Founded in 1992 and headquartered in Conyers, Georgia, Volume Transportation, Inc. provides ground transportation, cargo loading, warehousing, storage, and material flow management services. 1.The document is a confidential mediation stateme ...
Clustered 2 filings across 1 jurisdiction · filing window Aug 14, 2025 → Aug 19, 2025. View entity profile → Other incidents for this victim →
Attribution, victim identity, and counts shown here derive from a threat actor's public extortion-blog claims, aggregated by ransomware.live. They have not been validated by the victim or any regulator. Treat them as the threat actor's assertion until a regulatory filing or victim disclosure corroborates them.
incident inc_0ee08eed3fc947d5 · merge_method llm · confidence 50%
Time between earliest and latest filing
Not recorded for this incident
Discovery variance · Leak precedence · Materiality delta · SEC filing delay — no SEC 8-K in this cluster; needs two dated filings.
all Leak Site
Earliest sighting first · deep chronology in Litigation Timeline
Founded in 1992 and headquartered in Conyers, Georgia, Volume Transportation, Inc. provides ground transportation, cargo loading, warehousing, storage, and material flow management services. 1.The document is a confidential mediation stateme ...
Founded in 1992 and headquartered in Conyers, Georgia, Volume Transportation, Inc. provides ground transportation, cargo loading, warehousing, storage, and material flow management services. 1.The document is a confidential mediation stateme ...
About this clustering
DisclosureLens links filings into incidents through layered matchers: deterministic rules (same source document, multistate filings of one breach, tight-window same-victim pairs), a weighted-similarity scorer for cross-source candidates, and an operator review queue for everything uncertain. Each link records its own method and confidence — shown per filing in the timeline below. The system defaults to NOT merging when uncertain, because a false merge (collapsing two unrelated breaches) is more harmful than a false split (showing related filings separately); uncertain pairs route to human review instead of auto-merging. Filing summaries shown in the timeline are AI-generated extracts — verify each against its linked source.