Netskope Skylight AI Security

Unified AI visibility, risk context, and control over every user, prompt, app, agent, and data flow.

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One control plane, four ways AI shows up

Every AI initiative touches the same four surfaces: the people using it, the data feeding it, the models an enterprise hosts itself, and the agents now acting on its behalf. Secure all four from day one to enable your AI strategy.

Schnellere und sicherere KI-Einführung

Netskope Skylight secures AI with a unified approach to accelerating AI discovery, governance, and protection

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Discover AI tools and activity, everywhere

AI Command Center

Every user, app, agent, model, and AI interaction, discovered and inventoried continuously across cloud, endpoint, and network, feeding one picture instead of disconnected tools.

Govern AI risk, confidently

AI Command Center Gen AI App-Sicherheit Agentic Broker AI Red Teaming

Control the data fueling AI models, and gather contextual risk understanding for user, agent, app, model, and MCP servers. Automated adversarial testing finds attack paths in self-hosted models before they become incidents.

Protect AI Interactions

Gen AI App-Sicherheit Agentic Broker AI Gateway AI Guardrails Agent Action Control

Guardrails, threat, and data protection enforcement at runtime. Redaction and blocking stop sensitive data before it reaches a model, agent actions are controlled, adversaries get blocked, self-hosted model testing continues in production, and performance and resiliency scale with AI adoption.

Performance powered by NewEdge and AI Fast Path

Discovery, governance, and protection only work at AI speed if the network underneath does not add latency. NewEdge spans 120+ data centers across 80+ regions, interconnected with 12,000+ network adjacencies to 750+ ASNs, including AWS, Microsoft, Google, Anthropic, and OpenAI. AI Fast Path makes tens of thousands of automated route changes a day to find the fastest path to AI destinations.

Ready to secure enterprise AI

Wherever AI shows up, in an app, a browser, on an endpoint, inside infrastructure you control, or beyond it.

Secure AI users

An employee reaches for a personal AI applications account. A developer connects a coding assistant to a local endpoint model. Both actions are invisible to the security team.

Explore how to secure AI users

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Inline inspection across 1,800+ AI apps tells corporate accounts from personal and shadow AI, while endpoint visibility identifies AI running locally including known AI agents, LLMs and MCP servers.

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Control operates at the account instance level, so approving a tool company-wide does not approve every personal login.

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Real-time user coaching explains the risk to the user at the most effective moment, and points to the approved option, instead of applying an unexplained block.

Secure self-hosted AI

An enterprise fine-tunes its custom LLM for a customer-facing chatbot. No public provider has responsibility for hardening the model. The firm owns the entire threat surface.

Explore how to secure self-hosted AI

Test custom AI models

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Automated red team tests run thousands of malicious prompts against the model to discover weaknesses before an actual attacker does.

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Launching the model requires passing security tests, and authenticated, rate-limited access before go-live.

Sicher

Every call is authenticated, rate-limited, and logged as volume scales. LLM red teaming is continuous, with guardrails moderating prompts and blocking threats

Secure AI agent interactions

An engineering team adopts AI coding agents. Once an agent reaches a public MCP server, it is trusting something no internal team built.

Explore how to control agent actions

Explore how to secure MCP servers

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Scale your autonomous AI with unified visibility and inventory of AI agents and public MCP servers

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Risk assess MCP servers based on its security posture, and classify agent actions by risk level to control what an agent can reach before it executes.

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The moment an action crosses into high-risk territory (such as deleting a repository), it is blocked before it executes.

Secure AI data

AI cannot run without data. That data moves through prompts, responses, agent retrieval, and the pipelines behind model training and retrieval-augmented generation.

Explore how to secure AI data

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Continuous discovery classifies sensitive data across every store feeding a model, before any model training happens.

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One classification standard extends from endpoint, email, and SaaS into prompts, training exports, and retrieval pipelines.

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A match redacts the sensitive detail and lets the task through, instead of a blanket block.

Bechtel’s relationship with Netskope is fantastic. I appreciate that Netskope’s AI innovation is grounded in practical use cases

Patrick Noon, Chief Information and Digital Officer

Bechtel

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We rely on Netskope to secure operations as we future-proof the business through digitalization of professional services and the introduction of AI.

Stuart Walters, Partner and Chief Information Officer

BDO UK

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Roadshow „KI auf der Überholspur“

The Netskope team is coming to a city near you.

See Netskope Skylight AI Security in action and learn how we’re helping enterprises say yes to secure, scalable AI. Meet security leaders solving the same problems you are, and speak with our experts in person.

Finden Sie eine Veranstaltung in Ihrer Nähe

Ressourcen

Möchten Sie die Herausforderungen, denen sich Ihre Organisation gegenübersieht, besser verstehen? Oder sind Sie bereit, mehr über den weiteren Weg zu erfahren? Tauchen Sie in unsere Ressourcen ein, um mehr zu erfahren.

AI Risk and Readiness Report

Vergleichen Sie Ihre Organisation mit den Forschungsergebnissen von Cybersecurity Insiders zu Herausforderungen und Strategien im Bereich der KI-Sicherheit.

Das AI Security Playbook

Explore six core security challenges organizations face when adopting AI, along with proven, real-world strategies to address them.

Checkliste für AI Security

A practical guide to closing the gap between AI adoption and AI governance

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