AI Security
Securing AI/ML Workloads: End-to-End Protection for the ML Data Lifecycle
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.
Key Capabilities
Securing AI/ML Workloads Overview
Training Data Protection
Transform production data into ML-ready datasets using anonymization methods that preserve statistical distributions, correlations, and patterns essential for model accuracy while eliminating PII, PHI, and financial data.
Feature Store Security
Protect derived features that can reveal sensitive information, applying appropriate controls to direct PII, quasi-identifiers, and aggregated features based on re-identification risk.
Real-Time Inference Protection
Secure production inference with dynamic masking that operates in under 5 milliseconds, protecting input data before model processing and logging anonymized results.
ML Platform Integration
Native connectors for Databricks, AWS SageMaker, Azure ML, Google Vertex AI, and popular feature stores including Feast and Tecton, with Python SDK and Spark transformation support.
Model Governance & Audit Trail
Track sensitive data from source through training to deployed model (lineage), documenting protection methods and approvals, while generating compliance reports and access logs to prove no raw PII was used in model training.
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