Clustered 2 filings across 2 jurisdictions · filing window Feb 3, 2025 → Mar 3, 2025. View entity profile → Other incidents for this victim →
incident inc_bb0255747b3d41df · merge_method deterministic · confidence 95%
Discovered → first regulatory filing
Range of discovered_at dates across filings
Identity (basic) · Government ID
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.
MD VT
all State AG
Earliest sighting first · deep chronology in Litigation Timeline
Jan 16, 2025
Reported by MARYLAND AG, VERMONT AG filings
Sandy Spring Museum notified Maryland AG of a data security incident discovered Jan 16, 2025. An unauthorized individual gained access to systems. Affected data included names, addresses, DOBs, and SSNs of 23 Maryland residents. Museum engaged forensic experts, reset passwords, reformatted desktops, and offered 12 months of credit monitoring.
Affected (this filing): 23
Sandy Spring Museum notified consumers of a data security incident discovered on January 16, 2025. An unauthorized individual gained access to systems, potentially exposing names and addresses of former employees and contractors. The museum engaged forensic experts, secured systems, and offered credit monitoring via Epiq.
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.