AI-SPM vs Runtime Governance: Noma Security vs Trussed

Why this comparison matters

Enterprise AI security programs increasingly need to distinguish between posture assessment and live enforcement. AI-SPM helps teams understand risk before deployment or outside live execution. Runtime governance helps teams control what AI agents are allowed to do while they operate.

This distinction matters because agentic AI systems may select tools, pass data, call enterprise systems, and continue acting based on prior outputs. In that environment, understanding posture is important, but it is not the same as enforcing permissions, approvals, and auditability at runtime.

What AI-SPM covers

AI Security Posture Management evaluates AI systems at design time or at a point in time. In this page, Noma Security is treated as representative of that posture layer.

The posture layer focuses on configuration, exposure, and risk posture. It helps security and architecture teams identify risky systems, risky tools, risky data connections, and other concerns before or outside live execution.

What runtime governance covers

Runtime governance controls active AI agents during execution. In this page, Trussed AI is treated as representative of runtime governance for enterprise AI agents.

The runtime layer focuses on policy decisions, identity, permissions, tool use, approvals, and audit logging. Its role is to apply control while agents operate, especially when they use tools or interact with enterprise systems.

Where the gap appears in agentic AI

Static AI risk review and runtime agent control diverge most clearly at the tool-call boundary. A traditional application may have predictable code paths and predefined permission models. An AI agent can introduce more variable behavior: it may interpret a user request, select a tool, pass data to that tool, receive output, and decide on a next action. Each step can change the risk context.

Established AI security guidance separates risks related to configuration and exposure from risks related to excessive agency and insecure plugin or tool design. That distinction supports a layered architecture. Posture management helps identify whether systems, tools, and data connections are risky. Runtime governance helps constrain what happens when those connections are used.

For example, an assessment layer may flag that a tool grants broad access to customer records. That finding is useful, but it does not by itself enforce which agent can use the tool, whether human approval is required, or whether a specific request should be denied. A runtime layer can apply least privilege and approval logic during execution, but it still benefits from posture findings that inform which tools and data paths deserve stricter controls.

For governance leaders, the key design principle is separation of concerns. Security assessment and access control are different functions. Treating them as interchangeable can produce false assurance, especially when AI agents act across enterprise systems.

Noma Security vs Trussed: control-layer comparison

The comparison is best understood as a control-layer distinction rather than a direct feature-for-feature substitution. One layer helps assess risk posture. The other helps enforce runtime control.

How to decide whether you need AI-SPM, runtime governance, or both

Many agentic AI environments need both: posture management to understand risk before deployment, and runtime governance to enforce least privilege when agents act.

Enterprise architects should evaluate whether the AI environment includes execution-time behaviors that require direct control. This is especially important when agents can act across enterprise systems.

• Use posture management to understand configuration, exposure, and risk posture before deployment or outside live execution.
• Use runtime governance when active AI agents need policy decisions, identity controls, permission boundaries, tool controls, approvals, and audit logging during execution.
• Treat tool access and data access as runtime concerns when a specific request may need to be allowed, denied, constrained, or escalated for human approval.
• Use posture findings to identify which tools and data paths deserve stricter runtime controls.
• Avoid treating security assessment and access control as interchangeable functions.

Evaluation criteria for enterprise architects

For architecture and governance teams, the decision should be based on where control is needed. If the requirement is to assess exposure and identify posture risk, the AI-SPM layer is relevant. If the requirement is to govern what agents do while they operate, the runtime governance layer is relevant.

If your AI roadmap includes autonomous agents, MCP-connected tools, delegated permissions, or agent-to-agent workflows, posture assessment alone may not be enough.