Topic starter
25/06/2026 2:47 pm
TL;DR: Prompt injection, unapproved tool use, and nondeterministic agent behaviour are forcing security teams to treat AI agents as active insider risks, not static software, according to Zenity. The governance gap is that runtime identity and approval boundaries now matter more than preconfigured controls.
Editorial analysis by NHI Mgmt Group, based on content published by Zenity: “The League Assembled: Reflections from the AI Agent Security Summit”.
Key questions
Q: What breaks when AI agents are not governed at runtime?
A: Without runtime governance, an agent can shift behaviour after provisioning and still execute actions that were never reviewed in context.
Q: Why does prompt injection create risk in AI tools that inspect source code or malware samples?
A: Prompt injection is risky because the model may follow attacker-written instructions inside the sample instead of analysing the sample as untrusted data.
Q: What are the signs that an autonomous agent is operating outside its intended boundary?
A: Look for agents running with approval prompts disabled, outbound connections to unfamiliar destinations, and access to production-classified systems that the workflow does not require.
Practitioner guidance
- Define runtime approval boundaries Map which agent actions must be interrupted for human approval before the agent can add tools, call MCP servers, or expand its execution path.
- Baseline agent-specific behaviour signals Track unapproved tool use, repetitive destructive actions, and irregular data access as agent-specific indicators rather than forcing human UEBA thresholds onto agents.
- Inventory every runtime trust expansion Identify where agents can discover new tools, inherit new capabilities, or change code versions during execution so those paths can be governed explicitly.
Bottom line: AI agents create a governance problem because their risk profile can change while they are running, which makes setup-time trust insufficient.
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25/06/2026 3:55 pm
Runtime boundaries are now the real security perimeter for AI agents: the article shows that agent security breaks when organisations rely on setup-time permissions for systems that keep changing during execution. That is not a tuning problem, it is a model problem. If the agent can alter its effective reach mid-session, the control boundary must follow runtime behaviour, not deployment intent.
A few things that frame the scale:
- Gartner predicts that more than 50% of successful cyberattacks against AI agents through 2029 will exploit access control weaknesses.
A question worth separating out:
Q: How should teams govern approval for new tools and MCP servers?
A: Teams should require explicit runtime approval before new tools or MCP servers are added to an agent's effective capability set. That approval should be tied to the live session, not just the original deployment, because the security risk appears when the agent can expand reach without interruption. Governance must follow the point of capability change.
👉 Read our full editorial: AI agent security now hinges on runtime boundaries and trust