GreatNorthCollective/greatnorth-us-federal-ai-inventory huggingface dataset:
US federal AI use case: Veritone Redact
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in Veritone Redact is to support ATF in processing Freedom of Information Act (FOIA) requests by automating the redaction of sensitive information within audio and video files.
Purpose and benefits: The expected benefits of the AI use case in Veritone Redact involve enhancing the efficiency and accuracy of redacting sensitive information within audio and video files. By automating the identification and redaction of personally identifiable information and other confidential content, the AI reduces the time and effort required from staff to process multimedia materials. This leads to faster turnaround times for releasing information, thereby reducing customer wait times for FOIA requests. The AI-driven redaction process also helps ensure compliance with privacy laws and regulations, minimizing the risk of inadvertently disclosing sensitive information. These efficiencies result in cost savings through reduced labor hours and improved resource allocation. Overall, the AI in Veritone Redact is expected to improve operational efficiency, enhance compliance, and support timely access to information for the public.
Topic area: Admin Functions
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-11-01
US federal AI use case: Whooster
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in Whooster is to integrate and analyze data from multiple sources to build comprehensive profiles on persons or entities of interest. AI-driven entity resolution, relationship mapping, and data enrichment help automate what would otherwise be manual, time-consuming cross-referencing. This improves the speed and accuracy of identifying relevant connections, ensures more targeted follow-up, and optimizes the allocation of investigative resources.
Purpose and benefits: The expected benefit is more effective use of time, focusing on particularly relevant subjects.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Commercial LPR (via HIDTA partners)
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: This capability is not provided or managed by ATF. It provides ATF staff access to Flock license plate reader systems which are provided and managed by High Intensity Drug Trafficking Area (HIDTA) partner agencies.
Purpose and benefits: License plate reader systems are used by law enforcement to assist with identification of vehicles associated with criminal investigations. ATF has no role in training or managing AI capabilities which are incidental to providing the service.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: ELSAG/Leonardo (via HIDTA partners)
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: This capability is not provided or managed by ATF. It provides ATF staff access to ELSAG license plate reader systems which are provided and managed by High Intensity Drug Trafficking Area (HIDTA) partner agencies.
Purpose and benefits: License plate reader systems are used by law enforcement to assist with identification of vehicles associated with criminal investigations. ATF has no role in training or managing AI capabilities which are incidental to providing the service.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Thomson Reuters Vigilant Vehicle Manager
Agency: Department of Justice - Department of Justice / ATF
Problem addressed: The purpose of the AI use case in Thomson Reuters Vigilant Vehicle Manager is to intelligently organize, correlate, and analyze large volumes of license plate and vehicle sightings data. By automating the identification of recurring patterns and providing data-driven insights, it streamlines the workflow for identifying vehicles that may warrant attention. The AI capabilities reduce time-consuming manual reviews, improve accuracy in spotting significant trends, and help allocate investigative resources more effectively for timely and well-informed actions.
Purpose and benefits: License plate reader systems are used by law enforcement to assist with identification of vehicles associated with criminal investigations. ATF has no role in training or managing AI capabilities which are incidental to providing the service.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2020-09-01
US federal AI use case: Veritone Illuminate
Agency: Department of Justice - Department of Justice / ATR
Problem addressed: Veritone Illuminate is a cloud-based application that provides machine translation and audio and video analysis transcription to aid human review in investigations. The product allows us to leverage artificial intelligence (AI) to systematically turn unstructured data (audio and video files) into structured data. The structured data can be easily searchable and provide more value to our cases.
Purpose and benefits: Converts audio and video files to text searchable formats, and quickly translates multiple native languages into English-based text for more efficient review.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2023-01-01
US federal AI use case: SAS (Forecasting, predictive analytics, data/statistical analysis)
Agency: Department of Justice - Department of Justice / ATR
Problem addressed: Forecasting market conditions using various data sources for economic analyses.
