Robust Intelligence and Trussed address related but distinct parts of the enterprise AI governance problem. The practical distinction is assessment versus enforcement: AI validation helps teams evaluate whether an AI system behaves acceptably under defined tests, policies, and risk scenarios, while runtime governance controls what an agent can do when it is operating in production.
The core difference: assessment versus enforcement
Public Cisco material describes Robust Intelligence as focused on protecting AI applications from security and safety risks. That positioning aligns with validation, testing, and risk assessment around AI systems. These controls help organizations understand whether an AI application is likely to meet expectations before deployment or during periodic evaluation.
Trussed AI is positioned around runtime governance and security for enterprise AI agents. That includes runtime policy enforcement, agent identity, permissions, least privilege, tool approval workflows, runtime monitoring, and audit logging. These controls are concerned with the execution path, especially when AI agents can take actions, call tools, access data, or interact with enterprise systems.
The distinction matters because production agents are not only generating text. They may query databases, submit tickets, send messages, change records, invoke plugins, or call internal APIs. A system can pass validation and still require runtime controls that limit actions according to identity, context, permissions, and business risk.
AI validation vs runtime governance
AI validation assesses model or application behavior against defined tests, policies, benchmarks, red-team scenarios, and known risk conditions. It helps determine whether a system should be deployed, changed, held for review, or monitored more closely.
Runtime governance enforces policy, identity, permissions, tool-call restrictions, approvals, monitoring, and auditability while agents operate. It helps determine what the system can do after deployment, which tools it can use, what data it can access, when a human must approve an action, and what must be logged for oversight.
| Evaluation area | Trussed | Robust Intelligence | Practical difference |
|---|---|---|---|
| Primary control category | Runtime governance and security for enterprise AI agents. | AI application protection from security and safety risks, aligned with validation, testing, and risk assessment. | One controls agent behavior during operation, the other assesses risk around AI systems. |
| Control point | Runtime policy enforcement, identity, permissions, least privilege, approvals, monitoring, and audit logging. | Validation, testing, and risk assessment around AI applications. | Runtime governance sits in the execution path, validation informs readiness and risk posture. |
| Production agent concern | Controls what an agent can do after deployment when it calls tools, accesses systems, or performs actions. | Helps evaluate whether an AI application meets defined expectations or risk criteria. | Enterprises usually need both categories when AI systems move into production agent execution. |
| Decision supported | Whether an attempted action should be allowed, transformed, escalated, denied, monitored, or logged. | Whether a system should be deployed or remain subject to additional validation and assessment. | Assessment supports deployment decisions, enforcement supports operational decisions. |
What risks remain after validation?
Pre-deployment or periodic validation can reduce known risks, but it cannot by itself enforce least privilege or prevent an agent from using a tool that it has been granted permission to call. This distinction becomes critical when an agent can act beyond conversation: querying databases, submitting tickets, sending messages, changing records, invoking plugins, or calling internal APIs.
Security guidance for LLM applications identifies excessive agency as a risk when systems are given excessive functionality, excessive permissions, or excessive autonomy. The mitigations are operational in nature: limit available tools, apply least privilege, require human approval for high-impact actions, and log or monitor extension activity. These are runtime governance concerns because they must operate at the moment an agent attempts to use a capability.
Sensitive information disclosure creates a similar architecture problem. Validation can test whether an application tends to reveal restricted data under known prompts. Runtime governance must also consider who the user is, which data source is being accessed, whether the agent has authority for that task, and whether the output or tool call should be allowed, transformed, escalated, or denied. In production, access control, data minimization, segregation of user data, input and output validation, and auditability need to be designed into the execution path rather than left only to prompt instructions.
Where runtime governance needs to sit in the enterprise stack
Runtime governance needs to operate where the agent makes decisions, accesses data, invokes tools, or produces outputs that affect users or systems. It is not simply a report after the fact. It must have enough context to evaluate identity, permissions, task intent, tool risk, and required approval before an action is completed.
For enterprise AI agents, this means governance has to connect policy to the runtime path. Agent identity, least privilege, tool-call controls, approval workflows, monitoring, and audit logging all depend on placement close to agent execution. Without that placement, governance teams may have visibility into what happened, but limited ability to prevent or alter high-risk actions when they occur.
How to decide what you need
Enterprises should evaluate the problem they are trying to solve. If the immediate need is to assess model or application behavior against known risks, policies, benchmarks, or red-team scenarios, AI validation is the relevant category. If the need is to govern what AI agents can do in production, runtime governance becomes necessary.
The two categories are complementary. Validation helps teams understand whether an AI system is ready for use. Runtime governance helps teams manage what the system can do after it is deployed, especially when the system has access to tools, data, and enterprise workflows.