Topic starter
15/08/2026 6:12 pm
TL;DR: Autonomous AI agents create an identity and access problem that traditional security tools cannot handle well, according to Obsidian Security, because agents operate with far more access than their workflows need and can expand blast radius quickly across enterprise systems. The real issue is that existing IAM assumptions break when agents make decisions, access data, and act continuously without human-paced oversight.
Editorial analysis by NHI Mgmt Group, based on content published by Obsidian Security: “AI Agent Protection: Safeguarding Identities, Access, and Behavior”.
By the numbers:
- Research shows agents are granted 10x more access than their workflows actually need, making privilege sprawl one of the most urgent risks in agentic deployments.
Key questions
Q: What breaks when AI agents have broader access than their tasks require?
A: Over-privileged agents break segregation of duties, weaken auditability, and expand blast radius across transactions, data lookups, and workflow triggers.
Q: Why do AI agents increase non-human identity risk?
A: AI agents increase non-human identity risk because they can execute many actions quickly once they inherit a credential or tool permission.
Q: How do security teams know whether AI agent hardening is actually working?
A: They should look for evidence that declared scope and observed scope stay aligned over time.
Practitioner guidance
- Establish continuous agent discovery Build and maintain an inventory of sanctioned and shadow AI agents, including what each one can reach across cloud, SaaS, APIs, and internal systems.
- Map effective authority for every agent Compare assigned permissions with real reachable data and actions, then remove any cross-system combinations that create excessive blast radius.
- Enforce dynamic least privilege Use time-bound tokens, scoped resources, and context-aware access rules so agent privileges match the current task instead of the broadest possible workflow.
Bottom line: AI agents create a governance problem that sits inside identity, access, and runtime behaviour at the same time, which is why traditional controls miss the most dangerous failure modes.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
15/08/2026 6:19 pm
Runtime truth is now the control plane for AI agent governance: static entitlement records are no longer enough when the actor can change what it reaches during execution. The article’s core point is that security teams need verified visibility into live agent activity, not just policy documents or provisioning records. That shift is fundamental for agentic AI governance because the risk sits in execution, not description.
A few things that frame the scale:
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What should IAM teams do when an AI agent is used across several SaaS platforms?
A: Establish one authoritative inventory and one lifecycle path for the agent, then decide which policy and revocation actions must be enforced at the shared execution layer. Platform-native controls can still matter, but they should not be the only place where governance exists.
👉 Read our full editorial: AI agent protection exposes the limits of traditional identity controls