What is NetScaler AI Gateway?
NetScaler AI Gateway provides visibility and control needed to govern AI usage, prevent data exposure, and eliminate shadow AI
Shadow IT has been a challenge for enterprise IT teams for years. Employees find tools that work for them such as cloud storage, SaaS applications, unapproved collaboration platforms, and use them without IT’s knowledge, creating risks that are invisible until something goes wrong.
Shadow AI is the same problem, amplified. And in 2026, it’s arriving faster than most organisations are ready for.
The shadow AI problem
The adoption of AI tools across enterprise organisations has been rapid, largely organic, and in many cases entirely ungoverned. Employees are using publicly available large language models (LLMs) to draft communications, summarise documents, write code, analyse data, and accelerate workflows. The productivity gains are real. The risks are equally real, and significantly less visible.
When an employee pastes a customer contract into ChatGPT to get a quick summary, that data has left the organisation. When a developer uses an AI coding assistant to work through a problem involving proprietary logic, the context they provide doesn’t stay local. When a finance team member shares a spreadsheet with a generative AI tool to speed up analysis, the contents of that spreadsheet have potentially been processed on infrastructure the organisation doesn’t control and hasn’t assessed.
This isn’t a theoretical concern. It’s happening in enterprise organisations right now, at scale, largely without IT or security teams knowing. It is, in every meaningful sense, shadow AI.
What is NetScaler AI Gateway?
NetScaler AI Gateway is a purpose-built capability within NetScaler that brings the same principle of intelligent, policy-driven traffic inspection to AI interactions that NetScaler has long applied to application traffic.
Just as NetScaler sits between users and applications, inspecting, filtering, and enforcing policy on every transaction, AI Gateway sits between users and AI services. Every request to an LLM, every prompt sent to an external AI API, passes through it. That position provides something that has been entirely absent from most organisations’ AI deployments so far: visibility and control.
What NetScaler AI Gateway does
- AI traffic discovery and visibility. Before you can govern AI usage, you need to know what’s happening. AI Gateway provides a complete view of which AI services are being accessed across the organisation, by whom, how often, and for what apparent purpose. Shadow AI tools being used without IT awareness become visible in the same way that uberAgent exposes shadow IT at the endpoint level. The hidden becomes auditable.
- Data loss prevention for AI prompts. AI Gateway inspects the content of prompts before they reach external AI services. Sensitive data patterns such as personal identifiable information, financial data, credentials, intellectual property markers, can be detected and acted on in real time. Prompts can be blocked, redacted, or flagged for review before data leaves the organisation. For companies operating under GDPR, ISO 27001, or sector-specific data handling requirements, this is a meaningful compliance control that most organisations currently have no equivalent of.
- Policy-based AI access control. Not all AI tools are appropriate for all users or all contexts. AI Gateway allows organisations to define and enforce granular policies: which AI services different user groups can access, what types of content can be submitted, and what volume of AI interactions is permitted. Teams can be granted access to approved, vetted AI services whilst access to unapproved alternatives is blocked or logged.
- Prompt injection protection. As AI systems become more embedded in enterprise workflows, they become targets. Prompt injection attacks, where malicious input is crafted to manipulate an AI model’s behaviour, bypass its safety controls, or extract sensitive information from connected systems, are an emerging and growing threat. AI Gateway detects and blocks injection attempts before they reach the model.
- Semantic caching. AI API calls carry both a processing cost and a financial one. Semantic caching identifies requests that are functionally equivalent to previous queries and returns cached responses rather than generating new ones, reducing both latency and API expenditure. For organisations where AI usage is growing rapidly, this has a direct impact on the cost of scaling.
- Audit logging and compliance reporting. AI Gateway maintains a full, searchable audit trail of AI interactions. For organisations that need to demonstrate compliance with data handling policies, or that need to investigate an incident involving AI-processed data, this provides the evidential record that currently doesn’t exist in most environments.