Graph Modeling

A single representational law across T-UEBA and Augrade

Graph modeling works best when paired with explicit boundaries: learned compression inside, deterministic constraints outside.

T-UEBA: temporal heterogeneity

In tactical security, graphs encode users, hosts, processes, and events over time. The objective is causal legibility under shift, not abstract embedding quality.

  • streaming graph updates
  • multi-plane risk calibration
  • evidence drilldown for analysts

Augrade: geometry with editability

In 2D→3D workflows, graph-only models underfit hierarchy and repeated structure. A graph-program hybrid better preserves motifs, constraints, and revision semantics.

  • geometry-native object tokens
  • typed relation graphs
  • program-like edit grammars + validators

Distributed execution implication

DAICON-style deployment pressures make representation and systems inseparable: you must decide what state travels, what computes locally, and what remains verifiable under unreliable links.

RAM Labs dossier · Augrade artifact summary