Topic starter
25/06/2026 12:43 am
TL;DR: Agentic AI is already active in IT operations for 66% of organisations, while 56% report shadow AI issues at least monthly, according to Delinea's 2025 AI in Identity Security report. Traditional RBAC and static access models are not built for AI-to-AI credential exchange, auditable identity mapping, or intent-aware privilege control.
Editorial analysis by NHI Mgmt Group, based on content published by Delinea: “Agentic AI Security: Building the next generation of access controls”.
By the numbers:
- 66% of organisations actively use agentic AI in IT operations, according to Delinea's 2025 AI in Identity Security report.
- 56% encounter shadow AI issues at least monthly, according to Delinea's 2025 AI in Identity Security report.
Key questions
Q: What breaks when agentic systems are governed only by roles and static permissions?
A: Static roles fail when the same agent needs different permissions for different tasks in the same session.
Q: Why do agents increase governance risk even when they use valid credentials?
A: Valid credentials do not solve the governance problem when the actor can act continuously and outside the human review cycle.
Q: How do security teams know if agentic AI controls are failing?
A: The main signs are session drift, repeated retry loops, unauthorized tool calls, and behaviour that diverges from the documented task sequence.
Practitioner guidance
- Inventory AI identities and classify them by risk Build a discover-and-classify process for AI agents across hybrid and multi-cloud environments, then separate low-risk helpers from agents that can touch sensitive systems or data.
- Replace standing access with task-scoped access Grant agents only the permissions needed for a defined task and remove persistent access between tasks, especially where the agent can reach privileged systems.
- Map agent personas and authorization relationships Document agent IDs, AI personas, and training model metadata so reviewers can trace which identity acted, what it was allowed to do, and which system it touched.
Bottom line: Agentic AI pushes identity teams beyond static role assignment because runtime behaviour, delegated access, and machine-to-machine trust all need governance.
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25/06/2026 9:43 am
Identity-centric access control is now the control plane for agentic AI. Agentic AI does not simply add another workload class to existing IAM. It collapses the assumption that authorisation can be modelled once and reused unchanged, because the actor can choose actions at runtime. The practical implication is that IAM and PAM teams need to treat agent identity as an operational subject with its own governance boundary.
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.
A question worth separating out:
Q: Should organisations re-evaluate IAM and PAM for agentic AI deployments?
A: Yes, because agentic systems can inherit credentials and exercise privileged tools in ways that traditional IAM and PAM reviews do not fully capture. Organisations should reassess whether their current models account for ephemeral tasks, delegated authority, and machine-speed execution. The key test is whether access can be constrained to the exact task and revoked immediately afterward.
👉 Read our full editorial: Agentic AI security needs identity-centric access controls