Clustered 2 filings across 2 jurisdictions · filed Jul 15, 2019. View entity profile → Other incidents for this victim →
incident inc_9a8825662326441a · merge_method deterministic · confidence 100%
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.
CA MT
HHS OCR · State AG
Earliest sighting first · deep chronology in Litigation Timeline
Aug 1, 2018
When the intrusion reportedly occurred, per the linked filings
Natera, Inc. reported a data breach to the Montana Attorney General. The breach was reported on 2019-07-15. The breach occurred from 8/1/2018 to 3/30/2019. 8 Montana residents were affected.
Affected (this filing): 8
Natera, Inc. reported to HHS on 2019-07-15 a Hacking/IT Incident affecting 3035 individuals. Breached information located on Network Server. The breach involved a business associate, Retrieval-Masters Creditors Bureau, Inc. (d/b/a American Medical Collection Agency), which was the victim of a cyber-attack affecting ePHI including names, addresses, birthdates, treatment information, and financial data. Natera notified HHS, individuals, and media, provided credit monitoring, and terminated the BA relationship.
Affected (this filing):
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.