

Safety- and/or rights-impacting? No 

Face Recognition/Face Capture (FR/FC)? No 

Use Case Name: CFO Companion 

Use Case ID: DHS-2343 

Use Case Summary: This project develops an internal chatbot that assists authorized DHS personnel in accessing and understanding DHS Chief Financial Officer (CFO) data. The chatbot uses advanced natural language processing (NLP) to interpret and respond to user queries related to DHS financial matters. Its knowledge base is built upon the DHS CFO publicly available data set, including financial reports, budgets, and expenditures. Authorized users interact with the chatbot through a secure interface, asking questions about DHS financial operations. The chatbot analyzes the user's input, identifies key financial terms, retrieves relevant information from the DHS CFO data set, and generates human-like responses using NLP. This specialized internal chatbot provides authorized personnel with an intuitive, conversational interface to query and analyze DHS CFO financial data and reports. It democratizes access to financial information, reduces the time spent searching through documents, and enables quick self-service analytics without requiring specialized database knowledge. 

Use Case Topic Area: Mission-Enabling (internal agency support) 

Deployment Status: Pre-deployment (Acquisition and/or Development) 

Safety- and/or rights-impacting? No.  

Face Recognition/Face Capture (FR/FC)? No

Use Case Name: LIGER Generative AI Toolkit

Use Case ID: DHS-2454

Use Case Summary: FPS is initiating a pilot program to incorporate Generative AI (GenAI) into daily operations with the LIGER GenAI Toolkit. LIGER uses Natural Language Processing (NLP) and an embedded Large Language Model for text generation; LIGER employs a Retrieval-Augmented Generation (RAG) architecture to retrieve information used in the response from user-selected documents from a document ""collection"" to which the user has access. To enable use of Controlled Unclassified Information (CUI), LIGER™ employs document “collections” which are only available to the user who uploaded the document and other users to whom the original owner has granted access. LIGER™ will enable FPS users to employ GenAI against an authoritative, custom dataset to generate text for a wide range of purposes in a variety of styles.

Approved applications of LIGER™ for FPS include the following: Initial draft document generation to include: Position Descriptions (PD), Statement of Work (SOW) for contracting actions, Professional emails and workforce announcements, Public Affairs stories and releases, Law Enforcement operations orders; Summarization; Proofreading and providing feedback or suggestions on written work; Policy analysis to include: Policy comparison and compliance verification, Building textual process maps based on policy, 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; Information/data retrieval from document libraries.

Use Case Topic Area: Mission-Enabling (internal agency support) 

Deployment Status: Pre-deployment (Initiation)

Safety- and/or rights-impacting? No, use case is too new to fully assess impacts; will be reassessed before end of initiation stage. 

Face Recognition/Face Capture (FR/FC)? No 

Use Case Name: Text Analytics for Survey Responses (TASR) 

Use Case ID: DHS-45 

Use Case Summary: Text Analytics for Survey Responses (TASR) is an application for performing Natural Language Processing (NLP) and text analytics on internal staff survey responses. It is currently being applied by DHS Office of the Chief Human Capital Officer (OCHCO) to analyze and extract significant topics/themes from unstructured text responses to open-ended questions in the quarterly DHS Pulse Surveys. Results of extracted topics/themes are provided to DHS Leadership to better inform agency-wide efforts to meet employees’ basic needs and improve job satisfaction. 

Use Case Topic Area: Mission-Enabling (internal agency support) 

Deployment Status: Deployed (Operation and Maintenance) 

Safety- and/or rights-impacting? No 

Face Recognition/Face Capture (FR/FC)? No 

Use Case Name: DHSChat 

Use Case ID: DHS-2433 

Use Case Summary: This is a chatbot based on a Large Language Model (LLM) for internal DHS employee use with non-classified but internal information, including For Official Use Only (FOUO) and Controlled Unclassified Information (CUI), due to its improved security compared to publicly available chatbots. DHS employees are permitted to use an internally available generative AI for text generation in day-to-day work leveraging internal documents. This tool can dynamically create written content through text prompts submitted by the user. It uses Natural Language Processing (NLP) and Large Language Models (LLMs) to produce natural sounding language in a wide variety of contexts and styles. 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. 

