Fortinet's AI Gateway has arrived
In this blog
AI is already running inside most organizations, whether the security team knows it or not. The pace is set by the business, not by policy. NTT Data research cited by Fortinet found that 69% of CISOs say their teams lack the skills to work with generative AI.
Prompts and model responses are natural language, so they pass straight through firewalls, WAFs, and API gateways without meaningful inspection. Sensitive data leaves in a prompt. A crafted instruction overrides a system message. A runaway agent burns a GPU budget in an afternoon. None of it shows up in traditional controls.
FortiAIGate is Fortinet's AI gateway. It sits in line between your applications and your models, applying guardrails and traffic control to every request and response. One containerized platform combines application delivery with AI runtime security, so security teams get enforcement while development teams keep shipping.
FortiAiGate placement
Traffic in both directions passes through the gateway, which sanitizes input before it reaches the model and inspects output before it returns to the user.
Figure 1: FortiAIGate delivers AI runtime security between applications and models.
Two functions, one gateway
FortiAIFlow optimizes application performance. AI Flow routes via a static value the calling agent supplies. Output caching offloads duplicate work, cutting token and compute cost. It tracks usage and logs every prompt and response for audit.
FortiAIGuard detects and blocks security threats. Guardrails are customized per LLM endpoint to catch prompt injection, jailbreaking, model poisoning, and excessive consumption. A context aware DLP engine in the critical path prevents extraction of PII, sensitive data, and the model itself.
Deployment
Local containerized deployment keeps the footprint light. A built-in Kubernetes ingress controller and load balancer auto-scale with LLM utilization, and multi-GPU and SmartNIC acceleration keep proxy latency low. It runs across cloud, on-premises, and edge in private and hybrid AI environments, and is managed through a built-in GUI with no manual YAML or Python.
Security enforcement
| Capability | What it does |
|---|---|
| Prompt injection detection | Blocks input that overrides system prompts or hijacks model behavior. |
| Jailbreak prevention | Stops attempts to bypass model safety controls and content policies. |
| Model poisoning defense | Identifies input that corrupts model behavior or degrades output quality. |
| MCP tool protection | Guards Model Context Protocol tools against scanning. |
| Context-aware DLP | Blocks PII leakage and model extraction in both directions. |
| Consumption control | Provides visibility into queries designed to exhaust compute or inflate cost. |
| API gateway and DDoS | Standard API-layer protections on the AI traffic path. |
Delivery cost and governance
| Capability | What it does |
|---|---|
| Intelligent model routing | Directs queries to the optimal model on cost, latency, and performance. |
| Token and cost tracking | Admins set cost per token and monitor activity against budget. |
| Full prompt logging | Logs every prompt and response as a complete audit trail. |
| Centralized policy | One set of guardrails across models, clouds, and business units. |
Market alignment
The AI gateway is an emerging control point. It exists because the layers around it were built for a different kind of traffic.
| Control | What it inspects | The AI gap |
|---|---|---|
| Next-gen firewall | Network flows, ports, protocols | Cannot interpret natural-language prompts or model responses. |
| WAF | HTTP requests, injection patterns | Prompt injection is well-formed text. No malformed payload to match. |
| API gateway | Schemas, rate limits, authentication | Enforces structure, not semantics. A valid request can still exfiltrate data. |
| CASB | Sanctioned application usage | Covers shadow AI, not the apps your developers build on models. |
| Traditional DLP | Files, email, endpoints | Not positioned in the prompt and response path. |
FortiAIGate operates one layer up. It understands natural-language interaction and applies AI-specific runtime protections at the layer none of the controls above was designed to inspect.
Position in the Fortinet portfolio
Fortinet classifies FortiAIGate under AI runtime security within its cloud and application security portfolio. It complements adjacent products rather than replacing them:
- FortiAppSec and FortiWeb: unified web application and API protection.
- FortiCNAPP: cloud-native protection for the infrastructure the model runs on.
- FortiAI Protect: defense against AI-powered threats aimed at the enterprise.
- FortiAI Secure: security for AI infrastructure and workloads.
- FortiAI Assist: generative AI applied to SOC and NOC operations.
Customer use cases
| Use case | What FortiAIGate does |
|---|---|
| Internal AI assistant | DLP stops customer records and credentials going into the copilot and stops sensitive data leaving in a response. Every interaction is logged. |
| Customer-facing AI app | Endpoint guardrails plus DDoS and API protection defend a public AI feature against prompt injection, jailbreaking, and model extraction. |
| Multi-model governance | One enforcement and monitoring point across models and clouds, so policy is consistent without forcing teams onto a single model. |
| AI cost control | Caching, optimal routing, and consumption limits stop runaway agents inflating the bill. Admins set cost per token and monitor against budget. |
| Audit and compliance | FortiAIGate does full logging and supplies the evidence necessary for you to to perform framework mapping, audit and compliance. |
| Agentic AI and MCP | Agents acting on tools widen the blast radius. FortiAIGate safeguards MCP tools against scanning and guards the agent path. |
How WWT helps
Buying an AI gateway is straightforward. Knowing where to put it, what to enforce, and how it behaves under your traffic is not. World Wide Technology closes that gap.
- Advisory: We map where AI already lives against your risk appetite, regulatory obligations, and existing architecture, then produce a prioritized roadmap.
- ATC testing: Evaluate FortiAIGate in the WWT Advanced Technology Center first. Test guardrails with realistic prompts, measure latency under load, and compare alternatives side by side.
- Design: Where the gateway sits, how it integrates with identity, logging, and SOC tooling, and how policy is structured across business units.
- Deployment: Implementation at scale, then policy tuning, guardrail refinement, and capacity planning as usage grows.
Next steps
Start by finding out where AI traffic already flows in your environment and how much of it passes through no inspection at all. That inventory usually settles the question of urgency on its own. From there, bring one real use case to WWT for us to evaluate your environment and determine where the FortiAIGate can help close this gap. Contact your WWT account team to scope the session.