Purpose and benefits: Provides ATR with the ability to analyze, summarize, synthesize, and manage various data sources and data formats to support economic and statistical analysis of corporation and market conditions. ATR relies heavily on data analytical tools to support micro - and macro-level analysis.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: R (Forecasting, predictive analytics, data/statistical analysis)
Agency: Department of Justice - Department of Justice / ATR
Problem addressed: Forecasting market conditions using various data sources for economic analyses.
Purpose and benefits: Provides ATR with the ability to analyze, summarize, synthesize, and manage various data sources and data formats to support economic and statistical analysis of corporation and market conditions. ATR relies heavily on data analytical tools to support micro - and macro-level analysis.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Stata (Forecasting, predictive analytics, data/statistical analysis)
Agency: Department of Justice - Department of Justice / ATR
Problem addressed: Forecasting market conditions using various data sources for economic analyses.
Purpose and benefits: Provides ATR with the ability to analyze, summarize, synthesize, and manage various data sources and data formats to support economic and statistical analysis of corporation and market conditions. ATR relies heavily on data analytical tools to support micro - and macro-level analysis.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Matlab (Forecasting, predictive analytics, data/statistical analysis)
Agency: Department of Justice - Department of Justice / ATR
Problem addressed: Forecasting market conditions using various data sources for economic analyses.
Purpose and benefits: Provides ATR with the ability to analyze, summarize, synthesize, and manage various data sources and data formats to support economic and statistical analysis of corporation and market conditions. ATR relies heavily on data analytical tools to support micro - and macro-level analysis.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Databricks (Forecasting, predictive analytics, data/statistical analysis)
Agency: Department of Justice - Department of Justice / ATR
Problem addressed: Client-side and SaaS products that contains fundamental modeling, AI and machine learning techniques for predictive modeling, natural language processing, computer vision and deep learning. Design and develop sophisticated economic models to analyze markets.
Purpose and benefits: Provides ATR with the ability to analyze, summarize, synthesize, and manage various data sources and data formats to support economic and statistical analysis of corporation and market conditions. ATR relies heavily on data analytical tools to support micro - and macro-level analysis.
Topic area: Law Enforcement
Development stage: Pilot
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2023-04-01
US federal AI use case: Salesforce
Agency: Department of Justice - Department of Justice / CIV
Problem addressed: This initiative modernizes our current on-premise and matter management databases and storage systems by migrating to cloud-based platforms specifically architected for AI integration and enhanced system interoperability. Our existing applications and data are currently hosted on-premise systems that create fundamental barriers to AI implementation, limiting data accessibility, constraining scalability, and preventing the real-time data integration that modern AI tools require.
Purpose and benefits: (1) Creates a replicable model for DOJ-wide adoption by establishing standardized cloud architecture and data integration frameworks that can be scaled across components. Demonstrates how breaking down legacy data silos enables coordinated, AI-driven insights and business intelligence capabilities that support department-wide resource allocation and investigative priorities.                                                                                                                                                                                                                                                                     (2) Leverages JMD's established AWS Landing Zone to accelerate cloud adoption and builds upon existing data governance frameworks and system modernization efforts across DOJ components. The migration integrates with current cloud-based systems through the AWS Landing Zone infrastructure, enabling AI-powered business intelligence capabilities without duplicating technology investments.                                                                                                                                                                                                                              (3) Completing the migration to AI-native cloud infrastructure and deploying an AI-driven business intelligence dashboard that enables Division leadership to explore patterns and trends across the litigation portfolio.
Topic area: IT
Development stage: Pilot
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2025-01-01
US federal AI use case: ChatBot
Agency: Department of Justice - Department of Justice / COPS
Problem addressed: The Chatbot responds to the questions, allowing staff to focus on other priorities.
Purpose and benefits: The Intelligent Bot tool is implemented using Question and Answer (QnA) Maker and the Language Understanding (LUIS) services which is developed using the Microsoft Azure Software As A Service (SAAS) infrastructure in the Cloud on the COPS website.  

This will allow COPS to easily implement a brand new knowledge base of questions and answers feature on the site which will respond via a chat box based on a question entered by the customer.
