Veraify powered by Cloudbrink secures AI at the endpoint — where prompts, local agents, browser extensions and AI applications actually run. See how that compares to cloud-proxy approaches.
Zscaler’s approach is built around the Zero Trust Exchange, cloud inspection, AI access controls, AI Guard, shadow AI discovery, and AI security services. Zscaler positions its AI Security Suite as a way to secure AI adoption, protect GenAI apps, models, and users, and govern enterprise AI usage.
Zscaler is strongest when traffic reaches the Zscaler cloud. Local AI agents, desktop AI assistants, cert-pinned apps, offline activity, and high-volume developer AI workflows can be difficult or impractical to control through a cloud-only proxy model.
Capability
Veraify powered by Cloudbrink
Zscaler
Core architecture
Distributed zero-trust platform with endpoint, edge, and AI-aware enforcement
Cloud proxy / Zero Trust Exchange
AI traffic focus
Users, endpoints, apps, data, AI agents, APIs, and workflows
AI access, cloud inspection, GenAI governance
Local / desktop AI agents
Designed to detect and govern endpoint AI activity
Limited when traffic does not reach cloud proxy
Developer AI tools
Built for high-volume, low-latency AI traffic
Require cloud hairpinning
Request-side DLP
Scans data before it leaves the device
Primarily proxy-based inspection
Offline protection
Endpoint-first model supports local enforcement
Cloud enforcement depends on connectivity
Configuration overhead
Built-in catalogue of AI tools and PII for simple and quick enforcement of protection guardrails
Manual URL/DLP policy definitions on per-AI tool makes huge configuration overhead and error-prone
Performance model
FAST Edges and high-performance ZTNA
Cloud PoP inspection path adds latency and downgrades performance
Best fit
AI-native security across users, agents, and data
Enterprises already standardized on Zscaler cloud security willing to leave embedded AI unprotected
Unless the enterprise has advanced expertise and personnel in managing Zscaler deployment, trying to use Zscaler AI Guard to secure end user AI adaption will become a huge uphill task with lots of configuration additions, lower performance on AI as well as regular Internet traffic, and the risk of missing many AI paths when users fine-tune settings on the AI Agent.
Netskope’s approach is based on Netskope One, SkopeAI, CASB, SWG, DLP, AI discovery, and control of GenAI usage. Netskope says its AI security secures users, agents, applications, and data across the AI ecosystem, with visibility and protection from pre-deployment to runtime.
Netskope remains largely cloud-proxy dependent for inline inspection. That works well for controlled SaaS and browser-based AI, but local AI agents, desktop tools, unmanaged AI workflows, and traffic that does not traverse the Netskope cloud remain harder to govern.
Capability
Veraify powered by Cloudbrink
Netskope
Core architecture
Endpoint + distributed edge + zero-trust AI governance
SASE/SSE cloud platform
AI traffic focus
AI interactions wherever they occur
GenAI apps, SaaS, cloud, DLP, user coaching
Shadow AI
Designed for browser, desktop, API, and agentic AI
Strong SaaS/app discovery, proxy-dependent
Local AI agents
Endpoint-native protection
Limited for activity outside cloud path
AI DLP
Request-side controls before data leaves endpoint
Prompt and response DLP through cloud inspection
Developer workflows
Designed to avoid unnecessary latency and data hairpinning
Cloud hairpinning and inspection can add overhead
Configuration overhead
Built-in catalogue of AI tools and PII for simple and quick enforcement of protection guardrails
Manual URL/DLP policy definitions on per-AI tool makes huge configuration overhead and error-prone
Deployment
Very simple, requires only agent installation on endpoints
Complex set of multiple components (agents, MCP gateways, brokers) makes deployment challenging
Unified secure access
ZTNA, AI governance, performance, and visibility in one platform
Broad SASE/SSE platform
Best fit
Organizations needing endpoint-aware AI governance
Organizations already invested in Netskope SSE
Enterprises looking to get quick start into monitoring of AI usage followed by AI guardrail enforcement should stay away from Netskope which requires complex deployment steps for MCP gateways, brokers and agents. The performance of AI tools also will be very slow due to limited POP availability and cloud-proxy model.
