From Launch to Leadership: Zscaler AI Protect Raises the Bar for AI Security

Overview

Six months ago, we launched Zscaler AI Protect, the industry's first platform built to secure AI from the ground up. At that time, enterprise AI was accelerating fast. Today, it's moving faster still.

The pace of change is the point. What took years in traditional security cycles is happening in months with AI. That's why we didn't wait. At Zenith Live 2026, just six months after the initial launch, we're shipping a wave of enhancements to AI Protect that deepen coverage, sharpen controls, and close the gaps that matter most to security teams right now.

Here's what's new.

AI Asset Management: See Everything That's Running AI

Security teams can't protect what they can't see. AI has spread far beyond sanctioned tools; it's embedded in SaaS traffic, running in cloud environments, and baked into developer codebases. These enhancements give you the full picture.

• Support for 2,900+ AI Apps: Shadow AI is already in your organization. With visibility across the broadest AI app catalog in the industry, you'll see every tool in use, sanctioned or not.
• Public Cloud Agent Scanning: AI agents are spinning up across AWS, Azure, and GCP faster than any team can manually track. Automatic discovery and assessment means nothing slips through your cloud footprint.
• Source Code Scanning: AI i s being written into your applications right now. Risky AI usage and exposed model logic in agentic codebases gets caught before it ever reaches production.
• AI Code Runtime Scanning: Some threats only emerge when code is actually running. Monitoring agentic code in live environments catches what pre-deployment scans can't.
• AI Attack Surface Analysis: You can't defend what you haven't mapped. Get a continuous, comprehensive view of every AI asset, connection, and exposure, before an adversary finds it first.

Together, these capabilities answer the question every CISO is asking: what AI is actually running in my environment, and where am I exposed?

Secure Access to AI: Deeper Controls, Built for How AI Actually Works

Knowing what's running is only half the battle. These enhancements give your security and compliance teams the precision to control how AI is actually used—without slowing down the business.

• Multi-Turn Prompt Inspection: AI conversations aren't single exchanges. Evaluating the full context across multiple prompts catches risks that a single-turn view would miss entirely.
• Replay Prompt & Response Activity: Investigations and audits demand the full picture, not snapshots. Every AI interaction is captured and replayable, exactly as it happened.
• Runtime Protection Enforcement: Policies that only kick in after the fact aren't protection; rather, they're documentation. Enforcement at the moment of interaction stops risk before it lands.
• Auto-Remediation Policies: Not every violation needs a human in the loop. Detected violations are acted on automatically, reducing response time and freeing your team for higher-stakes work.
• Anthropic & OpenAI Compliance APIs: Your users are already working in ChatGPT and Claude. Native support for both compliance APIs means your policies follow them there without custom engineering.
• Bring Your Own Detector: Every organization defines sensitive content differently. Enforce your own detection models natively, so the platform works with your risk profile, not a generic one.
• Integration with Zscaler Private Access: AI risk doesn't stop at the public cloud boundary. Extending controls to private applications and internal workloads makes your Zero Trust policy truly end-to-end.

Visibility without control is just observation. These capabilities turn insight into enforcement across every AI interaction, every environment, every user.

Secure AI Infrastructure and Apps: From Deployment to Trust

Visibility and access controls address how AI is used. This third layer addresses whether the AI itself can be trusted; and for teams responsible for hardening AI infrastructure, it's where the most consequential new capabilities live.

• Onboarding Agent: Every new AI tool is a potential risk vector, and manual assessments can't keep pace. The full risk evaluation process is automated, so your team can clear new tools in hours, not weeks.
• MCP Red Teaming: The Model Context Protocol (MCP) is the emerging standard for agentic AI communication, and it's already being targeted. Automated adversarial testing directly against your MCP servers finds weaknesses before an attacker does.
• Prompt Hardening Service: Prompt injection is one of the most common and damaging ways to manipulate AI behavior. Systematic hardening at the service level reduces your exposure before it can be exploited.
• Compliance Heat Map: Governance gaps are easiest to fix before they become incidents. A visual, always-current view of your AI governance posture shows you exactly where you're strong and where to focus next.

Deploy fast. Trust what you deploy. That's what this pillar is built for.

The Bigger Picture

AI Protect launched in January 2026 with a clear thesis: securing AI requires a purpose-built platform, not retrofitted tools. Sixteen new capabilities later, that thesis isn't just holding—it's compounding.

Enterprises don't need to choose between AI speed and AI security. They need a platform that makes that trade-off obsolete. That's what we've built, and it's available now.

Ready to see it in action? Learn more and schedule a demo.