Use Case Topic Area: Mission-Enabling (internal agency support) 

Deployment Status: Deployed (Operation and Maintenance) 

Safety- and/or rights-impacting? No 

Face Recognition/Face Capture (FR/FC)? No 

Use Case Name: User and Entity Behavior Analytics (UEBA) 

Use Case ID: DHS-2434 

Use Case Summary:  As part of the personnel security vetting process for continuous vetting-eligible employees, employees provide information and authorize access to information about themselves, including criminal history, credit reports, and other information. This information is collected and collated on an ongoing basis. UEAB presents aggregated information in a structured manner to help prioritize staffing.  Additionally, pursuant to and as authorized by Executive Order 13467, “Reforming Processes Related to Suitability for Government Employment, Fitness for Contractor Employees, and Eligibility for Access to Classified National Security Information,” employees must be enrolled in a continuous vetting program.   

UEBA enhances the efficiency, accuracy, and effectiveness of personnel security processes by aggregating information provided by or authorized to be collected by an employee about the employee.   

The AI output is the collated information is a risk matrix for human review. The AI output uses a hard coded scoring system to provide alerts for collated information that presents greater risk in the vetting process.  Staff reviews the risk matrix and uses the collated information in the matrix as background information for the personnel security vetting process. The collated information supports decisions about staffing priorities. 

Use Case Topic Area: Mission-Enabling (internal agency support) 

Deployment Status: Deployed (Operation and Maintenance) 

Safety- and/or rights-impacting? No.  It was presumed rights-impacting relating to hiring and employment, but the DHS Chief AI Officer determined this use case does not satisfy the definition of rights-impacting AI in M-24-10.  This use case collates information provided by or authorized to be collected by an employee as part of the personnel security vetting process.  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.   

Read more about safety and/or rights-impacting AI. 

Face Recognition/Face Capture (FR/FC)? No 

Use Case Name: ESEC Inquiry (STORM) Summarization 

Use Case ID: DHS-2453 

Use Case Summary: The Executive Secretariat (ESEC) receives a substantial number of inquiries from members of Congress and the Senate seeking information on various subjects. To enhance efficiency and streamline the process, 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. Upon receipt of correspondence, the Executive Secretariat (ESEC) initiates a work-package within the STORM - a sophisticated Microsoft Dynamics CRM system integrated with SharePoint for document storage. Each file uploaded to STORM is consequently stored in SharePoint, adhering to the organizational structure of the relevant work-package. When a user creates a new work package and uploads a letter, it activates a Power Apps workflow that creates a summary. The workflow is designed to process the incoming letter efficiently and accurately. 

Use Case Topic Area: Mission-Enabling (internal agency support) 

Deployment Status: Deployed (Operation and Maintenance) 

Safety- and/or rights-impacting? No 

Face Recognition/Face Capture (FR/FC)? No

Use Case Name: Sentiment Analysis and Topic Modeling (SenTop) 

Use Case ID: DHS-159 

Use Case Summary: The initial purpose of the Sentiment Analysis and Topic Modeling (SenTop) project was to analyze survey responses for DHS’s Office of the Chief Procurement Officer related to contracting. However, it has evolved to be a general-purpose text analytics solution that can be applied to any domain or area. It has also been tested and used for HR topics. SenTop is a DHS-developed Python package for performing descriptive text analytics, specifically, sentiment analysis and topic modeling on free-form, unstructured text. SenTop uses several methods for analyzing text, including combining sentiment analyses and topic modeling into a single capability, permitting identification of sentiments per topic and topics per sentiment. Other innovations include the use of polarity and emotion detection, fully automated topic modeling, and multi-model/multi-configuration analyses for automatic model/configuration selection. The code has been established, performs an analysis, and provides a report, but it is only accessed and run by one person per customer request. 

Deployment Status: Inactive (no longer used)