Topic starter
27/10/2026 7:12 pm
TL;DR: DTEX’s RSAC 2026 panel recap argues that AI agents are already creating insider-like risk inside enterprises, while governance, visibility and ownership lag behind deployment. The key problem is not task completion but uncontrolled behaviour, because existing review and permission models assume stable, human-readable access patterns that agents can bypass or obscure.
Editorial analysis by NHI Mgmt Group, based on content published by DTEX: “What RSAC 2026 Revealed About AI Agent Security and Enterprise Risk”.
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 autonomous AI agents increase insider risk even when access is technically authorized?
A: Autonomous agents increase insider risk because authorization alone does not explain purpose.
Q: How should security teams set access boundaries for AI agents?
A: Security teams should define agent boundaries around data sources, tools, and allowed actions before any production rollout.
Practitioner guidance
- Map AI agents to explicit owners and purposes Create an inventory that records each agent, its business purpose, its human owner and the data or systems it can touch.
- Scope permissions to the task, not the environment Limit each agent to the minimum data, tools and actions required for the current task, and re-check scope whenever the workflow changes.
- Monitor runtime behaviour against intended purpose Log the agent’s inputs, tool calls, outputs and data movement so supervisors can compare what it did with why it was supposed to do it.
Bottom line: AI agents can create insider-like risk even when they complete assigned work successfully, because the unsafe part may be the path they took to get there.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →