Fortinet has introduced the FortiGuard Inline AI Security Service, a cloud-delivered solution that provides FortiGate customers with AI-specific inspection and security guardrails. The service helps organizations gain greater visibility into AI usage while preventing AI-related threats and data exposure through their existing Fortinet infrastructure. Powered by continued advancements in FortiAIGate, the new capabilities include high-performance AI guardrails, enhanced access and compliance controls, visibility into shadow AI and AI agents, and technology gained through Fortinet’s recent acquisition of Virtue AI.
Fortinet’s broad FortiGate footprint gives organizations a simpler path to adopt advanced AI security through the infrastructure they already use. Customers can enable the FortiGuard Inline AI Security Service without deploying and managing separate AI security infrastructure, making advanced AI security accessible to a broader set of organizations. Customers with more advanced security, privacy, and sovereignty requirements can deploy FortiAIGate directly in their own environment for greater control over how and where AI traffic and data are processed.
“Organizations need AI security that can scale with how quickly AI is being adopted across the enterprise,” said Michael Xie, Founder, President, and Chief Technology Officer at Fortinet. “By extending advanced AI inspection and guardrails through FortiGate, we are making AI security available through the infrastructure customers already use. FortiAIGate provides the foundation for this broader strategy, giving customers flexibility in how they deploy and consume AI security based on their cost, performance, privacy, and sovereignty requirements.”
Cloud-Delivered AI Security through Fortinet Infrastructure
With the FortiGuard Inline AI Security Service, FortiGate customers gain deeper visibility into AI activity and stop AI-specific threats and sensitive data exposure through infrastructure they already use. FortiAIGate-based guardrails inspect prompts and data in real time and enforce security, compliance, and data protection policies across interactions with agents, large language models (LLMs), and Model Context Protocol (MCP) tools.
The service goes beyond existing Fortinet capabilities for discovering shadow AI usage and applying data loss prevention (DLP) policies to AI traffic. It extends protection beyond identifying AI use and sensitive data movement to inspecting the content of AI interactions and masking sensitive data without adding new infrastructure to deploy and manage.
Context-Aware AI Security for Applications and Agents
With AI moving into production, LLMs and autonomous agents gain access to sensitive data, business applications, and critical workflows. Traditional security tools inspect IPs, signatures, and payloads, but they do not assess prompts and responses for potential threats. AI-specific threats hide in prompts, model responses, and MCP tool communications and can trigger harmful tool calls, API calls, and autonomous agent actions.
FortiAIGate addresses these risks by providing a real-time control point between AI applications and models, analyzing interactions, and enforcing policy in real time based on the model, application, user role, and business context. Organizations can:
- Stop AI attacks and data leaks in real time: Apply multilayered AI guardrails across prompts, model responses, MCP tool calls and responses, API activity, and agent actions. Dynamic inspection covers text, images, audio, video, and code, delivering industry-leading low latency and minimal false positives. Expanded DLP detects 35 types of personally identifiable information (PII) and confidential data, allowing organizations to alert, deny, or redact that sensitive information before exposure.
- Enforce usage policies and control AI spending: Set model permissions, budgets, spend limits, and rate limits by organization, workspace, project, or user. Administrators are alerted as spending nears a limit and requests are blocked once spending exceeds it. Malicious prompts and Denial-of-Wallet attacks are blocked before they consume tokens or GPU compute. Intelligent routing directs each request to the right model based on content, policy, and cost, with no application re-architecture.
- Govern AI across teams: Dedicated workspaces give teams and business units their own policies, models, logs, roles, and permissions under centralized visibility and control.
- Simplify compliance and auditability: Add more than 50 predefined policies with one click, spanning AI governance frameworks, data privacy laws, industry regulations, and security frameworks. Customize each policy, and give auditors detailed logs of every prompt, output, and tool call.
- Gain visibility into AI agents: See which agents are operating across the business and how they interact with enterprise applications, APIs, tools, data, and MCP servers.
- Maintain data sovereignty and deployment flexibility: Self-host FortiAIGate on-premises or in your cloud, on Kubernetes with NVIDIA GPUs. Prompts, responses, data, and logs stay inside your environment.
Reducing AI Security Complexity for Enterprises
Across cloud-delivered and self-hosted deployment options, Fortinet gives organizations a more unified approach to securing AI without adding disconnected tools and operational complexity.
“FortiAIGate takes an elegant approach to a complex problem,” said Will Townsend, Chief Analyst at LoneStar Advisory & Research. “Rather than asking customers to manage separate tools for AI security, governance, cost, and agent visibility, Fortinet is bringing these capabilities together in a single gateway. The rapid integration of Virtue AI technology further strengthens that story by improving the guardrails and performance customers need as they move AI into production.”
Together, FortiAIGate and the FortiGuard Inline AI Security Service connect AI inspection and runtime controls with Fortinet capabilities spanning network, application, cloud, data, identity, and security operations. Organizations gain a more consistent approach to managing AI risk across users, applications, models, agents, and infrastructure while meeting performance, privacy, regulatory, and sovereignty requirements.
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