Govern AI like it's production. Because it is.
AI applications are shipping into production faster than most governance frameworks can keep up. NetScaler AI Gateway gives IT and security leaders a single point of control for monitoring, managing and governing enterprise AI on the delivery infrastructure you already trust.
Analysts project roughly a third of enterprise AI projects will be abandoned by 2029 with ineffective governance and risk controls, and inadequate AI-ready data, among the leading causes.
Source: Gartner forecast analysis - Sept 2025The AI projects that scale will be the governed ones.
Pilots are easy. Production is where AI applications meet cost controls, security review, observability, provider availability and operational ownership. Four gaps show up quickly when teams connect applications directly to model providers.
The token bill arrives after usage has already happened
LLM consumption is metered in tokens. Without per-app and per-team rate limits, costs can grow faster than infrastructure teams can explain them.
Apps become tied to one model provider too early
When authentication, routing and observability are embedded in each app, switching models later becomes a code project instead of a policy decision.
Prompts and responses are a new attack surface
Prompt injection, jailbreaks, data leakage and toxic output need inspection that understands AI traffic, not only traditional web requests.
Every team builds its own AI plumbing
Team-by-team auth, logging and policy controls multiply inconsistency. Governance works best when the control point is central and repeatable.
Across ASEAN and South Asia, teams want productivity gains quickly while regulators and customers still expect control.
AI traffic may cross cloud services, private models and existing enterprise systems. One control layer reduces fragmentation.
Regional IT teams need governance that plugs into the delivery infrastructure they already understand.
A single point of control for enterprise AI.
NetScaler AI Gateway sits between your applications and your AI inference services, cloud or on-premises, monitoring, managing and governing AI usage across the enterprise.
How enterprise AI traffic moves through NetScaler
Applications, users and agents integrate with one governed path. NetScaler applies token controls, model routing, security policy and observability before traffic reaches any model provider.
Requests arrive from application front ends, core apps, chat tools and copilots.
Token rate limits, smart load balancing, routing, security policy and usage cost tracking happen in one place.
Requests route to cloud LLM provider A, cloud LLM provider B or a self-hosted model without locking every app to one model.
Prompts, traces, performance metrics and evaluations become part of the governed delivery path.
One integration pattern for AI applications
Teams connect applications to the gateway instead of wiring every app directly to each model provider. Governance becomes part of the platform path, not a separate project.
Token-aware performance and resilience
Use token-based rate limiting, performance-aware routing and provider failover to keep AI services available while controlling consumption.
Consistent policy and security controls
Inspect AI traffic, enforce access rules, record usage and apply central guardrails before prompts reach a model or responses reach a user.
Usage evidence for AI cost management
Track prompts, tokens, latency and provider usage so IT, security and application owners can tune policy with real operational evidence.
Enterprise AI governance with NetScaler.
Watch a practical NetScaler AI Gateway walkthrough for teams moving AI applications from experiments into governed production workflows.
Enterprise AI control with NetScaler AI Gateway
Featuring Brian Huhn, Senior Product Marketing Manager, NetScaler
See how a gateway approach helps teams centralise access, route across model providers, track token usage and apply AI-aware security before traffic reaches a model endpoint.
- How a single AI gateway endpoint simplifies onboarding for AI applications and coding assistants.
- Where token-based rate limiting, provider routing and cost tracking fit into the production path.
- How LLM security, PII controls and AI WAF signatures reduce risk before traffic reaches the model.
- Why MCP changes the governance conversation for AI agents that need enterprise data and tools.
- How Citrix Asean and South Asia can help position the discussion for customers and partners.
Defence in depth for prompts, responses and AI APIs.
AI security is not one filter. NetScaler AI Gateway combines delivery-layer enforcement with AI-aware protections for data exposure, prompt manipulation, harmful output and API abuse.
Mask risky data before it reaches the model
Inline detection and masking can help protect personal or sensitive information while keeping prompts useful enough for the model to reason on.
Inspect AI interactions in real time
AI-native inspection helps detect prompt injection, jailbreak attempts, data leakage and unsafe output patterns that traditional controls may not understand.
Protect the AI services themselves
NetScaler WAF and API protections help secure LLM endpoints and MCP servers against malformed payloads, abuse and emerging AI-specific attack paths.
Connecting AI agents to enterprise systems safely.
Model Context Protocol is how AI agents reach enterprise data and tools. That makes the MCP layer the next governance frontier, and NetScaler is already there.
NetScaler as the governed path for MCP traffic
AI agents should not connect directly to every enterprise system. NetScaler can act as an MCP proxy, enforcing access, policy, observability and guardrails between agents and the systems they act on.
The agent asks for data, context or action from enterprise tools.
Authentication, policy, guardrails and observability sit between agents and systems.
Responses are structured and governed before they are returned to the agent.
NetScaler as the control point for agent traffic
Place authentication, policy enforcement, observability and guardrails between AI agents and the enterprise systems they query or act on.
A governed way into operational intelligence
Expose NetScaler operational data through a structured MCP interface so AI-assisted operations can use context without bypassing platform controls.
Useful next steps for the AI Gateway conversation.
Explore verified NetScaler AI Gateway materials for a deeper look at enterprise AI governance, delivery and security.
NetScaler AI Gateway for enterprise AI application delivery
Read Citrix's announcement on bringing governance to AI application delivery with NetScaler.
Read the announcementNetScaler AI Gateway documentation
Review the technical entry point for AI Gateway, including configuration and feature guidance.
Open the docsEnterprise AI control with NetScaler
Use the demo video as a customer-friendly walkthrough of the AI governance story.
Watch the demoBring governance to your AI roadmap.
Talk to the Citrix Asean and South Asia team about where NetScaler AI Gateway fits in your environment, from model routing and token controls to MCP and AI security.