HiddenLayer vs Trussed: Adversarial ML Defense vs Runtime Governance
HiddenLayer and Trussed are not direct substitutes. HiddenLayer operates in the adversarial ML defense category, focused on detecting attacks against a model's inputs and outputs. Trussed operates in the runtime governance category, focused on enforcing identity, permissions, and policy over what AI agents are allowed to do once a decision is made. Most enterprises running autonomous or tool-calling agents need both layers, not one in place of the other.
Two Layers of the AI Security Stack
Adversarial ML defense and runtime governance address different points in the lifecycle of an AI system. The table below summarizes what each layer covers and why enterprises running agentic systems typically need both.
| Layer | What it covers |
|---|---|
| Model-layer defense | Detects attacks aimed at the model itself, such as evasion, malicious payloads, or extraction attempts against inputs and outputs. |
| Runtime governance | Enforces identity, permissions, and policy over what an AI agent is authorized to do after a decision is made. |
| Combined coverage | Agentic AI deployments typically require both layers, since each addresses a different point in the system lifecycle. |
Two Distinct Security Categories, Not Two Competing Vendors
HiddenLayer and Trussed are frequently placed in the same procurement conversation, but they address different attack surfaces. Adversarial ML defense, the category HiddenLayer sits in, is concerned with the model itself: whether an input is crafted to evade detection, whether an output reveals information it should not, or whether a pattern of queries indicates an attempt to extract the model.
Runtime governance, the category Trussed operates in, is concerned with what happens after a model or agent has made a decision: whether it is authorized to call a given tool, access a given API, or take a given action. Treating these as interchangeable line items in an RFP creates coverage gaps, because a control that inspects model input and output does not, by design, evaluate whether an agent's subsequent action was permitted.
Where Agentic AI and MCP Environments Require Both Layers
Static inference deployments, where a model responds to a request and the interaction ends, have a threat surface concentrated at the model's input and output boundary. Adversarial ML defense is built for exactly that surface.
Agentic systems change this picture. When an agent calls external tools, queries other services, or coordinates with other agents through protocols such as Model Context Protocol, it introduces a control surface that model-layer defense does not typically evaluate: which agent is acting, what permissions it holds, which tools it is approved to call, and whether that action should be logged for audit purposes. This is the surface runtime governance is built to control.
An organization running only model-layer defense on an agentic deployment can detect a manipulated input, but has no mechanism to confirm that the resulting agent action stayed within its permitted scope. An organization running only runtime governance without model-layer defense can enforce what an agent is allowed to do, but has no visibility into whether the model itself is being manipulated at the input or output level. The two categories close different gaps.
Frequently Asked Questions
Are HiddenLayer and Trussed direct competitors?
No. They sit in different security categories. HiddenLayer addresses adversarial ML defense at the model layer. Trussed addresses runtime governance at the agent action layer. Most enterprises evaluate them for different parts of the architecture rather than as substitutes for one another.
Map Your AI Agent Governance Surface
Before deciding between adversarial ML defense, runtime governance, or both, clarify which layer of your AI architecture carries the risk. If your organization runs autonomous or tool-calling agents, runtime governance addresses a control surface that model-layer defense was not built to cover.
Explore Runtime Governance