Comparison

Lakera vs Trussed: AI Firewall vs Runtime Governance

An AI firewall and runtime governance address different control points. A Lakera-style AI firewall inspects prompts and model outputs for risks such as prompt injection, data leakage, and policy-violating content at the request and response layer. Trussed AI focuses on runtime governance for enterprise AI agents, including policy enforcement, runtime monitoring, agent identity, permissions, least privilege, MCP security, tool approval workflows, and audit logging. For production agent deployments, the key question is not which category is universally better. It is whether the risk is content manipulation, agent action authorization, or both.

Request a Demo Talk to an Expert

Why this comparison matters

Trussed AI focuses on runtime governance for enterprise AI agents, including policy enforcement, runtime monitoring, agent identity, permissions, least privilege, MCP security, tool approval workflows, and audit logging.

For production agent deployments, the key question is not which category is universally better. It is whether the risk is content manipulation, agent action authorization, or both.

AI firewall vs runtime governance: practical differences

A firewall-style AI control is typically placed inline with the model interaction. It evaluates the text payload going into the model and the text payload returned by the model. This is valuable when the risk is embedded in the prompt, the retrieved context, or the model output.

For example, a prompt injection firewall is relevant when an attacker tries to override system instructions, exfiltrate sensitive information through the model, or force the application to generate disallowed content.

Runtime governance sits closer to the agent execution layer. It is concerned with what the agent is, what it is allowed to access, what tools it can call, under what policy conditions, and what evidence is captured when it acts.

This becomes especially relevant in AI agent runtime security, where the system is not only producing text but also invoking functions, calling tools, reading enterprise data, or initiating actions in connected systems.

Control area	Trussed runtime governance	Lakera-style AI firewall	Practical difference
Primary control point	Agent execution layer, including identity, permissions, tools, and policy conditions.	Request and response layer, including prompts and model outputs.	Runtime governance controls actions during execution, while firewall controls inspect text payloads around the model.
Common risk focus	Agent action authorization, tool access, least privilege, approval workflows, MCP security, and audit logging.	Prompt injection, data leakage, and policy-violating content.	The risk surface may be content manipulation, action authorization, or both.
Enterprise agent use cases	Especially relevant when AI agents invoke functions, call tools, read enterprise data, or initiate actions in connected systems.	Relevant when risks are embedded in prompts, retrieved context, or model output.	Production agents often require controls beyond text inspection when they can act in enterprise systems.
MCP-connected systems	Enterprises should verify whether policy is enforced at the tool-call interaction point.	Model prompt boundary controls can inspect model inputs and outputs.	The tool-call surface is not the same as the model prompt boundary.
Auditability	Focuses on what evidence is captured when an agent acts.	Focuses on inspection of prompts and model outputs.	Action-level evidence matters when agents execute workflows.

Coverage gaps to look for

Model Context Protocol governance sharpens this distinction. MCP defines a standardized way for AI applications to connect to external tools, data sources, and systems through client-server interactions. That tool-call surface is not the same as the model prompt boundary.

A model output may appear acceptable as text while still leading to an inappropriate tool invocation if the runtime lacks authorization controls. For MCP-connected agents, enterprises should verify whether policy is enforced at the tool-call interaction point, not only before and after the LLM call.

Evaluation principle: content controls and runtime controls should be assessed against different parts of the architecture. One evaluates what the model sees or says. The other governs what the agent is allowed to do.

How to evaluate the right control mix

Use the control layer that matches the risk surface: content, actions, or both. For enterprise AI agents, the assessment should include the prompt and output boundary as well as the runtime environment where identities, permissions, tool calls, policies, approvals, and logs are handled.

Is the primary risk prompt injection, data leakage, or policy-violating content?
Can the agent invoke functions, call tools, read enterprise data, or initiate actions in connected systems?
Does the architecture require agent identity, permissions, and least privilege controls?
Are tool approval workflows required before an agent performs sensitive actions?
For MCP-connected agents, is policy enforced at the tool-call interaction point?
Is audit logging required to capture evidence when the agent acts?

When to use one, the other, or both

An AI firewall is valuable when the organization needs to inspect prompts and model outputs for malicious, sensitive, or policy-violating content. Runtime governance is valuable when the organization needs to control agent identity, permissions, tool calls, policy enforcement, and auditability during execution.

Use firewall-style controls when

The risk is embedded in the prompt, retrieved context, or model output, including prompt injection, data leakage, or disallowed content generation.

Use runtime governance when

The agent can access tools, read enterprise data, call functions, initiate actions, or operate across MCP-connected systems.

For production agent deployments, the decision is not which category is universally better. The decision is whether the architecture needs content inspection, action authorization, or both.