AI and machine learning have moved from experimental to mission-critical, with organizations deploying models for fraud detection, customer recommendations, predictive maintenance, and healthcare diagnostics. But this expansion creates security challenges at every stage-sensitive data in training datasets, PII in feature stores, real-time inference on customer records, and prediction logs that could expose private information. The ML lifecycle has become an expanding attack surface.

Mage Data provides end-to-end data security for AI/ML workloads, protecting sensitive information from data ingestion through model deployment. By integrating with modern ML platforms like Databricks, SageMaker, and Vertex AI, organizations can build accurate models using protected data, maintain compliance with healthcare and financial regulations, and enable data scientists with self-service access to safe datasets.