These are the specific frameworks and obligations relevant to your sector, not a generic GDPR checklist. Each one has a direct implication for how you govern AI use and data handling.
If personal data is in your training set, erasure requests create a model retraining obligation, unless training data was properly anonymised.
Only data necessary for the specific purpose should be processed. Production data used for ML training typically violates this principle.
ICO guidance on lawful bases for training AI systems and the requirements for data minimisation in ML pipelines.
These are the specific workflows most organisations in your sector deploy first, in plain terms.
Generate synthetic datasets that match the statistical distribution, correlation structure, and null rates of production, without any real PII. FK-preserving extraction maintains referential integrity. Your models train on data that behaves like production.
Differential privacy noise injection with configurable epsilon. GDPR data minimisation and HIPAA safe harbour compliant synthetic outputs. Quantifiable privacy guarantee on every dataset.
Export directly to Parquet, S3, Delta Lake, or your existing data lake. No intermediate CSV steps. Compatible with PyTorch, TensorFlow, and HuggingFace data loading patterns.
Configure once. The production-to-training pipeline runs automatically. ML team always has a current, privacy-safe dataset without accessing production systems or raising a ticket.
Both products share the same detection engine. Most organisations in your sector start with one before adding the other.
Synthetic data pipeline for ML teams. Differential privacy mode. Direct Parquet and S3 export. Scheduled refresh. Designed to eliminate production data from ML training workflows.
Data scientists use AI assistants to write training code, debug pipelines, and query production-like data. VestraShield intercepts every AI-assisted session to ensure sensitive content stays within your environment.
Referential integrity preserved across related tables. Relational structure of production data maintained in synthetic output.
Noise injection with configurable epsilon. Quantifiable privacy guarantees on every synthetic dataset, not just a qualitative claim.
Direct export to Parquet, S3, and Delta Lake. Column names and schema preserved. Works with PyTorch, TensorFlow, and HuggingFace DataLoaders.
Configure once. Refresh runs automatically on your schedule. ML team always has a current dataset. No access to production required.
Statistical distribution, correlation structure, and null rates matched to production. Edge cases preserved in synthetic output.
Synthetic training data means erasure requests don't create retraining obligations. The personal data was never in the training set.
Every AI-assisted cell execution in Jupyter, Colab, or similar environments is intercepted. Production-like data in notebooks doesn't reach external LLMs.
GitHub Copilot, Cursor, and code assistant completions governed. Data scientists writing training code are covered without changing their workflow.
AI-assisted queries against production-like data stores intercepted at the HTTP proxy layer. No application changes required.
Data science, ML engineering, and analytics teams get different intercept rules. Fine-grained configuration without a separate deployment per team.
Model names, experiment IDs, pipeline references, and dataset identifiers caught by zero-shot GLiNER. No retraining required for custom entity types.
Every AI-assisted data science session logged with entity inventory. Attributable to team and user. Hash-chained and tamper-evident.
We connect to something real in your environment and you see actual findings. No slide decks. No fabricated data. Median time to first scan: under 4 hours from credentials.
For ML engineers and data scientists. SDK integration and pipeline questions welcome.