Topic starter
06/06/2026 11:08 am
TL;DR: RSA 2026 showed that AI agent security has moved from curiosity to operational urgency, with security leaders now trying to govern systems already taking autonomous actions across diverse environments, according to Zenity. The central problem is that static identity models and point-in-time policies assume agents behave like users, but agent runtime decisions keep breaking that assumption.
Editorial analysis by NHI Mgmt Group, based on content published by Zenity: “The Floor Was Selling AI. The Hallways Were Asking for Help.”.
By the numbers:
- Roughly 37% of booths had some version of AI in their primary messaging at RSA 2026.
- Over 600 vendors were on the RSA 2026 expo floor.
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 do AI agents create risk even when they stay within approved permissions?
A: AI agents can be authorised correctly and still produce harmful outcomes because permission is not the same as intent or behavioural appropriateness.
Q: What are the signs that a coding agent security control is failing?
A: A control is failing when benign-looking inputs can still trigger command execution, when skills or tools remain usable after the original task ends, or when untrusted content can influence agent actions across workflow boundaries.
Practitioner guidance
- Define runtime authority boundaries Map which actions an AI agent may take during a live session, then distinguish those from the permissions it merely receives at launch.
- Separate build-time review from live enforcement Use pre-deployment checks for baseline approval, but require enforcement that can restrict actions while the agent is executing.
- Inventory delegated and inherited access Trace where agents operate through human credentials, NHI credentials, or tool-mediated delegation so you can see the full trust chain.
Bottom line: AI agents expose a gap between approved access and actual runtime behaviour, which makes static identity models insufficient on their own.
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06/06/2026 11:46 am
Static identity models are no longer sufficient once an AI agent can change what it does after access is granted. IAM was designed around subjects whose permissions and purpose are knowable at provisioning time. That assumption fails when an agent can alter its effective behaviour through tool use, context changes, or delegated actions. The implication is that governance has to move from identity state to runtime state, because the old model no longer describes the thing being governed.
A few things that frame the scale:
- 90% of IT leaders say properly managing NHIs is essential for a successful zero-trust implementation, according to the Ultimate Guide to NHIs.
- 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 security, compliance, and engineering teams share responsibility for AI agent security?
A: AI agent security should be owned jointly, with engineering responsible for safe implementation, security for policy and control design, and compliance for visibility into data use and governance evidence. The article’s emphasis on collaboration reflects a practical reality: no single team can manage agent autonomy, business impact, and oversight alone. Shared responsibility is essential for effective control.
👉 Read our full editorial: RSA 2026 exposed the gap between AI agents and IAM controls