AI Governance for Defense ATR Systems

AI governance for mission-critical defense AI — built with a complete MLOps platform that handles ATO documentation, edge deployment, and automated retraining from day one.

This demo walks through Domino's automated target recognition (ATR) system for unmanned underwater vehicles (UUVs), using sonar object classification as the concrete use case — a workflow equally applicable to drone footage analysis, infrastructure inspection, and search and rescue operations.

Deploy governed AI at the edge — models export to edge devices on UUVs, auto-scale to hundreds of analyst users, and maintain a complete model registry across dozens of terrain-specific variants, with every action logged against an ai governance framework built from a NIST AI RMF template.

Eliminate ATO bottlenecks with model governance — governance bundles attach to projects before a line of code is written; automated metric checks, compliance officer review workflows, and auto-populated model cards feed directly into ATO documentation with no email threads or spreadsheets

Operate on any infrastructure without DevOps overhead — compute scales across GPU clusters, Slurm, Spark, Ray, Dask, and MPI; air-gapped LLM connections generate analyst-readable classification summaries; and the model registry promotes winning models to dashboards, APIs, or Docker containers for edge deployment

Timestamps:

00:00 — What Does Mission-Critical AI Governance Look Like?

00:35 — ATR System Architecture: Dashboard, Model Registry & Edge Export

01:10 — Governed Data Access Across Classified and Unclassified Sources

01:45 — Experiment Tracking, Compute Scaling & AI Governance Framework Setup

02:25 — ATO Documentation: From Model Card to Governance Bundle

03:00 — Deployment Options: Dashboard, API Endpoint & Edge Container