What Causes AI Data Leakage and Tips for Staying Protected

This report outlines how AI systems can leak sensitive data across the AI stack (LLM outputs, API logs, agent tool calls, and training pipelines), explains regulatory, intellectual property, and reputational impacts, and recommends detection and prevention measures—such as audit logging, agent monitoring, data classification, data masking, short‑lived credentials, policy‑based access, and centralized identity controls—while positioning Aembit as a platform to manage agentic AI access.