DOJ · DOJ-0103 · record R1555

Digital Forensics

Department of Justice · Department of Justice / Department Wide

Overview

Development stage: DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.
High-impact designation: High-impactSource: a) High-impact
Impact justification: Not reported
Start date: 2024-01 (month precision)Source: 01/2024
Withheld from public reporting: NoSource: a) No

Mission

Topic area: Law Enforcement
Operational functions: No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

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.

Expected 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.

System outputs

Outputs vary by use case.

Technology

AI classification: Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…
System name(s): Redacted for cybersecurity purposes.
Custom-developed code: No
Public source code: Not reported

Sourcing

How it was built: Purchased from vendorSource: a) Purchased from a vendor
Vendor (as reported): These tools include, for example, Cellebrite, Magnet Axiom and Griffeye
Vendors (standardized): Cellebrite

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII: Yes
Privacy Impact Assessment: Not reported
Authorization to Operate: Yes
Demographic features: This AI use case utilizes one or more of the demographic variables listed in compliance with all federal laws and agency regulations.
Training and evaluation data: The case owner relied on DOJ AI governance practices to select and prepare data, as well as evaluate performance.
Federal Data Catalog: Not reported

Governance

8 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Pre-deployment testingReported in progressSource: b) In-progress

AI impact assessmentReported in progressSource: b) In-progress

Independent reviewReported in progressSource: d) In-progress

Ongoing monitoringReported in progressSource: b) Development of monitoring protocols is in-progress

Operator trainingReported in progressSource: b) Establishment of sufficient and periodic training is in-progress

Fail-safeReported in progressSource: c) In-progress

Appeal processReported in progressSource: c) Establishment of an appropriate appeal process is in-progress

User and public consultationReported in progressSource: e) In-progress

Potential impacts and how they were identified

Consistent with Executive Orders and OMB guidance, the case owner relied on DOJ AI governance practices to evaluate impacts and risks.