Topic area: IT
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2021-11-01
US federal AI use case: BMC Helix ITSM
Agency: Department of Justice - Department of Justice / CRM
Problem addressed: BMC Helix's ITSM AI capabilities include Proactive Problem Management and Incident Correlation, which will allow CRM's IT Service Desk to more efficiently identify issues, resolve incidents, automate case routing, and perform root cause analysis.
Purpose and benefits: Increase IT service desk productivity, prevent issues before they occur, and decrease user downtime.
Topic area: IT
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2025-04-01
US federal AI use case: Thomson Reuters CLEAR - License Plate Recognition
Agency: Department of Justice - Department of Justice / CRM
Problem addressed: CLEAR LPR enables investigators to identify location history for license plates, connect addresses and individuals of interest to a vehicle's location and obtain images of a vehicle. CRM uses CLEAR LPR to aid in investigations.
Purpose and benefits: Streamline investigations, reducing the need to search multiple platforms, while saving costs and allowing investigators to more quickly identify relevant information.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Veritone
Agency: Department of Justice - Department of Justice / CRM
Problem addressed: Improve transcription and translation of written, video, and audio files
Purpose and benefits: Transcribe audio and video, saving manual review time and associated costs.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: AWS/cloud.gov -  Network Routing
Agency: Department of Justice - Department of Justice / CRT
Problem addressed: Support public complaint submission by enabling network routing technology to optimize speed of routing and re-route traffic around any configuration issues.
Purpose and benefits: Expected benefits: Helps sustain reliable networks access to CRT networked infrastructure by CRT staff and by Public users for complaint submissions.
Topic area: IT
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2019-01-01
US federal AI use case: Azure Platform/Tools - Network Routing
Agency: Department of Justice - Department of Justice / CRT
Problem addressed: Decision and action relating to network routing and load management.
Purpose and benefits: Expected benefits: Helps sustain reliable networks access to CRT networked infrastructure by CRT staff.
Topic area: IT
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2019-01-01
US federal AI use case: Camtasia
Agency: Department of Justice - Department of Justice / CRT
Problem addressed: Dynamic captions use AI tech within the local client software to convert speech to text and produce a transcript file.
Purpose and benefits: The captioning is used based on user preference within their client app.  The producing and retaining a transcript is disabled by CRT IT Policy.
Topic area: IT
Development stage: Pre-deployment
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Cloudflare Turnstile
Agency: Department of Justice - Department of Justice / CRT
Problem addressed: Prevents bots from automatically submitting spam complaints through public web portal.
Purpose and benefits: This should reduce the time required for reviewing reports submitted by the public, thus increasing efficiency in the report review process and improving public service.
Topic area: IT
Development stage: Deployed
Risk category: High-impact
First used: 2024-07-01
US federal AI use case: Dragon
Agency: Department of Justice - Department of Justice / CRT
Problem addressed: Use natural language processing to convert spoken audio to text for employees with vision or mobility limitations.
Purpose and benefits: Increased accessibility. Employees with a disability are provided a reasonable accommodation.
Topic area: IT
Development stage: Deployed
First used: 2016-09-01
US federal AI use case: Evidence.com
Agency: Department of Justice - Department of Justice / CRT
Problem addressed: Use the auto-transcribe feature to transcribe body worn camera footage to text making it more easily searchable.   Also use the redaction assistant feature to remove sensitive information from videos.
Purpose and benefits: Improved speed of pre-processing workflows, faster identification of relevant audio, efficiency of investigations.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: High-impact
US federal AI use case: DEA Drug Signature Program Models
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: Understanding the manufacturing origin and distribution route of illicit drugs by analyzing the chemical composition of seized drugs is a core DEA task. The purpose of this use case is to use AI/ML techniques to develop, maintain, and improve models that enable designated forensic chemists to identify a notional geographic origin or notional manufacturing route of samples selected for DEA's Drug Signature Programs.
Purpose and benefits: The solution supports designated forensic chemists at DEA to automate analyses and to more quickly identify trend changes regarding drug sample notional geographic origin or notional manufacturing route.
