Javelin vs Trussed AI: AI Gateway Security or Runtime Governance?

Two architectures, two control points

The choice between Javelin and Trussed AI is best treated as an architectural question: where is control applied, and what evidence does that placement produce?

A gateway-based architecture places a proxy between applications and models. Prompts and responses pass through it, so policy can be applied to that traffic. Its coverage is defined by what is routed through it. If every model call is forced through the gateway, it gives a single and manageable point of inspection.

Policy enforcement in the execution path applies controls where the AI acts. That includes model calls, but also the tool calls and API actions an agent makes, interactions with MCP servers, use of AI sites and coding assistants. The OWASP Gen AI Security Project describes this pattern as a runtime enforcement layer that intercepts tool calls and API actions at execution time, applying policy, intent validation and zero-trust authorization to stop unsafe or out-of-scope operations.

Terminology adds confusion. OWASP lists separate taxonomy categories for LLM guardrails and for runtime guardrails, moderation and anomalous tool use, and vendors appear under overlapping labels. Category names are a weak basis for evaluation. Map each product to named enforcement points instead.

How the two approaches differ at the enforcement point

The table describes the architectural patterns in general terms. Individual products vary within each pattern, so each row should be tested against the specific product.

• Primary control point: Gateway: traffic between applications and models. Execution path: individual interactions and tool calls wherever the AI acts.
• Model calls: Gateway: inspected when routed through the gateway. Execution path: inspected in the path alongside other actions.
• Agent tool calls and MCP servers: Gateway: visible only if the tool traffic is routed through it. Execution path: policy applies at the point the tool call is made.
• AI sites and coding assistants: Gateway: often outside the application-to-model route. Execution path: treated as enforcement points to be covered.
• Enforcement actions: Gateway: varies by product. Execution path: should support allow, redact, block, rate limit and escalate for human approval.
• Resulting evidence: Gateway: a record of traffic that crossed it. Execution path: a record of each decision and action enforced.

Javelin and Trussed AI in this comparison

Javelin is evaluated here as an AI gateway offering, which makes it a representative of the gateway-centric pattern. Gateway products differ in what they cover, so the criteria in this article should be applied to Javelin's current documentation rather than assumed from the category.

Trussed AI is positioned as runtime control for securing AI, not as a gateway. It enforces policy in the execution path across models, agents, tools, MCP servers, AI sites and coding assistants. The available actions are allow, redact, block, rate limit and escalate for human approval, and the scope is individual interactions and tool calls. Trussed also provides code provenance, covering the origin, licence and security context of code.

On deployment, Trussed is self-managed in the customer cloud, with configurable retention and data residency. For a regulated enterprise, that determines where enforcement data lives and who operates the control.

The tradeoff is breadth against simplicity. A gateway is a narrower control that can be simpler to reason about when model traffic is already centralized. Enforcement across more points covers more of what AI does, and it requires policy design across more kinds of action.

What auditors and regulators will ask for

The practical test is whether you can answer two questions: what was the AI allowed to do, and what did it do? A traffic record can show what crossed a gateway. It does not by itself show a tool call made by an agent, an escalation to a human, or a blocked action elsewhere.

Frameworks point to the same need. As of October 2026, the EU AI Act (Regulation (EU) 2024/1689) states in recital 66 that requirements for high-risk AI systems cover risk management, data set quality and relevance, technical documentation and record-keeping, transparency, human oversight, and robustness, accuracy and cybersecurity. Whether a given system is high-risk determines how these apply.

NIST guidance on agentic systems describes organizations defining risk thresholds that trigger human review, and technology that can escalate to the correct authority. It also describes scopes narrowing as authority is delegated to sub-agents or downstream services. These ideas are useful design tests, though they are guidance and not binding requirements. The NIST AI RMF Generative AI Profile is a common reference for framework mapping.

Trussed generates continuous audit-ready evidence from enforcement itself, mapped to HIPAA, NAIC, NYDFS, the EU AI Act and NIST. Evidence that is a by-product of enforcement is tied to decisions actually taken at runtime, which is different from a record assembled afterward.

When each approach fits

A gateway approach is a reasonable fit when AI use is limited to applications calling models, all of that traffic can be forced through one point, and the main requirements are traffic-level inspection and policy on prompts and responses. This is a general architectural judgment, and it should be confirmed against your own estate.

Requirements extend beyond a gateway when agents call tools, MCP servers are in use, employees use AI sites or coding assistants, or an auditor expects evidence of actions as well as traffic. In those cases the control has to sit where the actions occur, and the evidence has to come from that same point.

The two are not mutually exclusive in principle. The decision is which enforcement points you must cover and which evidence you must be able to produce.

Trussed has run continuously in production at a regulated enterprise for more than 18 months, with 99.95% uptime on live production traffic, 100+ models under one policy plane, and 30+ GB of audit evidence generated per day. It serves customers in insurance, pharma, healthcare and education, and holds SOC 2 Type II, HIPAA, GDPR, FERPA, ISO 27001 and NIST AI RMF credentials. If your requirements match the second scenario, use the criteria above to test it against your environment.