DHS

The BICE AI system simplifies documentation and planning tasks for real estate professionals by generating several valuable outputs.

Save worker time by providing an alternative to lengthy bitwise forensic device inspections through the application of an expedient triage tool; reduce the number of higher-level inspections.

The chatbot is intended to solve inefficiencies in FEMA's grants application review process by streamlining access to policy information, reducing the time and effort required for manual research, and simplifying the...

How AI helps: Reads every format: Scans and reads PDFs, Word files, text, and images so all resumes can be processed; Extracts key details: Pulls out skills, certifications, education, locations, roles, dates, and yea...

The AI model/system may generate outputs such as:_x000D_ • Fraud Risk Scores: Quantitative assessments of fraud likelihood for transactions, applications, or entities._x000D_ • Anomaly Alerts: Notifications of unusual...

The purpose of using AI to generate test data for trade partners is to create more realistic data for trade partners to use for testing their systems before releasing new ACE capabilities.

Computer Vision for Aerial Detection of Land and Open Water Items of Interest

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...

Cyber deception is used alongside other cybersecurity measures to enhance overall security posture.

The AI uses a large language model to generate proposed descriptions for field names in our data catalog.

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.

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...

Leverages Azure Commercial OpenAI within the FEMA system boundary, currently leveraging ChatGPT4o.

This use case delivers improved internal government tools for reverse engineering of malware and speeding the development of cyber threat intelligence that can be shared across the government and with CISA partners.

An interface is provided for pre-publication documents to be uploaded.

Sensors and CUAS capabilities to support significant events.

The tool uses generative artificial intelligence (GenAI) powered by large language models (LLMs) and coding foundation models to assist with software development.

The AI's intended purpose is to solve the challenge of efficiently monitoring typically unoccupied or restricted environments for unauthorized human or vehicle presence.

The AI will output a potential product classification and sub-classification that would best fit the product within the TRM and BC.

CounselAI will allow OCC to be more efficient and effective.

This use case is designed to solve these challenges by reducing inconsistency in manual research, improving access to historical knowledge, and helping staff quickly identify relevant recovery resources.

This AI use case addresses three inter-related problems: It eliminates delays associated with human translation of documents originally in non-English languages; it reduces the cost of translation from approximately $...

Our government developers lack the capacity and consistency to produce quality code in a timely manner.

Expedited continuous scraping of data from identified official and unofficial open sources and comparison against pre-defined critical information requirements with summarization and sourcing of information for review...

The system improves the screening efficiency and accuracy of contraband detection in international express consignment and mail inspection.

The AI system processes data from radar, infrared sensors, and video surveillance to detect and track suspicious activities along U.S.

User input is categorized and captured in Salesforce to refine the chatbot's interpretation of future inputs.

Images and data of baggage inspection.

Budget Exhibits, Passback Materials, Hearing Testimony, Questions Received, Answers Provided Travel Policy Documents (Joint Travel Regulation (JTR), DHS Travel Policy, FEMA Travel Policy) Fiscal Policy Documents - Tre...

Training data included all HMA policy, training, and data available on FEMA.gov.

In support of production performance metrics for device health purposes, LBI uses both license plate and RFID read metrics to evaluate device health states.

Vetting/Border Crossing Information/ Trusted Traveler Information

Numerical data from RPM radiation detectors and ERNIE assessments.

Information stored within the Consular Consolidated Database.

Recorded Future AI is trained on over 10 years of threat analysis from Insikt Group, the company's threat research division, and is combined with the insights of the Recorded Future Intelligence Graph.

X-ray images and associated metadata.

Babel uses proprietary data, public datasets, and machine-labeled datasets to train its NLP and matching models.

All data used in training, validation, test and evaluation of the AI is Thruvision proprietary - no data from any external sources (including the Agency) is used.

This model leverages data housed within the Automated Targeting System (ATS) Unified Passenger (UPAX).

This model leverages data provided by carriers within the Automated Commercial Environment (ACE), as well as transformations of that data within the Automated Targeting System (ATS).

