Topic starter
10/06/2026 12:08 am
TL;DR: Agentic AI systems can reason, plan, and act across enterprise tools with very little human oversight, but that same autonomy expands the attack surface and weakens static RBAC assumptions, according to Lasso Security. Existing IAM programmes now have to govern non-human actors that can initiate workflows, move data, and trigger downstream actions in real time.
Editorial analysis by NHI Mgmt Group, based on content published by Lasso Security: “Top Agentic AI Use Cases Transforming Enterprise Operations”.
Key questions
A: Security teams should treat agentic AI as a runtime identity and policy problem, not just a prompt filtering problem.
Q: Why do agentic AI workflows create new IAM risk compared with traditional automation?
A: Traditional automation usually follows fixed rules and predictable paths, so its access model is easier to review.
Q: What are the signs that an autonomous agent is operating outside its intended boundary?
A: Look for agents running with approval prompts disabled, outbound connections to unfamiliar destinations, and access to production-classified systems that the workflow does not require.
Practitioner guidance
- Classify agents as non-human identities Assign each agent an owner, lifecycle state, and scope so provisioning, rotation, and deactivation are governed like any other machine identity.
- Bind privileges to task context Use just-in-time and just-enough-access controls so the agent can only reach the tools, data, and actions required for the current task.
- Log every agent action with context metadata Capture time, initiator, purpose, outcome, and data classification for each API call or file access so investigators can reconstruct the full workflow.
Bottom line: Agentic AI changes identity governance because the actor can plan, choose tools, and trigger actions at runtime rather than following a fixed script.
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This topic was modified 3 hours ago by NHI Mgmt Group
11/06/2026 1:47 am
Agentic AI turns identity governance into runtime governance. The article makes clear that the control problem is no longer just who can access what, but what an identity can decide to do once access is active. That shifts the field away from static entitlement review and toward continuous governance of action, context, and delegated tools. Practitioners should read this as a structural change in IAM scope.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- 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 teams review before deploying agentic AI in production?
A: Review whether the agent has a clear owner, a defined purpose, bounded tools, and a termination point for access. If those answers are vague, the deployment is already creating unmanaged privilege and audit exposure.
👉 Read our full editorial: Agentic AI governance gaps are widening in enterprise operations
12/06/2026 3:21 am
Agentic AI exposes an assumption collapse in access governance: static RBAC was designed for actors whose useful permissions are broadly stable between review cycles. That assumption fails when the actor can select tools, sequence actions, and change behaviour at runtime. The implication is not just tighter access control, but a different model of authority for non-human actors.
A few things that frame the scale:
- Half of companies using generative AI will deploy agentic AI by 2027, according to Deloitte's 2025 Technology Predictions.
A question worth separating out:
Q: Who is accountable when an AI agent takes an unsafe action?
A: Accountability should sit with the business owner of the agent, the team that provisioned the access, and the control owners responsible for monitoring and revocation. If no one can answer who approved the identity, the scope, and the oversight model, the governance framework is not complete enough for production.
👉 Read our full editorial: Agentic AI governance gaps are widening in enterprise operations
This post was modified 3 hours ago by NHI Mgmt Group