Netskope One

AI Gateway

As you build AI-powered applications, data risk shifts from human prompts to autonomous app-to-LLM API calls. Netskope One AI Gateway secures this modern ecosystem providing the visibility and control needed to scale your AI innovation securely.

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Secure your AI-powered applications

Netskope One AI Gateway delivers localized control and inspection for your AI-powered applications. Secure critical API traffic between private applications and LLMs, whether public or privately hosted, by centralizing authentication, traffic management, and content inspection to ensure autonomous data flows remain secure and governed.

Build AI-powered apps with confidence
features and benefits

Your high-performance control point for private AI environments.

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Centralized AI control

Manage fragmented ecosystems of custom LLMs and agents through a single control point. Unify management across OpenAI, Google Gemini, and Anthropic Claude while enforcing consistent traffic and authentication policies.

Full content inspection

Integrate real-time AI guardrails, DLP, and threat protection all within a single gateway. Ensure every prompt and response is moderated for safe use, and to prevent jailbreaking, prompt injection, and sensitive data exposure.

Secure agent authentication and logging

Ensure only authenticated agents communicate with LLMs using unique gateway tokens. Maintain searchable API logs to ensure no interaction bypasses security controls.

Optimize reliability

Maintain application stability by tracking AI consumption and enforcing rate limits. Prevent resource abuse and manage the frequency of requests to ensure your AI-powered services remain resilient.

Flexible deployment

Deploy a lightweight, high-performance virtual appliance exactly where your models reside. Whether in AWS VPCs or VMware ESXi, our software-defined layer brings security up to par with web and SaaS standards.

Ensure only authenticated agents communicate with LLMs using unique gateway tokens. Maintain searchable API logs to ensure no interaction bypasses security controls.

Maintain application stability by tracking AI consumption and enforcing rate limits. Prevent resource abuse and manage the frequency of requests to ensure your AI-powered services remain resilient.

Netskope One AI Gateway use cases

Build AI-powered apps with confidence

Secure the app-to-LLM traffic that never hits the public network. The gateway acts as a localized inspection point for models hosted privately on AWS or VMware to ensure the data that stays within your perimeter is covered by security policy.

Secure agentic workflows

Protect autonomous agents from exploitation as they interact with sensitive tools and data. Use granular access controls and rate limiting to ensure non-human interactions don't become a new attack surface.

Multi-model governance

Manage interactions across multiple providers including OpenAI, Google Gemini, Anthropic Claude as well as custom models with a unified API entry point following the API schema for these models.

Enhanced SecOps efficiency

Integrate AI guardrails, DLP, and threat protection, to map content inspection detections to MITRE ATLAS and OWASP Top 10 for LLMs. This unified view reduces investigation time and aligns teams with the latest TTPs.

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FAQs

What is an AI gateway?

An AI gateway is a dedicated software layer designed to secure the critical API traffic that fuels modern AI-powered applications. It acts as a centralized control point for authentication, traffic management, and content inspection between your private applications, autonomous agents, and the Large Language Models (LLMs) they communicate with, whether those models are privately hosted or public. Netskope One AI Gateway is an example of this approach, and integrates with the wider Netskope One platform for unified data security.

Do I need an AI gateway if I have other security in place already?

Traditional security proxies are built for human-to-app traffic, meaning these autonomous "app-to-LLM" interactions often bypass standard security entirely. An AI gateway such as Netskope One AI Gateway specifically bridges this gap by securing non-human, agentic workloads.

How do I ensure my private AI models and workflows are secure?

To ensure your private AI models and workflows are secure, you need to implement a strategy that addresses the unique risks of internally hosted AI (models on AWS Bedrock, Google Vertex AI, or Azure) and the non-human, machine-to-machine traffic that powers them. You should look to include the following steps and capabilities into your security strategy:

Harden models before deployment with adversarial simulation capabilities such as those found in Netskope One AI Red Teaming.
Centralize traffic management and authenticate locally using an AI gateway like the Netskope One AI Gateway.
Apply real-time runtime defense with integrated AI guardrails and modern DLP, such as that provided within the Netskope One platform.
Secure autonomous agentic workflows in public applications supporting MCP with an agentic broker like the Netskope One Agentic Broker.

How does an AI gateway integrate with my existing security stack?

If you choose an AI gateway that is part of a unified data security platform then it will integrate with your existing security stack by acting as a centralized software layer that embeds security directly into your internal traffic path. For example, the Netskope One AI Gateway seamlessly connects with broader data security and Security Operations (SecOps) tools in the following ways:

Direct integration with Netskope One DLP and Threat Protection so that you can apply centralized policy detections to your app-to-LLM API traffic, preventing sensitive intellectual property exposure and blocking AI-specific exploits.
Integration with Netskope One AI Guardrails to provide real-time content moderation, protection against AI-specific threats including prompt injection and jailbreaking attempts, and to enforce safe AI usage boundaries for your autonomous agents and applications.
Alignment with SecOps frameworks including MITRE ATLAS and the OWASP Top 10 for LLMs to provide security teams with a single, cohesive view that reduces investigation time and aligns with the latest attacker techniques (TTPs).
Integration into your existing private infrastructure by deploying as a lightweight virtual appliance (VM) directly within your privately-hosted environments, such as AWS VPCs or VMware ESXi. This ensures that your internal app-to-LLM traffic remains governed locally and consistent security policies are enforced without needing to route traffic to public cloud control points.

How does an AI gateway help with compliance?

An AI gateway such as the Netskope One AI Gateway helps organizations align with industry-standard security frameworks and compliance requirements by securing autonomous "app-to-LLM" traffic that often bypasses traditional perimeters. These include:

OWASP Top 10 for LLMs & MITRE ATLAS: The gateway maps content inspection detections to these specific AI threat frameworks. It identifies and mitigates risks, such as prompt injections and insecure output handling, by embedding security directly into the traffic path. This reduces investigation time and aligns security teams with the latest Tactics, Techniques, and Procedures (TTPs).
Regulatory compliance and auditing: The Netskope One AI Gateway captures and maintains a fully searchable audit log of all API calls and interaction content. This detailed logging is essential for satisfying strict regulatory compliance requirements and monitoring internal usage patterns.
Centralized authentication and control: The gateway manages authentication and traffic behind a single interface, ensuring that only authenticated agents can communicate with LLMs via unique gateway tokens. This guarantees that no interaction bypasses established security controls.
Availability and NIST Cybersecurity Framework (CSF): By managing traffic volume, the AI Gateway supports the "Protect" and "Recover" functions of frameworks such as NIST CSF. It implements rate limiting on the volume and frequency of API requests to prevent resource abuse and ensure application stability and reliability.