Two very different platforms keep coming up in the same conversation: Zscaler, an inline cloud-proxy that inspects AI traffic through the Zero Trust Exchange, and Microsoft Purview, the data security and compliance layer native to Microsoft 365 and Copilot. This is a fair, source-based look at how each one protects data when employees use AI, scoped to the AI / DLP angle rather than every feature in either suite.
Zscaler and Microsoft Purview are not direct replacements for each other. They solve the AI data-protection problem from opposite ends.
Zscaler protects data as AI traffic crosses the network: it inspects prompts and responses inline through its cloud proxy and applies DLP, guardrails, and access policy. Microsoft Purview protects data where it already lives in Microsoft 365: it labels sensitive content, controls what Copilot can read and return, and records AI activity for compliance. Many organizations end up using both. Everything below is drawn from each vendor's public documentation, scoped to AI and data protection, and current as of 2026.
Scoped to AI and data-protection capabilities. Descriptions reflect each vendor's public documentation; nothing here is attributed beyond what the vendors state.
| Capability (AI / data protection) | Zscaler | Microsoft Purview |
|---|---|---|
| Primary architecture | ✓Cloud proxy / inline inspection through the Zero Trust Exchange; AI traffic is inspected in line. | ✓Native to the Microsoft 365 and Purview stack; endpoint DLP via Windows devices onboarded to Purview. |
| Prompt-level DLP for AI tools | ✓Inline DLP can detect and block sensitive data in prompts and responses using 100+ predefined dictionaries (PII, PCI, PHI, source code). | ✓DLP can detect Sensitive Information Types in prompts; endpoint DLP can warn or block pasting sensitive data into third-party GenAI sites in the browser. |
| Coverage of third-party AI tools (ChatGPT, Gemini, etc.) | ✓Inline coverage of GenAI apps that traverse the proxy; can detect and classify thousands of AI apps including AI embedded in SaaS. | ~Detected as 'Other AI apps' via browser activity in the Defender for Cloud Apps catalog; deepest controls are strongest for Microsoft Copilot and connected enterprise AI apps. |
| Microsoft 365 Copilot data protection | ✕Not the focus; Zscaler governs network-delivered AI traffic, not Copilot's grounding in your M365 tenant. | ✓Core strength: sensitivity labels, EXTRACT usage rights, and DLP control what Copilot can process and return inside the tenant. |
| Shadow AI discovery | ✓Dashboards surface users, departments, app trends, and at-risk data across detected AI apps. | ~DSPM for AI gives insights into AI activity; 'Other AI apps' are discovered through browser activity via Defender for Cloud Apps. |
| Access control over GenAI apps | ✓Policies can allow, block, or coach access by user or group, and can enforce browser isolation and control copy-paste within AI apps. | ~Endpoint DLP can warn or block specific actions (e.g. pasting) on browser GenAI sites; not a forward proxy that brokers app access. |
| AI security posture / guardrails | ✓Inline AI guardrails for prompt injection, jailbreaks, malicious URLs, and content moderation; AI-SPM for AI assets. | ~Insider Risk Management 'Risky AI usage' policy detects prompt injection and protected-material access; DSPM for AI for posture. |
| Audit, eDiscovery, retention of AI prompts | ~Logging and reporting on inspected AI traffic; not a compliance-records platform for Microsoft 365 content. | ✓Prompts and responses flow into the unified audit log, activity explorer, eDiscovery, and retention policies. |
| Real-time, in-the-moment employee coaching | ~A 'coach' policy action can warn users at the point of AI app access. | ~Endpoint DLP can show a user a policy tip or override prompt; coaching is action-blocking rather than guided education. |
Legend: ✓ clearly documented as a core capability · ~ documented but conditional or narrower in scope · ✕ not a focus of that product per public docs. Both are broad platforms; this table is intentionally limited to the AI and data-protection angle.
There is no single winner. The right choice depends on where your sensitive data and AI usage actually live.
If your sensitive data and AI usage live mostly inside Microsoft 365 and Copilot, Purview's sensitivity labels, EXTRACT usage rights, DLP, audit, eDiscovery, and retention give you the deepest in-tenant control with the least new tooling.
If you already route traffic through the Zero Trust Exchange and want inline DLP, AI guardrails, GenAI app access control, and browser isolation across many third-party AI tools, Zscaler covers that proxy-delivered layer well.
Both can warn or block sensitive data heading into browser AI tools, but each has a different center of gravity: Purview is M365-data-centric, Zscaler is network-inline-centric. Neither was designed primarily for in-the-moment workforce coaching at the browser and endpoint, regardless of which AI tool an employee opens.
Both Zscaler and Purview do important things well. But there is a layer neither was built to fully own: the moment an employee opens any AI tool in their browser or on their device, and the ongoing job of changing how the whole workforce uses AI. Purview is strongest inside Microsoft 365 and Copilot; Zscaler is strongest as an inline network proxy. Aona sits at the browser and endpoint, where workforce AI actually happens, regardless of which tool an employee chooses.
Aona is not a replacement for inline network security or for Microsoft 365 governance. It is the workforce AI layer that complements them. It is SOC 2 Type II certified and reports 92.9% AI classification accuracy, with a 30-day free trial and no credit card required.
SOC 2 Type II · 92.9% AI classification accuracy · 30-day free trial
Whichever way you lean on Zscaler or Purview, see how Aona adds shadow AI discovery, prompt-level DLP, and real-time coaching at the browser and endpoint. Book a demo on your own environment, or start a 30-day free trial, no credit card required.