F5 AI Gateway vs Trussed AI: Network Control or Governance Evidence?

What each approach controls

An AI gateway sits in the traffic path. In the way the category is commonly described, it routes requests across providers, applies input and output security controls, and offers centralized logging, policy management and usage visibility. F5 AI Gateway belongs to this network and traffic control category. Its natural strengths are the controls network teams already understand: routing, inspection, authentication and rate limiting applied to AI traffic.

Runtime policy enforcement operates at a different scope. Trussed enforces policy in the execution path across models, agents, tools, MCP servers, AI sites and coding assistants. The unit of control is the individual interaction or tool call, not the connection or request stream. The available actions are allow, redact, block, rate limit and escalate for human approval, and they are enforced at runtime, not reviewed after the fact.

The term "AI gateway" is not uniform. Some products route LLM traffic with security controls, some are network-layer appliances, and some connect agents to enterprise applications. Define the scope of each candidate before comparing it with anything else.

Side-by-side comparison

The table contrasts the two approaches on the dimensions that matter most for regulated enterprise buying decisions. It describes the categories, not a feature-by-feature scorecard.

• Primary layer: Network-layer AI gateway: the traffic path. Trussed: the AI execution path.
• Unit of control: Gateway: requests, responses and connections. Trussed: individual interactions and tool calls.
• Typical scope: Gateway: traffic to and from models. Trussed: models, agents, tools, MCP servers, AI sites and coding assistants.
• Actions: Gateway: routing, inspection, authentication and rate limiting on traffic. Trussed: allow, redact, block, rate limit and escalate for human approval.
• Evidence produced: Gateway: traffic-level logging and usage visibility. Trussed: audit-ready evidence generated by each enforcement decision, mapped to HIPAA, NAIC, NYDFS, EU AI Act and NIST.
• Deployment: Gateway: depends on the product and its network placement. Trussed: self-managed in the customer cloud, with configurable retention and data residency.

Where traffic control ends and interaction-level enforcement begins

A gateway sees what crosses the network boundary it sits on. That is valuable: it can authenticate callers, inspect prompts and responses, and limit volume. But an agent invoking a tool, a coding assistant writing to a repository, or an MCP server returning data may not present itself as a distinct, policy-relevant event at the traffic layer. OWASP's own taxonomy reflects this split, listing agent-to-tool and agent-to-API policy enforcement as a separate control category from gateway-style traffic controls.

The practical test is whether a control can make a decision about one specific action and apply a specific outcome. Redacting a field from one tool response, blocking one call while allowing the next, or holding a call for human approval are interaction-level decisions. Rate limiting appears in both approaches, but the meaning differs. A gateway limits traffic volume for a client or route. Runtime enforcement can limit a particular interaction or tool call under a policy.

This is a difference of scope, not of quality. A traffic-level control that is excellent at its job still does not become an interaction-level control.

What evidence each approach leaves behind

The central question for audit is what record remains. Centralized logs and usage visibility describe traffic: who called what, when, and how much. That is useful for operations and security review. Whether it also serves as audit evidence depends on whether the record captures the enforcement decision itself, such as allowed, redacted, blocked, rate limited or escalated, and under which policy.

Trussed generates evidence as a direct product of enforcement, so each decision made in the execution path is recorded as it happens. That evidence is mapped to HIPAA, NAIC, NYDFS, the EU AI Act and NIST. Retention and data residency are configurable, and deployment is self-managed in the customer cloud, so the evidence stays where the regulated enterprise decides it should.

On the regulatory side, the EU AI Act text refers to principles including human agency and oversight, transparency and accountability, as of October 2026. NIST also publishes a Generative AI Profile of its AI Risk Management Framework. Mapping any tool to specific articles or controls should be done against the full texts with compliance counsel, not assumed from a product category.

Where each approach fits, and how they combine

Choose a network-layer gateway when the main requirement is managing and securing AI traffic: consistent routing, authentication, inspection and volume limits at the network boundary. Choose runtime policy enforcement when the requirement is to show a regulator or auditor what AI was allowed to do and what it did, across the models, agents and tools in use.

The two are not mutually exclusive. A gateway can continue to handle traffic while enforcement in the execution path decides on individual interactions and tool calls and records the result. The risk to avoid is selecting a traffic control and assuming it satisfies evidence and policy requirements it was not designed to meet.

For context on Trussed in production: it has run continuously in a regulated enterprise for more than 18 months, with 99.95% uptime on live production traffic, more than 100 models under one policy plane, and over 30 GB of audit evidence generated per day. Customers span insurance, pharma, healthcare and education. Trussed holds SOC 2 Type II, HIPAA, GDPR, FERPA, ISO 27001 and NIST AI RMF credentials.

Trussed does not currently provide transaction-level governance, model lineage or end-to-end tracing of agentic workflows. Include those needs in your criteria if they apply, and assess them separately.