Protect AI vs Trussed: ML Supply Chain Security vs Runtime Governance

Two Stages of the AI Lifecycle

Enterprise AI risk spans a boundary that runs from artifact validation to live agent behavior. The table below outlines where each stage sits.

Why This Comparison Gets Conflated

Enterprises adopting AI and ML pipelines routinely encounter two security problems that sound similar but operate on different sides of a deployment boundary. The first is ML supply chain security: verifying that the models, datasets, and dependencies entering a pipeline are what they claim to be, free of known vulnerabilities, and traceable to a legitimate source. The second is runtime governance: controlling what an AI agent is permitted to do once it is live, including which tools it can call, what data it can access, and under what identity it operates.

Protect AI and Trussed are frequently placed side by side in vendor evaluations, but they are not solving the same problem. Protect AI is positioned in the ML supply chain security category. Trussed operates as a runtime governance layer for deployed AI agents. Understanding this distinction matters more than ranking the two platforms, because most enterprise AI risk exposure comes from gaps between lifecycle stages, not from a single vendor's feature list falling short.

Lifecycle Stage Comparison

Placed side by side, the two categories answer different questions at different points in the lifecycle.

What ML Supply Chain Security Covers

ML supply chain security tooling, the category Protect AI is generally associated with, operates before an AI system reaches production. Its purpose is to answer a narrow but important question: can the components entering an ML pipeline be trusted. In practice, this category of tooling typically involves scanning model files for embedded malicious code, checking datasets and dependencies for known vulnerabilities, and establishing some form of provenance record for how an artifact was produced and where it came from.

This work resembles software supply chain security applied to ML artifacts rather than application code. The scope is inherently pre-deployment. These platforms are not designed to observe or restrict what a model or agent does after it is running in production, because that is a separate control point requiring visibility into live agent behavior, not static artifact analysis. Buyers should confirm directly with any vendor in this category whether their platform extends into any runtime monitoring, since the boundary is not always identical across products.

What Trussed's Runtime Governance Layer Covers

Trussed operates on the other side of that boundary, after an agent has been deployed and is actively operating. Its scope centers on runtime policy enforcement: establishing an identity for each agent, defining what permissions that agent holds, and enforcing least privilege so an agent can only access the data and tools its task requires. This includes tool approval workflows that govern whether a specific tool call is permitted before it executes, along with runtime monitoring and audit logging so that agent actions are recorded and reviewable after the fact.

Trussed's runtime governance also extends to agent-to-agent security, where one agent's actions or requests toward another agent need the same identity and permission checks as a human-to-system interaction would require. A defining characteristic of this category is that it assumes the model or agent is already deployed and asks a different question than supply chain scanning does: not whether the artifact was safe to bring in, but whether the specific action being attempted right now is authorized.

Coverage Gaps and the Role of MCP

The practical risk for enterprises is not choosing the wrong vendor category, it is assuming one category covers a gap that only the other addresses. A model that passes every pre-deployment scan can still be misused at runtime if the agent using it has excessive permissions or no tool-call restrictions. Conversely, a tightly governed runtime layer cannot detect a compromised or tampered model artifact before it is ever loaded, because that is a supply chain concern, not a runtime one.

Model Context Protocol, a publicly documented protocol for standardizing how AI models and agents connect to external tools and data sources, has made this boundary more consequential over the past year. As agents increasingly rely on standardized tool-call interfaces, the question of which tool calls an agent is authorized to make, and under what identity, becomes a runtime governance requirement rather than a pre-deployment scanning concern. MCP security, in this context, refers to enforcing permissions and approval controls at the point where an agent actually invokes a tool, which is a runtime function regardless of how well the underlying model was vetted before deployment.

Questions to Ask Before Choosing One, Both, or Neither

Before evaluating either category further, it helps to make the boundary between them concrete:

• Which lifecycle stage, pre-deployment or post-deployment, carries the most unmanaged risk in your environment today?
• Does your current tooling extend into runtime monitoring, or does its coverage end once an artifact is deployed?
• Are agent identity, permissions, and tool-call approvals enforced at runtime, or only assumed based on pre-deployment vetting?
• Could a compromised model artifact bypass your runtime controls simply because it was never flagged during scanning?
• Does agent-to-agent interaction in your environment receive the same identity and permission checks as human-to-system access?