Use Case
Build AI without exposing personal data
Generate synthetic datasets that preserve the statistical properties of real data — without containing any real personal information.
The challenge
Privacy regulations constrain AI development
AI development often requires access to sensitive personal data — financial records, medical information, customer behaviour. Privacy regulations (GDPR, HIPAA, CCPA) restrict how this data can be used for training. Anonymisation is fragile. Synthetic data is the structural solution.
No PII in training data
Generate training data that statistically resembles real data but contains no real personal information.
Regulatory compliance
Use synthetic data to satisfy GDPR, HIPAA and CCPA requirements for AI training data.
Share without risk
Share datasets across teams, vendors and jurisdictions without privacy review processes.
How LiteSeed helps
Cloud-Assisted Masking
When uploading a seed file, LiteSeed can detect and mask PII fields automatically using cloud-assisted analysis (opt-in).
- Detects: email, phone, SSN, credit card, IP address, full name
- Deterministic masking: masked values are consistent across rows
- Opt-in only — no data leaves the system without explicit consent
- Masked fields are flagged in the Blueprint for transparency
Synthetic generation from masked seeds
Generate new data from a masked seed — the statistical properties of the original data are preserved without any real PII.
- Distribution inference from masked seed values
- Faker-style generators for name, email, phone fields
- UUID and hash generators for identifier fields
No real data in exports
Generated datasets contain only synthetic values — no row from the seed file is included in the output.
- Generation is purely statistical — not a copy of the seed
- Seed file is never included in exports
- Seed file retention policy configurable per project
Audit trail for compliance
Every generated dataset version has a full audit trail — Blueprint, seed hash, generation parameters and export history.
- blueprintHash and generationSeed per dataset version
- Export history with timestamps and format
- Full lineage from seed to dataset version