This model leverages data provided by carriers within the Automated Commercial Environment (ACE), as well as transformations of that data within the Automated Targeting System (ATS).

Border Crossing Information.

Trusted Traveler Information.

Border Crossing Information.

Vetting/Border Crossing Information/ Trusted Traveler Information

Border Crossing Information.

Training data was collected from several publicly available, social media, and media outlet sites.

Training data was collected from several publicly available, social media, and media outlet sites.

CBP Link submission information.

Border Crossing Information

X-ray images and associated metadata.

This model leverages data provided by carriers within the Automated Commercial Environment (ACE), as well as transformations of that data within the Automated Targeting System (ATS).

CBP is seeking Anomaly Detection Algorithm (ADA) models capable of operating on CBP systems to enable rapid screening of commercially owned vehicles (CoVs).

CBP is seeking Anomaly Detection Algorithm (ADA) models capable of operating on CBP systems to enable rapid screening of passenger and cargo vehicles.

The AI will analyze images from the sensors to determine what the item is in the image (e.g.

Proprietary, public, and machine labeled datasets including structured and unstructured data such as online ads, research datasets, images, and geospatial information.

Current data used is pre-publication content that has already been approved.

Publicly available position descriptions (primarily from the DoD), OPM standards and guidelines (e.g., the OPM classifier's handbook), and CBP position descriptions.

Previous production traveler questions that were sanitized and anonymized, mock traveler questions.

Sample documents were provided and executive summary was reviewed for relevance/accuracy.

FEMA: Historical Declaration and Public Assistance activity data_x000D_ U.S.

Cybersecurity cloud, network and host logs; Cybersecurity threat intelligence (CTI)

Cybersecurity indicators of compromise (IOCs), Cybersecurity threat intelligence (CTI)

ATAP relies on CBP source system information from CBP's ACE, ATS, and SEACATS systems, including import/export filing information, compliance reviews, targeting, seizure, and fine/penalty information.

The platform uses a supervised machine learning model trained on baseline non-governmental data, which is regularly updated and tested for accuracy.

Live flight testing data of the platform in test and operational environments.

Programming languages were used to test and fine tune the model, such as JAVA Script, COBOL, SQL, Python.

Internal CBP document samples are used to test the efficacy and accuracy of performance of chatCBP.

Initial training was conducted by the vendor using vendor obtained audio.

The commercial LLMs used for this use case were trained using a diverse range of publicly available data, including text from books, articles, websites, and other sources and data types.

The FEMA model is trained, fine-tuned, and evaluated using comprehensive datasets of historical grant management records, including subaward closeout documentation, financial reconciliation data, and management cost l...

This model utilizes metadata documentation provided by datastewards, known as the Source System Intake Form (SSIF), during source system intake for FEMADex.

CBP images of open water ways for vessels.

Data is provided from a surveillance system in the form of real-time messages on detected tracks within the system.

This model leverages data provided by carriers within the Automated Commercial Environment (ACE), as well as transformations of that data within the Automated Targeting System (ATS).

The datasets that the system uses are GOTS and LES.

The models are trained using examples of translated sentences and documents, which are typically collected from the public web.

Border Crossing Information.

PDRI trains its AI models for assessments by combining expert human ratings with robust data, using seasoned raters to score responses first, then training AI on these expert-validated examples, and continuously testi...

Meta-data and data created by CBP.

Border Crossing Information

This model leverages data housed within the Automated Targeting System (ATS) Unified Passenger (UPAX).

This model leverages data provided by air carriers within the Advance Passenger Information System (APIS) and Electronic System for Travel Authorization (ESTA).

This model leverages data provided by carriers within the Automated Commercial Environment (ACE), as well as transformations of that data within the Automated Targeting System (ATS).

All of the image data fed to the models are owned by USBP.

Training data was collected from several publicly available, social media, and media outlet sites.

Altana utilizes a combination of commercial, public, and proprietary data sources to build a searchable and traversable graph of global trade.