Agentic AI security: what access controls do IAM teams need now?

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.

NHIMG editorial — based on content published by Delinea: Agentic AI Security: Building the next generation of access controls

By the numbers:

• 66% of organizations actively use Agentic AI in IT operations.
• 56% encounter shadow AI issues at least monthly.
• 30.9% of organizations store long-term credentials directly in code.

Questions worth separating out

Q: How should security teams govern agentic AI access without relying on static RBAC?

A: Security teams should govern agentic AI with task-scoped entitlements, explicit ownership, and high-risk action gates rather than broad static roles.

Q: Why do agentic AI systems create more IAM risk than ordinary automation?

A: Agentic systems create more IAM risk because they can alter their behaviour during execution, choose actions dynamically, and interact with multiple systems without a human following each step.

Q: What do security teams get wrong about shadow AI governance?

A: Teams often treat shadow AI as a discovery issue only, but unmanaged agents also break ownership, attestation, and offboarding processes.

Practitioner guidance

• Inventory every agent identity and delegated credential Map AI personas, agent IDs, tokens, and certificates to owners, purposes, and system boundaries.
• Replace standing roles with task-scoped entitlements Use policy-based access that binds privileges to approved tasks, sensitivity levels, and execution windows.
• Add human approval gates for privileged AI actions Require real-time approval before agents can reach high-impact systems or perform destructive changes.

What's in the full article

Delinea's full blog covers the operational detail this post intentionally leaves for the source:

• Step-by-step guidance for AI-to-AI credential brokering across machine-to-machine workflows
• Practical examples of visual digital identity mapping for AI personas, agent IDs, and model metadata
• A five-step roadmap for discovery, guardrails, JIT access, intent validation, and monitoring
• PAM-oriented handling of high-risk agent actions, including human approval before execution

👉 Read Delinea's analysis of agentic AI security and access controls →

Agentic AI security: what access controls do IAM teams need now?

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