AI Training Data Transparency: Anonymization

Evidence & Data Points

• California Assembly Bill 2013 requires AI developers to disclose the sources and composition of training data for generative AI models. This includes disclosing whether personal information was included in training data, what categories of personal information, and how it was collected. Organization

Solution

The Solution: How anonymize.solutions Addresses This Self-Managed Training Data Processing anonymize.solutions' Self-Managed On-Premises deployment runs within the organization's infrastructure. Training datasets are processed through the anonymization engine before model training. All 260+ entity types are detected and replaced, ensuring no personal information remains in the data used for training. Audit Trail for Compliance Documentation The anonymization process generates logs documenting: entities detected per category, anonymization methods applied, processing timestamps, and data volumes. This audit trail directly supports AB 2013 disclosure requirements — organizations can demonstrate that personal information was detected and removed before training. Scale for Training Datas

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Compliance Context

Compliance Mapping This pain point directly addresses California AB 2013 (AI training data transparency), CCPA/CPRA (personal information processing), and intersects with EU AI Act Article 10 (training data governance). Anonymization provides a compliance strategy that satisfies multiple jurisdictions simultaneously. anonymize.solutions's GDPR, HIPAA, PCI-DSS, ISO 27001, SOC 2 compliance coverage, combined with Customer-selected (SaaS: Hetzner DE, Private: dedicated, Self-Managed: on-prem) hosting, provides documented technical measures organizations can reference in their compliance documenta