AI adoption is accelerating, but so is the risk that comes with it.

As organisation scale AI across workflows, they’re exposing sensitive data in ways traditional security models weren’t designed to handle. The challenge isn’t just protecting models, it’s securing the entire data lifecycle behind them.

This guide by Zscaler explores how security leaders can bring structure, visibility, and control to AI environments without slowing innovation.

In this guide, you will learn how to: 

• Identify hidden risks across AI models, training data, and pipelines
• Understand threats like data poisoning, prompt injection, and model theft
• Gain visibility into sanctioned and unsanctioned (shadow) AI usage
• Build a framework for AI governance, data security, and compliance
• Secure AI at scale using AI-SPM and DSPM approaches