Approach · AIG
Standalone AI security layer: AI gateways and runtime guardrails
In short
A standalone AI security layer sits in front of models, AI apps and agents, usually as an AI gateway that requests are routed through or as runtime guardrails that inspect prompts and responses. It fits when you build AI apps or run agents that call models and tools from servers rather than from employees' browsers. All six vendors here describe one, often as a product separate from their SASE service.
By The Inline Desk · GenAI Security Finder · Published 2026-10-26
Desk research from the six vendors' own public pages, read on 7 October 2026. Profiles follow our published method; we did not run the products.
- SSE · Security cloud (SASE or SSE)
- AIG · AI gateway and guardrails
- FWG · Firewall or self-hosted gateway
- Filled square: described on the vendor pages we read
- Hatched square: partly described (a narrower scope, or the enforcement point is not stated)
- Dashed square: not in our sources (it may exist; we did not find it)
What does it protect?
AI applications you build and the agents that act for them. Typical controls named on the vendor pages we read: blocking prompt injection and jailbreaks, filtering sensitive data in prompts and responses, governing which tools an agent may call, and red teaming the app before release.
How is it different from controls in the security cloud?
The security cloud follows people; the gateway follows applications. A prompt typed by an employee passes the security cloud. A prompt sent by a customer-facing chatbot to a model, or a tool call made by an agent running on a server, passes the gateway. Some vendors run both from one engine and console; others sell them as separate products. Each profile records what the vendor states.
What should you ask a vendor?
- How do requests reach the gateway: SDK, proxy, API routing or a network path?
- Which attacks does the runtime layer detect, and can it block, rewrite or only log?
- Does it inspect MCP tool calls and agent-to-agent traffic?
- Is it managed in the same console as employee AI controls?
- Where are prompts and logs stored?
Vendors that describe it
- Cato NetworksDescribed
Protects homegrown AI apps, blocking prompt injection and jailbreaks, through the Cato cloud or the AI gateway you already run.
Source: Cato Networks: AI security · Read 7 Oct 2026
- CiscoDescribed
AI Defense includes AI Runtime Protection, AI Model and Application Validation, AI Cloud Visibility, AI Access and AI Supply Chain Risk Management.
Source: Cisco AI Defense · Read 7 Oct 2026
- FortinetDescribed
FortiAIGate secures and governs AI apps and agent traffic at runtime from one control point.
Source: Fortinet: FortiAIGate · Read 7 Oct 2026
- NetskopeDescribed
An AI Gateway for API traffic between custom-built apps and LLMs, and AI Guardrails as a dedicated runtime defense layer.
Source: Netskope: AI Guardrails · Read 7 Oct 2026
- Palo Alto NetworksDescribed
Prisma AIRS includes an AI Gateway, AI Runtime Security, AI Red Teaming, AI Posture Management and Agent Identity Security.
Source: Palo Alto Networks: Prisma AIRS · Read 7 Oct 2026
- ZscalerDescribed
An AI Gateway routes, secures and governs every AI transaction, with inline DLP applied to every model, agent and API call.
Source: Zscaler: AI security · Read 7 Oct 2026
Questions
Is a standalone layer a separate product?
Often. Palo Alto Networks describes Prisma AIRS as its own platform and Cisco offers AI Defense next to Secure Access. Cato states one engine and one console cover users, applications and agents.
Do I need one if we do not build AI apps?
Less often. If your AI use is mostly employees using third-party AI tools, start with controls in the security cloud. Agents and homegrown apps are what make a gateway layer necessary.
What is red teaming in this context?
Testing an AI app or agent with attack prompts before and after release. Cato, Cisco, Netskope, Palo Alto Networks and Zscaler describe AI red teaming on the pages we read.