Topic starter
06/09/2026 9:34 am
TL;DR: RSAC 2026 sessions from Cisco, CrowdStrike, Microsoft, and Okta converged on a clear finding: AI agents are already inside enterprises, but most security teams still cannot observe, govern, or baseline them, according to Vorlon. The unresolved problem is not adoption, but action-level identity control and behavioural detection for autonomous systems.
Editorial analysis by NHI Mgmt Group, based on content published by Vorlon: “Five Things RSAC 2026 Taught Us About Securing AI Agents”.
By the numbers:
- Enterprise adoption of AI agents rose from 1% of organizations in Q1 2024 to 91% by late 2025, according to Vorlon.
Key questions
Q: What breaks when discovery does not lead to ownership for AI agents?
A: You get visibility without accountability.
Q: Why do AI agents create more risk than traditional automation?
A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously.
Q: How do organizations prove AI agent controls are actually working?
A: Organizations prove control effectiveness by showing which agents accessed which data, what actions they executed, and whether those actions stayed within approved task boundaries.
Practitioner guidance
- Inventory AI agents across business and technical teams Identify agents created by developers, business units, and SaaS platforms, then record owner, connected systems, and business purpose for each one.
- Assign a lifecycle owner to every agent Map each agent to a responsible owner who can approve changes, accept residual risk, and decommission the agent when its job ends.
- Define intent profiles before enabling production access Document what each agent is built to do, which actions are permitted, and which organisational constraints override user requests or local workflow logic.
Bottom line: AI agent security is now an identity governance problem because enterprises are already deploying agents faster than they can classify and oversee them.
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06/09/2026 9:34 am
AI agent identity is now a governance discipline, not a future roadmap item. The article shows that enterprises are already deploying agents faster than they can classify, own, and supervise them. That makes discovery, ownership, and lifecycle management immediate identity problems rather than optional enhancements. The implication for practitioners is that agent governance must sit inside IAM and NHI operations, not beside them.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
- 1 in 3 organisations encountered suspicious AI agent activity in 2025, and 99.4% experienced a SaaS or AI ecosystem incident.
A question worth separating out:
Q: How should organisations govern MCP connectors used by AI agents?
A: Treat every connector like a trusted dependency that can extend the agent’s reach into data and workflow systems. The decision is not only whether the connector works, but whether it has been reviewed, constrained, and placed inside a trust boundary that matches the agent’s job.
👉 Read our full editorial: Agentic security at RSAC 2026 exposes the identity gap