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 1998-01-01
US federal AI use case: LPR: DEA DEASIL Program
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA needs an efficient and effective way to identify and track the movements of persons of interest based on vehicle license plates.
Purpose and benefits: License Plate Readers (LPR) can be one important investigative tool to support cases.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2012-01-01
US federal AI use case: LPR: State Partner
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: License Plate Readers (LPR) can be one important investigative tool to support understanding of drug markets, manufacturing, and distribution channels.
Purpose and benefits: License Plate Readers (LPR) can be one important investigative tool to support cases.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: LPR: Federal Partner
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA needs an efficient and effective way to identify and track the movements of persons of interest based on vehicle license plates.
Purpose and benefits: License Plate Readers (LPR) can be one important investigative tool to support cases.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: LPR: Commercial Solution
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: License Plate Readers (LPR) can be one important investigative tool to support understanding of drug markets, manufacturing, and distribution channels.
Purpose and benefits: License Plate Readers (LPR) can be one important investigative tool to support cases.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Friction Ridge Print Comparisons
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA, when conducting fingerprint analysis to identify individuals who may be connected to evidence, needs to be able to compare friction ridge prints to other prints within the boundaries of a case. Product enables linking of cases where individuals are not necessarily identified.
Purpose and benefits: This use case saves time and provides information for human decision-making.
Topic area: Law Enforcement
Development stage: Pilot
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Automated Count of Items in Photos
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA needs a way to validate the drug forms, shapes, sizes, and counts contained in drug seizure exhibits so that this information can be effectively used in court proceedings.
Purpose and benefits: To ensure timely high confidence of counts, this use case allows forensic scientists to accelerate validation and enables quality control checks by serving as a unbiased count against submitted paperwork and manual counts. This will reduce a labor and resource intensive counting process.
Topic area: Law Enforcement
Development stage: Pre-deployment
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Body Worn Camera (BWC) Audio-Video Software AI Tools
Agency: Department of Justice - Department of Justice / DEA
Development stage: Retired
Risk category: High-impact
US federal AI use case: Supply Chain Analytics
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA has a mission to protect communities and save lives. It requires an understanding of global drug markets, manufacturing, and distribution channels as well as the impact of illicit drugs on communities and individuals. Supply chain analytics is a tool to further open and active investigations and protect the American public.
Purpose and benefits: Supply chain information about goods associated with drug manufacturing and trafficking can further investigative leads and allow dedicated DEA personnel to track global trends, determine the impact of market forces, and understand import/export stakeholders. This is particularly critical for DEA's work on precursor chemicals used in the production of synthetic drug like fentanyl.
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2022-01-01
US federal AI use case: Data & Analytics: Database Tools
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA needs a way to search, monitor, and analyze a wide variety of public-facing unstructured and structured data sources such as scientific literature, social media, dark web, news, public records, public internet forums, and more in furtherance of investigations.
Purpose and benefits: This use case enables DEA to gain key insights at exponentially higher speeds with lower costs by fusing variety of data sources. Benefits include: operational insights, emerging threat detection, agent safety, public safety, improved mission-enabling services, etc.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Data & Analytics: Healthcare Fraud Detection
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA needs a way to facilitate analysis of health care fraud and abuse, especially those impacting government health programs. Federal government healthcare insurance programs include Medicare, Medicaid, TRICARE, VA, and others. The Health Care Fraud and Abuse Control Program (HCFAC) was created to unite DOJ and HHS in their efforts to combat fraud.
Purpose and benefits: Enhances the detection and prevention of health care fraud and abuse crimes within the context of DEA's mission.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Data & Analytics: Threat and Security Incident Monitoring
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA wants to improve its real-time collaboration, executive protection, and agent safety with data-driven situational awareness, and identify potential threats using entity resolution to analyze multiple commercial and government data feeds.
Purpose and benefits: This use case enables DEA to gain key insights at exponentially higher speeds with lower costs by fusing variety of data sources on potential threats from both insiders and external actors and to assist internal monitoring of  multiple devices for real-time collaboration.  This use case provides entity resolution, taking information entered by DEA analysts and scanning data sources for likely matches with risk indicators, and returning those results to analysts for review.
