Controlled synthetic data pipeline
Problem
Real police reports contain sensitive personal and operational data. Teams need realistic test data, but actual documents cannot leave the production environment. Manual anonymisation is slow, error-prone and doesn’t scale.
Approach
A controlled generation pipeline that produces synthetic documents based on anonymised templates, statistical scenario sampling and language models — with quality gates at every step. The pipeline separates case structure, statistical distribution and language generation so each component is independently verifiable.
Process
- Anonymise — Remove sensitive data from source material
- Template — Define document structure and scenario parameters
- Generate — Produce synthetic content using language models
- Validate — Run schema validation and statistical checks
- Trace — Record generation parameters and provenance
- Review — Human assessment of quality and representativeness
Controls & evidence
- Privacy shield — Personal data is removed before generation. The pipeline has no access to original identities.
- Schema validation — Every generated document is validated against the expected schema. Invalid output is rejected.
- Trace links — Every synthetic document is traceable to its generation parameters, template and validation results.
- Human review gate — Representative samples are assessed by domain experts before a batch is released.
Outcome
Synthetic police reports that are statistically representative, schema-valid, privacy-safe and traceable to their generation parameters. Teams can safely test, train and demonstrate without using real personal data.