Lilly Tech Systems
AI Data Loss Prevention
Learn how to protect sensitive data throughout the AI lifecycle. From classifying data sensitivity to detecting leakage in model outputs, implementing prevention controls, and monitoring for data exposure — build a comprehensive DLP strategy for your AI systems.
Your Learning Path
Follow these lessons to build a comprehensive AI DLP strategy.
1. Introduction
Why DLP matters for AI, unique data loss vectors in AI systems, and the DLP landscape.
2. Classification
Data classification frameworks for AI — sensitivity levels, labeling strategies, and automated classification.
3. Detection
Detecting sensitive data in AI inputs, outputs, training data, and model artifacts.
4. Prevention
Implementing controls to prevent data leakage through AI systems and model outputs.
5. Monitoring
Continuous monitoring for data exposure, anomaly detection, and compliance reporting.
6. Best Practices
Enterprise patterns for comprehensive AI data loss prevention programs.
What You'll Learn
By the end of this course, you'll be able to:
Classify AI Data
Implement data classification frameworks tailored for AI training data and model artifacts.
Detect Data Leakage
Identify sensitive data exposure in model inputs, outputs, and training pipelines.
Prevent Data Loss
Implement technical controls that prevent sensitive data from leaking through AI systems.
Monitor Continuously
Build monitoring systems that detect anomalies and maintain ongoing DLP compliance.