Topic area: Law Enforcement
Development stage: Pilot
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: Data & Analytics: Transportation OCR
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA needs a reliable, accurate way to identify the cartel or other organization associated with a seized drug exhibit.
Purpose and benefits: AI capabilities can enable DEA to identify cartels or others organizations associated with drug seizures. This could improve DEA's understanding of drug trends.
Topic area: Law Enforcement
Development stage: Pre-deployment
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Data & Analytics: Chemistry Instrument Library
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA needs a way to identify unknown drug samples, which can be done by comparing them against a library of known spectra for the best match.
Purpose and benefits: For less commonly encountered compounds, this use case saves time and provides information for human decision-making. The  comparison/matching also fulfills requirements to provide spectra from known/traceable materials.
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 1990-01-01
US federal AI use case: Data & Analytics: Gunshot Detection System
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: Rapidly locate gunshots and activity of potential investigative interest to alert relevant law enforcement agencies.
Purpose and benefits: Rapid detection of gunshots, which can help to decrease the time to respond to a violent crime.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Financial and Cryptocurrency Analysis: Federal Partner
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: Intelligence Analysts and DEA agents require tools that enable them to accelerate triage, discovery, investigations, and reporting while preserving the context, control, and credibility necessary to make their intelligence actionable. These tools must also support the rapid processing of raw, primary source content to provide useful insights and facilitate the identification of patterns and relationships in financial transactions, including cryptocurrencies, to aid in drug trafficking and money laundering investigations.
Purpose and benefits: Saves time and money by freeing up Intelligence Analysts to focus on higher-order analysis, while enhancing the detection and prevention of financial crimes related to ongoing investigations.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Financial and Cryptocurrency Analysis: Commercial Solution
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: Intelligence Analysts and DEA agents require tools that enable them to accelerate triage, discovery, investigations, and reporting while preserving the context, control, and credibility necessary to make their intelligence actionable. These tools must also support the rapid processing of raw, primary source content to provide useful insights and facilitate the identification of patterns and relationships in financial transactions, including cryptocurrencies, to aid in drug trafficking and money laundering investigations.
Purpose and benefits: Saves time and money by freeing up Intelligence Analysts to focus on higher-order analysis, while enhancing the detection and prevention of financial crimes related to ongoing investigations.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Controlled Substances Act (CSA): Automation of Reports and Consolidated Orders System (ARCOS) Data Summarization
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA needs a way to automate the validation, summarization, and outlier identification of ARCOS data for further analysis.
Purpose and benefits: To save time and effort of fully manual review of ARCOS data.
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2023-01-01
US federal AI use case: Controlled Substances Act (CSA): Transaction Data Ranking
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA needs a way to rank CSA transaction data based on the factors identified/calculated as part of a manual analysis.
Purpose and benefits: To reduce time and effort of manually ranking.
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2022-01-01
US federal AI use case: Intelligence Data Platform
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA has a mission to protect communities and save lives. It requires a need to understand communications within criminal operations. Multi-language machine transcription of audio files from lawfully seized devices, authorized correctional facility, and other authorized communications with necessary English translation will filter massive audio files to relevant data for review.
Purpose and benefits: Expedites investigations as audio files can be easily searched and filtered to quickly identify which parts of the conversations should be reviewed and interpreted officially by human translators and analysts for discovery purposes.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2018-01-01
US federal AI use case: Call Center Management and Service Delivery Support
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA wants to improve the effectiveness of the Diversion Control Registrant Call Center by automating the routing of incoming calls, providing answers to common questions for Diversion Control Registrant call center agents to identify and quickly propose steps toward resolution, and monitoring customer feedback.
Purpose and benefits: To expedite responding to registrant needs with timeliness and consistency of DEA's customer service posture to DEA registrants. To decrease labor costs while maintaining high levels of customer service to DEA registrants. To capture needed improvements to service for implementation.
