Clustered 2 filings across 2 jurisdictions · filing window Dec 16, 2022 → Dec 23, 2022. View entity profile → Other incidents for this victim →
incident inc_147fbcde0f664f0f · 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.
Government ID · Identity (basic)
ME NH
all State AG
Earliest sighting first · deep chronology in Litigation Timeline
Oct 22, 2022
When the intrusion reportedly occurred, per the linked filings
Nov 17, 2022
Reported by MAINE AG filing
NAR Training, LLC reported unauthorized access to systems containing Social Security numbers and driver's license numbers affecting 3 New Hampshire residents. Notification was sent on December 19, 2022. The investigation is ongoing, and 24 months of credit monitoring is offered.
Affected (this filing): 3
NAR Training, LLC (DBA North American Rescue Education and Training) reported an external system breach (hacking) occurring on 10/22/2022 and discovered on 11/17/2022. The incident affected 1,408 individuals, including 1 Maine resident. Acquired data included names and driver's license numbers. The entity provided 24 months of identity protection services via Experian.
Affected (this filing): 1,408
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.