The bottom line: The effective access of AI agents is not determined by IAM permissions alone, but by the interplay with firewall rules, cloud policies and microsegmentation — a policy governance task that most organizations systematically underestimate.
Autonomous AI agents require a new perspective on access control: Identity Management alone is insufficient. The central risk lies in the discrepancy between configured permissions and the access paths actually available through the surrounding policy landscape.
AI agents differ fundamentally from human users: they act on behalf of individuals, call APIs, trigger workflows, and generate additional agents as needed. Some architectures even enable communication between agents acting on behalf of different users. This complexity makes traditional identity frameworks inadequate. The classic access control question — “Who or what is executing this action?” — is becoming increasingly difficult to answer.
Many security teams treat agentic AI primarily as an identity problem and focus on IAM platforms (Identity Access Management) to issue credentials and enforce authentication. This is a necessary but insufficient approach. The actual security-relevant question arises afterward: Once an agent is authenticated and its access rights are defined, does the surrounding policy landscape enforce this access as intended? Do access paths exist that the original model never anticipated? Does the organization have visibility into all enforcement levels that the agent touches?
The principle of least privilege applies to AI agents as it does to human users — only the required minimum access rights are granted. It becomes problematic when organizations treat this as a pure IAM configuration task and consider it complete afterward. The actual access results from the interplay of IAM permissions with the entire surrounding policy landscape: firewall rules, cloud access controls, and microsegmentation boundaries define what the agent can actually reach and execute in the network. Outdated firewall rules, cloud policies created for specific workloads that were never updated, or microsegmentation boundaries that no longer reflect a deliberately designed access model create unintended access paths.
An agent configured on paper according to least privilege can in practice have significantly more access than expected. This policy governance discrepancy is not yet systematically addressed in most agentic AI implementations. The central question for security teams is therefore: Does the effective access match the originally defined model, or have additional paths emerged through policies, exceptions, and changes? What is critical is transparency and traceability of these relationships — a policy-layer task that overlays the identity management debate.
Source: www.it-daily.net · Published 22 June 2026
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