Clustered 4 filings across 3 jurisdictions · filed Jan 8, 2025. View entity profile → Other incidents for this victim →
incident inc_3e15f007f01b444d · merge_method deterministic · confidence 100%
Discovered → first regulatory filing
Range of discovered_at dates across filings
Time between earliest and latest filing
Not recorded for this incident
Leak precedence · Materiality delta · SEC filing delay — no leak-site claim in this cluster; no SEC 8-K in this cluster.
Identity (basic) · Government ID · Health (basic)
CA TX VT
HHS OCR · State AG
Earliest sighting first · deep chronology in Litigation Timeline
Sep 24, 2024 → Oct 14, 2024
When the intrusion reportedly occurred, per the linked filings
Oct 11, 2024
Reported by CALIFORNIA AG, VERMONT AG filings
BayMark Health Services, Inc. reported to HHS on 2025-01-08 a Hacking/IT Incident affecting 265701 individuals. Breached information located on Network Server. Business Associate present.
Affected (this filing): 265,701
BayMark Health Services, Inc. reported to HHS on 2025-01-08 a Hacking/IT Incident affecting 3170 individuals. Breached information located on Network Server. Business Associate present.
Affected (this filing): 3,170
BayMark Health Services, Inc. reported that an unauthorized party accessed files on its systems between September 24 and October 14, 2024. The company discovered the incident on October 11, 2024. Affected information may include names, Social Security numbers, driver's license numbers, dates of birth, and health-related data such as services received, dates of service, insurance information, and treatment details. BayMark engaged third-party forensic experts, notified law enforcement, and implemented additional security safeguards. Complimentary credit monitoring is being offered to affected individuals.
BayMark Health Services, Inc. notified consumers of a data breach where unauthorized parties accessed patient files between September 24 and October 14, 2024. The incident involved names, SSNs, driver's licenses, DOBs, and PHI. BayMark engaged forensic experts, notified law enforcement, and offered one year of Equifax credit monitoring.
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.