Clustered 2 filings across 2 jurisdictions · filing window Feb 12, 2026 → Feb 13, 2026. View entity profile → Other incidents for this victim →
incident inc_ca767ca820f14d31 · merge_method deterministic · confidence 100%
Discovered → first regulatory filing
Range of discovered_at dates across filings
Identity (basic) · Health (basic)
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.
NH VT
all State AG
Earliest sighting first · deep chronology in Litigation Timeline
May 18, 2025 → May 22, 2025
When the intrusion reportedly occurred, per the linked filings
May 22, 2025
Reported by VERMONT AG, NEW HAMPSHIRE AG filings
Academic Urology & Urogynecology of Arizona notified consumers of a cybersecurity incident occurring between May 18-22, 2025. Unauthorized access potentially exposed names and protected health information. The organization engaged external forensic investigators, secured its environment, and is offering complimentary credit monitoring and identity theft protection services to affected individuals.
Academic Urology & Urogynecology of Arizona notified the NH Attorney General of a cybersecurity incident occurring between May 18-22, 2025. Unauthorized access to files containing PHI and PII (SSN, DL, financial info) affected approximately 2 NH residents. Notification to individuals began Feb 12, 2026, offering credit monitoring. No identity fraud reported.
Affected (this filing): 2
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.