What the comparison is really about
This comparison is not a replacement decision between two tools that do the same job. It is a control-plane decision. Application security controls reduce software and supply-chain risk across the development lifecycle. AI runtime governance controls evaluate and constrain what deployed agents can do when they operate with delegated authority.
For CISOs and security architects, the practical question is where each control belongs in the enterprise architecture. AppSec remains necessary for AI systems because AI agents are still built from software artifacts. Runtime governance becomes necessary when those agents begin making decisions, calling tools, accessing data, or initiating actions in live environments.
Snyk and Trussed by control plane
| Evaluation area | Trussed | Snyk | Practical distinction |
|---|---|---|---|
| Primary control plane | AI runtime governance for live agent behavior after deployment. | Application security for source code, open-source dependencies, containers, and infrastructure-as-code. | One governs execution-time AI behavior, the other reduces software artifact risk. |
| Core security focus | Agent identity, permissions, tool use, policy enforcement, monitoring, and audit logging. | Finding and remediating risk across development workflows. | Runtime authorization and evidence differ from vulnerability detection and remediation. |
| Typical operating point | Model gateway, agent orchestrator, tool proxy, API gateway, or another inline enforcement point. | Build, delivery, and remediation workflows for software artifacts. | AI governance should sit where agent decisions can be evaluated before an action occurs. |
| Enterprise fit | Enterprises deploying AI agents with access to tools, APIs, data, or external actions. | Enterprises managing software defects and supply-chain risk in applications and infrastructure. | These categories are typically complementary rather than interchangeable. |
Where AppSec controls stop for AI agents
Application security remains necessary for AI systems. AI agents are still built from code, third-party packages, containers, infrastructure definitions, APIs, and deployment pipelines. If those components contain exploitable defects, weak dependencies, or misconfigured infrastructure, runtime governance alone does not make the system secure. Snyk or equivalent AppSec tooling should remain part of the software security baseline.
The gap appears after deployment, when an agent begins operating. Traditional AppSec tooling can help reduce defects in the artifacts that make up the agent system, but the cited Snyk documentation focuses on scan, test, and remediation capabilities for software artifacts. It does not establish runtime authorization or denial of individual live AI-agent tool calls.
That distinction becomes important for agentic AI because prompts can shape behavior. OWASP identifies prompt injection as a risk where crafted inputs can manipulate model behavior or cause unintended actions. It also identifies excessive agency as a risk when LLM-based systems receive too much functionality, permission, or autonomy. These are not just code-quality problems. They are execution-time governance problems.
A practical example is an internal support agent connected to ticketing, customer records, and administrative tools. AppSec scanning can help reduce vulnerabilities in the agent application, its dependencies, and its deployment artifacts. Runtime governance is needed to decide whether a specific user request should allow that agent to retrieve sensitive records, update a ticket, call an administrative API, or require human approval before proceeding.
Reference architecture: separate build-time and runtime controls
A defensible enterprise architecture separates the pre-deployment AppSec control plane from the live AI-agent runtime control plane. The two should exchange operational context where useful, but they should not be treated as the same layer.
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Use AppSec controls at build time
At build time, security teams use AppSec controls to reduce software defects and supply-chain risk. This includes scanning source code, open-source dependencies, containers, and infrastructure-as-code, then routing findings into remediation workflows. These controls align with secure software development practices and vulnerability response.
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Place governance where agent actions can be evaluated
At runtime, AI governance controls should sit where agent decisions can be evaluated before an action occurs. Depending on the architecture, this may be at the model gateway, agent orchestrator, tool proxy, API gateway, or another inline enforcement point.
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Evaluate policy using execution context
The objective is to evaluate the requested action using policy context such as agent identity, user context, data sensitivity, environment, tool type, requested operation, and risk tier.
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Enforce least privilege and preserve evidence
The runtime layer should support least privilege by limiting each agent to approved tools, data sources, and actions. It should preserve an auditable record of prompts, tool-call requests, policy evaluations, approvals, denials, exceptions, and downstream actions.
Those records are operationally different from vulnerability findings. They are evidence of what an agent attempted to do and how policy responded during execution.
Two enterprise security control planes
Application security
Find and fix risk in code, dependencies, containers, and infrastructure-as-code before and during delivery.
AI runtime governance
Control what AI agents can do during live execution, including tool calls, permissions, policy decisions, and audit trails.
Enterprise architecture
Use both where AI agents are built from software artifacts and then operate with delegated authority in production.
Evaluation checklist: when runtime governance should complement AppSec
Runtime governance should be evaluated alongside AppSec when AI agents move beyond simple response generation and begin operating with data access, tool access, or delegated authority.
- The agent can call tools, APIs, action groups, databases, or external systems.
- The agent has access to sensitive information, regulated data, credentials, or customer records.
- The agent can perform actions with side effects, such as updates, exports, messages, transactions, or production changes.
- The organization needs audit evidence for prompts, tool calls, approvals, denials, policy versions, and downstream actions.
- The agent operates under delegated authority and needs a clear identity, owner, permission boundary, and risk tier.
- Prompt injection, excessive agency, unsafe output handling, or over-permissioned tool use would create material business risk.
Implementation decisions for CISOs and security architects
For enterprise teams, the implementation decision is best framed as layered control design. Keep AppSec controls in place for the software artifacts that make up the AI system. Add runtime governance where deployed agents require identity, permission boundaries, policy decisions, monitoring, and auditable evidence during execution.
This separation helps security teams avoid treating vulnerability findings and runtime authorization decisions as the same evidence type. Vulnerability findings show what may be wrong in software artifacts. Runtime governance records show what an AI agent attempted to do, which policy was evaluated, what was approved or denied, and what downstream action followed.