Static scanning breaks down because it only reviews the skill package or prompt before execution. Agentic risk emerges when the system decides, in context, whether to trust a document, chain a skill, or call a tool. Governance therefore has to evaluate runtime behaviour, not just inspect artefacts in advance.
Why static skill scanning fails as a governance model
Static scanning is useful for finding obvious defects in a skill package, prompt, or tool definition, but it cannot tell you how an agent will behave once context changes. The governance failure is not inspection itself, it is assuming that pre-execution artefacts fully describe runtime trust decisions. In agentic systems, those decisions are often made after the scan has already finished.
That means a static gate can confirm that a skill exists, yet still miss the real control question: will the agent trust the document, follow the chain, and invoke the tool under the live conditions it actually sees? The answer depends on runtime state, surrounding instructions, provenance signals, and policy enforcement at the moment of action.
This is why agentic governance has to treat the static artefact as only one input. The important security property is not merely whether the skill code or prompt looks acceptable, but whether the system can safely decide, in context, whether to proceed, defer, or refuse.
What runtime behaviour changes the risk picture
Runtime behaviour is where agentic systems expose their real attack surface. A benign-looking skill can become risky when the agent chains it with another capability, reuses prior context, or follows untrusted content that was not part of the original scan boundary. The control problem therefore shifts from artifact review to trust decisions made during orchestration.
That runtime layer is also where privilege becomes meaningful. If the agent can call tools, access files, query services, or act on behalf of a user, then the security question is whether each action is authorized for that moment and that context, not whether the package passed a preflight review. For a practitioner view of that problem, see AI Agent Authorisation Guide.
Static scanning also misses the distinction between planned capability and exercised capability. A tool may be present but never used, or it may be invoked only when a hidden trigger appears. That is why policy needs to observe actual action paths, not just declared dependencies or packaging metadata.
What governance has to evaluate instead
Governance needs to assess the live chain of trust: what the agent can see, what it chooses to trust, what it is allowed to do, and what gets logged when it does it. In practice that means runtime authorization, boundary checks, and traceable action records matter more than a one-time artifact scan. NHIMG’s Agentic AI Security Guide frames this as a layered threat model across inputs, memory, tools, orchestration, and identity.
For agentic systems, a stronger governance stance is to ask whether each action is safe to execute now, with this context, for this principal, against this target. That is the logic behind Zero Trust for AI Agents, which moves control from static trust assumptions to continuous verification and removal of standing privilege.
Where agent behaviour depends on delegated authority or on-behalf-of access, runtime evaluation becomes even more important because the agent may be operating with borrowed permissions. In that case, the scan must be supplemented by per-action policy, short-lived authorization, and a clear approval path for higher-impact operations.
Risk and Threat Considerations
Static skill scanning creates a false sense of control when the real abuse path appears only after the agent has begun reasoning over live inputs. An attacker can exploit that gap by steering the agent toward a trusted-looking document, a chained skill, or a tool call that was not obvious in the original artefact review.
Failure mechanism: The defender validates code or prompts before execution, but the agent’s runtime decision to trust, chain, or invoke is made later and under different conditions, allowing malicious context to bypass the scan boundary.
Impact: The result can be unauthorized actions, unsafe tool use, privilege abuse, data exposure, or compound failures across multiple steps in an agent workflow.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 addresses the attack and risk surface, while NIST AI RMF sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | Runtime agent decisions can misuse delegated privilege after static review. |
| ASI02 — Tool Misuse | Static scans miss harmful tool selection and chaining at execution time. | |
| ASI09 — Human-Agent Trust Exploitation | Agents may trust untrusted documents or context that passed pre-execution review. | |
| Recommendation — Enforce per-action authorization and remove standing privilege from agent workflows. Validate tool calls at runtime and constrain actions to approved tool scopes. Require trust checks and escalation for agent actions influenced by external content. | ||
| NIST AI RMF | Govern | Agentic governance needs runtime oversight, accountability, and policy enforcement. |
| Recommendation — Establish monitoring, accountability, and approval controls for live agent actions. | ||
Practitioner Guidance
What to verify: Confirm that your control design can inspect and constrain runtime decisions, not just static skill assets. If a governance checkpoint cannot see the live principal, current context, and intended action, it is too weak to treat as the primary approval mechanism.
What good looks like: The agent can explain why it trusted a source, which tool it selected, and what policy allowed the action, with enough evidence to reconstruct the decision path. If you cannot produce that trail, you do not yet have governance over the behaviour that matters.
Decision rule: Treat static scanning as a pre-screening step, and require runtime authorization, action logging, and escalation for any step that changes trust, chains capabilities, or crosses a material privilege boundary.
Practitioner takeaway: The control objective is not to prove that the skill package looked safe, it is to prove that the agent’s live decisions remain bounded, attributable, and policy-governed when context changes.
Related resources from NHI Mgmt Group
- How should security teams govern machine identity credentials in agentic AI environments?
- What breaks when static privileges are used for agentic AI security?
- What breaks when static file permissions are used to govern AI assistants?
- When does just-in-time access reduce risk for agentic AI, and when does it fall short?