Topic area: Benefits Processing
Development stage: Pre-deployment
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Generative AI R&D Sandbox
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: The purpose of this use case is to create a R&D environment to enable DEA to test and prototype Generative AI based solutions.
Purpose and benefits: To provide a safe environment to prototype and test experimental AI based use cases.
Topic area: IT
Development stage: Pilot
Risk category: High-impact
First used: 2025-11-01
US federal AI use case: Instructional System Design Online Courses
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA has a mission to protect communities and save lives. The purpose of this use case is to generate images for use in training materials.
Purpose and benefits: To reduce the amount of time and resources needed to develop training aids and graphical inserts for online Computer Based Training (CBT).
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Autonomous Drone Detection and Monitoring
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA needs drones that can safely and effectively navigate autonomously
Purpose and benefits: This use case supports human drone operators and pilots in deploying drones by collecting data. It minimizes the need for costly, extensive training and allows drone operators to focus on real-time operational needs. It supports post-deployment data analysis.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: Nuclear Magnetic Resonance (NMR) Spectra Prediction
Agency: Department of Justice - Department of Justice / DEA
Problem addressed: DEA has a mission to protect communities and save lives. Phase and baseline corrections are important processing steps in the analysis of Nuclear Magnetic Resonance (NMR) spectra. Deep learning achieves excellent results in recognition and segmentation tasks, supporting users with spectra processing and interpretation.
Purpose and benefits: Fast processing and interpretation of nuclear magnetic resonance spectra.
Topic area: Law Enforcement
Development stage: Pre-deployment
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Automated IT Services and Application Monitoring
Agency: Department of Justice - Department of Justice / DEA
Development stage: Retired
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: BriefCatch
Agency: Department of Justice - Department of Justice / Department Wide
Problem addressed: Addresses errors found in manual legal writing.
Purpose and benefits: Aids attorneys with grammar and sentence structure to enable stronger written product.
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2020-10-01
US federal AI use case: Cybersecurity Defense Tools
Agency: Department of Justice - Department of Justice / Department Wide
Problem addressed: Addresses manual process of detecting unknown cybersecurity threats.
Purpose and benefits: Certain tools are used Department-wide to provide endpoint detection; threat intelligence, analysis, and response; and related services. 

These tools help the Department more quickly identify and respond to threats and indicators of compromise from systems.
Topic area: Cybersecurity
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: eLitigation Tools
Agency: Department of Justice - Department of Justice / Department Wide
Problem addressed: Addresses errors and speed delays found in exclusively manual human review of  voluminous electronic information.
Purpose and benefits: Department of Justice components use electronic litigation (“eLitigation”) tools for a broad range of purposes, including support of investigations, litigation, and FOIA and Privacy Act processes. Most of these tools are commercial-off-the-self products that are commonly used outside of government, such as Everlaw, FOIAXpress, and Relativity. These tools increasingly integrate AI capabilities that can assist with tasks core to the mission of the Department, such as surfacing potentially discoverable information in voluminous collections of emails, text messages, or other electronic records; locating potentially inculpatory or exculpatory evidence in voluminous electronic data; and identifying material that may be appropriate for disclosure or withholding according to applicable legal rules and privileges.

eLitigation tools can offer substantial benefits over exclusively human review of voluminous electronic information: they can be faster, more accurate and consistent, and more efficient. 

Please note: These tools are used in contexts that are high-impact, but the nature and details of AI uses vary, which may affect whether particular uses are high impact.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: Presumed high-impact (determined not high)
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2024-01-01
US federal AI use case: Digital Forensics
Agency: Department of Justice - Department of Justice / Department Wide
Problem addressed: Addresses key problems in processing digital data related to law enforcement including efficiency in processing digital evidence, automating time consuming tasks such as organizing and classifying data, and accuracy in evidence analysis.
Purpose and benefits: Forensic analysis tools used to extract, analyze, search, and organize digital evidence and datasets.

Increases the efficiency of extracting data from devices and of analyzing/searching for pertinent data within devices and datasets.
