Data Security and Privacy for AI
Training Data Guardrails: Eliminate Exposure Before Training
Every model is a function of its training data, and the copies it is built from are rarely governed. To build, fine-tune and evaluate ML and LLM systems, teams create dozens of copies — extracts, notebooks, feature stores, experiment runs, vendor uploads. Runtime guardrails govern what a model is asked and what it answers, not the sensitive data baked in during development. By the time a prompt filter runs, the exposure has already happened upstream. Models trained on raw PII or PHI can memorize and later leak it through inference or extraction attacks, and GDPR, CPRA, HIPAA and PCI DSS all require sensitive data to be controlled before it enters non-production environments.
Mage Data Training Data Guardrails protect sensitive information before a dataset is ever used to train, fine-tune, evaluate or test a model. They give data, privacy and ML teams a single operating model: discover sensitive elements, protect them with the right technique, and validate that the result is still fit for the model's task. Once protected, a dataset is no longer sensitive — it can move to your central AI/ML team, across departments, or to third parties without the privacy hold-ups that normally slow data sharing down.
Key Capabilities
Controls apply at the two stages where sensitive data is most exposed — before it fans out into features, experiments and deployed models — while continuous discovery runs across the whole lifecycle.
Training Data Guardrails Overview
Integrated Sensitive-Data Discovery
Mage Data's patented engine — dictionary, pattern, NLP and AI context analysis — across structured, semi-structured and unstructured content.
Single Policy Across the Platform
One policy drives classification and protection, managed in one pane of glass.
Flexible Enforcement Points
Apply protection in secure pipelines at collection, or via SDK/API inside notebooks and pipelines at preprocessing.
80+ Protection Techniques
Masking, encryption, tokenization, anonymization and synthetic data, applied automatically by policy.
Runs Inside Your Environment
Deployed in your VPC and your pipelines; the SDK lives inside the workflows teams already have. Your data never leaves your premises.
Runs inside your environment. Deployed in your own VPC and your pipelines — your data never leaves your premises, and nothing is processed in a vendor cloud.
Protect your training data before the first run
Book a 30-minute demo and we will show Training Data Guardrails applied to a dataset like yours — protected, and still fit to train on.