Clustered 3 filings across 2 jurisdictions · filing window Dec 11, 2023 → Aug 9, 2024. View entity profile → Other incidents for this victim →
incident inc_4a0a20f6cbbc496e · merge_method human · confidence 100%
Discovered → first regulatory filing
Range of discovered_at dates across filings
Gap between first leak claim and first regulatory filing
Time between earliest and latest filing
Not recorded for this incident
Materiality delta · SEC filing delay — no SEC 8-K in this cluster.
CA
Leak Site · State AG
Earliest sighting first · deep chronology in Litigation Timeline
Nov 6, 2023 → Dec 6, 2023
When the intrusion reportedly occurred, per the linked filings
Dec 6, 2023
Reported by CALIFORNIA AG filings
The Glendale Unified School District is a school district based in Glendale, California, United States. It consists of 20 elementary schools, 4 middle schools, 4 high schools and 3 facilities for homeschoolers and special-needs students.It currently has about 20,000 students
Glendale Unified School District experienced a security incident between November 6 and December 6, 2023. On December 6, 2023, the district launched an investigation into suspicious activity. The investigation determined that an unauthorized party accessed files containing employee personal information, including names, addresses, dates of birth, Social Security numbers, Driver’s License numbers, and financial account information. The district locked down its network, engaged cybersecurity experts, and offered identity protection services to affected employees.
Glendale Unified School District experienced a security incident involving unauthorized access to personal information of students, parents, and employees. The breach occurred between November 6 and December 6, 2023, and was discovered on December 6, 2023. Affected data may include names, addresses, Social Security numbers, and educational records. The district engaged forensic experts, secured its network, and is offering credit monitoring services. This is a supplemental notice providing updated findings from the investigation.
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.