Mobile Device Forensics for Investigations

Overview

Development stage

In development or acquisitionSource: a) Pre-deployment – The use case is in a development or acquisition status.

High-impact designation

Presumed high-impact, determined notSource: b) Presumed high-impact, but determined not high impact

Impact justification

This use case falls into a presumptive high-impact category under M-25-21 Section 6 (j) due to its role in supporting law enforcement activities, specifically application of digital forensic techniques. However, it does not meet the definition of high-impact AI as outlined in OMB Memorandum M-25-21 because its outputs do not serve as a "principal basis" for decisions or actions with legal, material, binding, or significant effects on an individual or entity's civil rights, civil liberties, or privacy; or human health and safety. The AI capabilities, including classification, decoding, and origin analysis, are designed to assist analysts in prioritizing human review rather than independently determining outcomes. For example, AI-generated tags and app artifacts are suggestions that require manual validation and further investigation before enforcement actions are taken. All outputs are reviewed as part of a broader investigative process before any actions are taken.

Start date

Not reported

Withheld from public reporting

Not reported

Mission

Topic area

Law Enforcement

Operational functions

No function tag matched Derived by keyword rules; see methodology

Problem the AI is intended to solve

The AI is intended to solve the problem of analysts having to manually organize, decode, and review large volumes of complex mobile device data, which makes it difficult to quickly identify information relevant to an investigation.

Expected benefits

AI-generated category tags, app artifacts, and origin classifications allow analysts to more effectively prioritize human review of mobile device data that may be responsive to the investigation.

System outputs

The platform’s AI outputs include: (1) AI‑suggested category tags for extracted media (videos and images) and apps, based on user‑selected categories (e.g., media: cars, drugs, weapons; apps: chat, spoofing, cryptocurrency);(ii) AI‑decoded app data artifacts, such as chats, contacts, and locations; and (iii) AI predictions about whether a media file was captured on the extracted device or obtained from another source.

Technology

AI classification

Computer visionSource: Computer Vision: AI that processes and interprets visual data (e.g., images and videos).

System name(s)

Not reported

Custom-developed code

Not reported

Public source code

Not reported

Sourcing

How it was built

Not reported

Vendor (as reported)

Not reported

Vendors (standardized)

None on the standard list

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

Data and privacy

Involves PII

Not reported

Privacy Impact Assessment

Not reported

Authorization to Operate

Not reported

Demographic features

Not reported

Training and evaluation data

Not reported

Federal Data Catalog

Not reported

Governance

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

Potential impacts and how they were identified

Not reported