Clustered 2 filings across 2 jurisdictions · filing window Apr 8, 2026 → Apr 14, 2026. View entity profile → Other incidents for this victim →
incident inc_cf22264e738942bb · merge_method deterministic · confidence 100%
Discovered → first regulatory filing
Range of discovered_at dates across filings
PII · 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.
CA TX
all State AG
Earliest sighting first · deep chronology in Litigation Timeline
Feb 22, 2026
When the intrusion reportedly occurred, per the linked filings
Mar 6, 2026
Reported by CALIFORNIA AG, TEXAS AG filings
TEXCAZ Insurance Services notified the California AG of a data breach involving driver names, license numbers, and dates of birth. The breach occurred on Feb 22, 2026, and was detected/stopped on Mar 6, 2026. The company reinforced cybersecurity procedures including MFA and password updates. No specific count of affected individuals was disclosed.
TEXCAZ Insurance Services based in Leander, Texas, a insurance services entity reported a data breach to the Texas Attorney General. The breach was discovered on 2026-03-06 and reported on 2026-04-14. 1,606 Texas residents were affected. 2,628 individuals affected in total. Types of information involved: Name of individual;Driver’s License number;Date of Birth. Consumers were notified via Posted at company website or special website;U.S. Mail.
Affected (this filing): 1,606
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.