AI Powered Data Governance
Description
This initiative deploys AI-driven data governance and metadata management to auto-tag, catalog, and enforce retention, while identifying duplicate/low-value files. Aligns with M-25-21 governance & E.O. 14179 innovation.
Detailed example
Classifications, Recommendations, Automated Actions: content categorization, retention recommendations, duplicate identification.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
(1) Reduces costs, improves compliance with records management/FOIA, and data governance. Boosts transparency by making DOJ data discoverable and reusable. (2) DOJ records systems, NARA retention schedules, existing FOIA/eDiscovery platforms. (3) Measurable data storage savings; % of files tagged with metadata; improved FOIA response times.
Audit / financial statement impact
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.
Controls / human review
ATO: Not reported; PIA: Not published