Topic starter
24/12/2025 9:46 am
TL;DR: AI agents are increasingly behaving like autonomous machine identities, with reasoning, memory, tool access, and the ability to act across enterprise systems, according to CyberArk. That shift expands the attack surface faster than conventional IAM, privilege management, and monitoring models were built to handle, especially once agents collaborate with each other.
Editorial analysis by NHI Mgmt Group, based on content published by CyberArk: “The Rise of AI Agents—Collaborative Intelligence”.
Key questions
Q: What breaks when agentic AI is governed like a normal application account?
A: Security controls break down because agentic systems do not behave like fixed-function applications.
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 security teams know whether an AI agent control stack is actually working?
A: Look for three things: every agent has a traceable identity, permissions are narrow enough to explain in operational terms, and actions can be audited end to end.
Practitioner guidance
- Define AI agents as governed identities Assign each agent an owner, purpose, access scope, and revocation path so that identity lifecycle controls exist before the agent is allowed to act.
- Separate planning, execution, and memory boundaries Treat orchestration layers as distinct control points and do not let a single agent identity inherit unrestricted access across all layers.
- Replace post-hoc review with runtime limits Set tool-scoped permissions, session boundaries, and explicit approval gates where an agent can initiate high-risk actions without human intervention.
Bottom line: AI agents are not just another automation layer. They behave like machine identities with memory, tool use, and delegated execution, which changes the governance problem.
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This topic was modified 10 months ago by Abdelrahman
This topic was modified 5 days ago by NHI Mgmt Group
08/10/2026 5:42 pm
AI agents are becoming machine identities, not just smarter applications. The article’s core signal is that identity governance now has to cover systems that reason, retain memory, and execute actions on behalf of goals. That combination moves the control problem from software feature management into identity lifecycle management, where scope, ownership, and revocation all matter. Practitioners should stop treating agents as auxiliary automation and govern them as first-class non-human identities.
A few things that frame the scale:
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should organisations govern AI agents that act as business units of work?
A: Organisations should govern AI agents as first-class non-human identities. That means assigning named ownership, defining the scope of permitted actions, separating duties such as propose versus publish, and requiring auditable logs and revocation paths before the agent is allowed into production. The governance model belongs in procurement, not just in the technical rollout.
👉 Read our full editorial: AI agents are becoming machine identities that outpace IAM controls