Topic starter
10/06/2026 12:04 am
TL;DR: AI agents are now operating as autonomous actors inside enterprises, with adoption above 72% of organizations and 40% already running multiple agents in production workflows, according to Pillar Security. The security problem is no longer visibility alone but action control, because static access policy cannot safely govern machine-speed decisions that chain tools and permissions.
Editorial analysis by NHI Mgmt Group, based on content published by Pillar Security: “Securing the Agentic Workforce”.
By the numbers:
- 40% are running multiple agents in production workflows.
Key questions
Q: What breaks when autonomous agents are governed like human users?
A: Session-based IAM breaks first, because autonomous agents can make and execute decisions between review points.
Q: Why do AI agents need action-level authorisation instead of resource-level access?
A: Resource-level access tells you what an agent can reach, but not whether its runtime actions match its authorised purpose.
Q: How should security teams discover shadow AI agents in the enterprise?
A: Use endpoint artefacts first.
Practitioner guidance
- Inventory every production agent Map where agents exist, who created them, what systems they can reach, and whether they are sanctioned or shadow deployments.
- Separate authentication from action approval Treat login or token issuance as only the start of governance, then require runtime checks before each meaningful tool call or downstream write.
- Attach ownership to every agent identity Record a human owner, a business purpose, and an expiry path so agent identities can be reviewed, revoked, and retired without ambiguity.
Bottom line: Autonomous agents invalidate the assumption that identity activity can be reviewed after execution, because the work may be finished before a human sees it.
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This topic was modified 2 hours ago by NHI Mgmt Group
11/06/2026 1:41 am
Autonomous identity governance is collapsing because access review assumes access remains stable long enough to be reviewed. That assumption was designed for human-paced and service-account-paced control loops. It fails when an autonomous agent can acquire context, combine tools, and complete harmful action chains within a single session. The implication is that existing review cadences no longer describe the actual risk state, so practitioners must rethink what governance is trying to observe.
A few things that frame the scale:
- Independent surveys put adoption above 72% of organizations either using or testing AI agents, with 40% running multiple agents in production workflows, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: What should organisations do before scaling agentic workflows?
A: Before scaling agentic workflows, organisations should define who owns each agent, what it is allowed to do, and where intervention will happen if behaviour drifts. They should also test whether tools can be chained into unsafe outcomes even when individual permissions look reasonable. That is the real governance test for autonomous systems.
👉 Read our full editorial: Securing the agentic workforce: why existing IAM controls fail
12/06/2026 3:15 am
Action control is now the decisive governance layer for autonomous agents. The article is right to move the focus away from static access policy. Once an actor can chain tools, change direction, and complete work at machine speed, session-level IAM no longer defines the real security boundary. Practitioners should treat action authorization as the primary control plane for agentic systems.
A few things that frame the scale:
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
- Only about one-third of approximately 500 organizations surveyed by McKinsey in 2026 report maturity level three or higher across agentic AI governance controls.
A question worth separating out:
Q: What should organisations do when an AI agent starts chaining tools beyond its intended task?
A: Contain the agent before the workflow completes, because chained tool use can turn an ordinary task into data movement, unwanted writes, or business process manipulation. The right response is to stop the execution path, preserve the trace, and review the permission scope that allowed the chain to form.
👉 Read our full editorial: Securing the agentic workforce: why existing IAM controls fail
This post was modified 2 hours ago by NHI Mgmt Group