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2024-01-01
US federal AI use case: LexisNexis (AI assisted legal research)
Agency: Department of Justice - Department of Justice / Department Wide
Problem addressed: Addresses manual process of conducting legal research.
Purpose and benefits: Improves accuracy and efficiency of legal research.
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2020-01-01
US federal AI use case: Percipio Skillsoft
Agency: Department of Justice - Department of Justice / Department Wide
Problem addressed: Helps employees identify relevant training courses.
Purpose and benefits: Interactive tool customizes learning environments for the workforce's individual needs.
Topic area: HR
Development stage: Pre-deployment
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: ServiceNow
Agency: Department of Justice - Department of Justice / Department Wide
Problem addressed: ServiceNow automates IT helpdesk tickets triage and classification and can handle routine inquires and simple tasks to free up resources to focus on higher priority issues.
Purpose and benefits: To automate engagement with IT helpdesk personnel for IT requests. ServiceNow is a cloud-based platform that digitizes and automates workflows. It provides IT support to common and simple requests 24/7, and virtual agents handle common inquires that free staff for higher priority issues. Consistent processing could help reduce errors and increase efficiency.
Topic area: Service Delivery
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2019-09-01
US federal AI use case: Westlaw (AI assisted legal research)
Agency: Department of Justice - Department of Justice / Department Wide
Problem addressed: Addresses manual process of conducting legal research.
Purpose and benefits: Performs legal research and improves accuracy and efficiency of legal research.
Topic area: Law Enforcement
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2019-10-01
US federal AI use case: Parallel Search from CaseText
Agency: Department of Justice - Department of Justice / ENRD
Development stage: Retired
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Qualtrics
Agency: Department of Justice - Department of Justice / ENRD
Problem addressed: Qualtrics is a cloud-based service that can send out survey and record responses. ENRD uses it to send out questionnaires to its own employees to gather feedback on issues such as training needs and student feedback on training sessions. ENRD also uses Qualtrics to collect victim impact statements and to collect public input in environmental justice matters. Qualtrics includes a sentiment analysis feature, but ENRD has not used it. Sentiment analysis can review narrative responses from respondents and summarize the overall sentiment of a group of respondents and surface insights and issues from a large number of responses.
Purpose and benefits: It can provide immediate analysis of respondent sentiment and surface insights. It can reduce the number of hours and effort to read and score narrative responses and summarize the overall opinion on the subject.
Topic area: Admin Functions
Development stage: Pre-deployment
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: SimplyFile
Agency: Department of Justice - Department of Justice / ENRD
Problem addressed: SimplyFile uses AI to learn a user’s email filing habits and then predict where they will want to move each email by displaying a list of suggested filing locations they can select from. This allows users to file their emails with a single button click, rather than having to click and drag them to the correct folders.
Purpose and benefits: Faster and more accurate email filing
Topic area: Admin Functions
Development stage: Deployed
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
First used: 2022-09-01
US federal AI use case: Veritone
Agency: Department of Justice - Department of Justice / ENRD
Problem addressed: Improve transcription and translation of written, video, and audio files
Purpose and benefits: Faster screening of large volumes of electronic materials or information that may be of interest in an investigation or discovery.
Topic area: Energy and Environment
Development stage: Pre-deployment
Impact assessment: Does not produce an output that serves as a principal basis for decisions or actions with legal, material, binding, or significant effect on any of the individuals or entities identified in OMB-25-21.
US federal AI use case: Critical Mention
Agency: Department of Justice - Department of Justice / EOUSA
Problem addressed: Identification of  public data
Purpose and benefits: Reduction in time required to update
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-01-01
US federal AI use case: Evidence.com
Agency: Department of Justice - Department of Justice / EOUSA
Problem addressed: Inefficient and costly manual evidence review
Purpose and benefits: Cost Savings, reducing court preparation times
Topic area: Law Enforcement
Development stage: Deployed
Risk category: High-impact
First used: 2025-03-01
US federal AI use case: Flashpoint Ignite
Agency: Department of Justice - Department of Justice / EOUSA
Development stage: Retired
Risk category: High-impact