Clustered 2 filings across 2 jurisdictions · filing window Feb 11, 2022 → Feb 28, 2022. View entity profile → Other incidents for this victim →
incident inc_83b5ade127df4328 · merge_method human · 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
Apr 2, 2021
When the intrusion reportedly occurred, per the linked filings
Jan 10, 2022
Reported by MAINE AG filing
Dr. Day Care reported a data breach to the Montana Attorney General. The breach was reported on 2022-02-11. The breach occurred on 4/2/2021. 1 Montana residents were affected.
Affected (this filing): 1
Dr. Day Care, an education entity based in Rhode Island, experienced an external system breach (hacking) on April 2, 2021. The incident affected 3,143 individuals, including 5 Maine residents. The breach was discovered on January 10, 2022. Compromised data included names and driver's license numbers. The entity provided written notification on February 11, 2022, and offered 12 months of identity theft protection services through Experian.
Affected (this filing): 3,143
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.