Topic starter
05/12/2025 4:33 pm
TL;DR: AI agents are becoming autonomous access-bearing entities that expand the attack surface as they accumulate credentials, tools, and entitlements, according to CyberArk. The security model now has to shift from static automation controls to identity-first governance, because blast radius, not just authentication strength, determines the real risk.
Editorial analysis by NHI Mgmt Group, based on content published by CyberArk: “AI agents and identity risks: How security will shift in 2026”.
Key questions
Q: How should security teams govern AI models that can call tools and access data?
A: Security teams should govern AI models as non-human identities with named owners, limited scope, short-lived credentials, and continuous authorization.
Q: Why do AI agents create more risk than traditional automation?
A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously.
Q: What are the signs that an AI agent has more access than it needs?
A: Common signs include access to systems outside the declared task, frequent permission exceptions, policies that are broader than the observed workflow, and usage logs showing actions the agent did not need to complete its job.
Practitioner guidance
- Inventory every AI agent as a governed identity Record each agent's owner, purpose, credentials, tools and downstream systems so the organisation can see where agentic access exists and who is accountable for it.
- Replace standing access with task-scoped entitlements Grant agents only the permissions required for the current job and remove them when the task ends, including access to tools that can expose invoices, records or repositories.
- Segment agent toolchains by business function Separate read, write and export capabilities so one manipulated input cannot move an agent from a harmless lookup to a sensitive disclosure path.
Bottom line: AI agents should be managed as identities because their tools, credentials and entitlements define the real security boundary.
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08/10/2026 5:33 pm
AI agent identity is now a governance category, not a tooling feature. The article shows that an agent can hold credentials, call tools and accumulate entitlements over time, which makes it an access-bearing subject rather than a simple automation layer. That shifts the control question from whether the model is accurate to whether the identity is bounded, owned and reviewable. Practitioners should treat agent inventory, ownership and entitlement scope as core IAM data.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
- 54% of organisations are actively deploying AI agents across workflows, yet only 21% report a mature governance model for agentic AI.
A question worth separating out:
Q: How can organisations reduce data exposure in AI tools?
A: Start with data classification, then map where sensitive information can flow into prompts, connectors, and logs. Limit AI systems to the minimum data they need, require owner approval for higher-risk datasets, and monitor for unsanctioned sharing. Data controls work best when paired with identity controls and usage visibility.
👉 Read our full editorial: AI agent identity risk is outpacing enterprise IAM controls