Butler Snow LLP reported a data breach to the Montana Attorney General. The breach was reported on 2023-09-22. The breach occurred on 5/3/2023. 17 Montana residents were affected.
Affected (this filing): 17
Clustered 2 filings across 2 jurisdictions · filed Sep 22, 2023. View entity profile → Other incidents for this victim →
incident inc_53faed1c22184041 · merge_method deterministic · confidence 100%
Discovered → first regulatory filing
Time between earliest and latest filing
Not recorded for this incident
Discovery variance · Leak precedence · Materiality delta · SEC filing delay — no leak-site claim in this cluster; no SEC 8-K in this cluster; needs two dated filings.
ME MT
all State AG
Earliest sighting first · deep chronology in Litigation Timeline
May 3, 2023
When the intrusion reportedly occurred, per the linked filings
Aug 15, 2023
Reported by MAINE AG filing
Butler Snow LLP reported a data breach to the Montana Attorney General. The breach was reported on 2023-09-22. The breach occurred on 5/3/2023. 17 Montana residents were affected.
Affected (this filing): 17
Butler Snow, LLP, a professional services firm, reported a data breach affecting 11 Maine residents. The breach, which occurred on May 3, 2023, and was discovered on August 15, 2023, resulted from an external system breach (hacking). The compromised information included names and Social Security numbers. The company notified the affected individuals on September 22, 2023, and offered 24 months of credit monitoring and identity restoration services through Experian.
Affected (this filing): 11
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.