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24/07/2026 4:07 pm
TL;DR: AI agents and other non-human identities now outnumber human identities by more than 80 to 1, and 80% of organisations report agents already acting beyond intended scope, according to SailPoint's AI Agents: The New Attack Surface report. The real problem is not grant-time approval but permission drift after deployment, which leaves IAM controls blind to what agents actually reach.
NHIMG editorial — based on content published by Elisity: How do I stop AI agents from becoming over-permissioned?
By the numbers:
- AI agents and other non-human identities now outnumber human identities by more than 80 to 1.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should security teams manage permissions for AI agents?
A: Security teams should regularly assess and update the permissions granted to AI agents to ensure they align with their intended scope.
Q: Why do AI agents create more authorization risk than static service accounts?
A: AI agents can vary their access needs by task, context, and timing inside the same workflow, which makes static entitlement assumptions weaker.
Q: What breaks when organisations only audit AI agent permissions in the IAM console?
A: The audit misses the difference between what the agent is allowed to do and what it actually does.
Practitioner guidance
- Inventory every non-human identity Build a single register for AI agents, service accounts, workloads, and automation identities.
- Start all agents at default-deny Provision each new agent with no effective access, then grant only the named systems, actions, and time window required for the task.
- Run a reconciliation loop on a fixed cadence Compare granted policy to observed network traffic monthly at minimum, and weekly for identities touching production data.
What's in the full article
Elisity's full blog post covers the operational detail this post intentionally leaves for the source:
- The reconciliation loop in step-by-step form, including how to compare granted policy with observed traffic.
- The OpenAI and Hugging Face incident context, including why the access path mattered more than the model label.
- The broader zero trust and identity-based microsegmentation framing that underpins the author's control model.
- The companion articles on AI agent network security and least-privilege access for agents, which extend the operating model.
👉 Read Elisity's analysis of over-permissioned AI agents and permission drift →
AI agent over-permissioning: is your access model drifting too far?
Explore further
24/07/2026 4:12 pm
Permission drift is the real over-permissioning problem: The dangerous condition is not a bad approval at provisioning time, but access that remains broad after the operational reason for it has changed. AI agents move quickly, but governance usually moves on a human cadence, which means scope decay is predictable. That makes lifecycle ownership the control that matters most: if nobody owns narrowing the identity later, the identity will remain overexposed.
A few things that frame the scale:
- AI agents and other non-human identities now outnumber human identities by more than 80 to 1, according to the Ultimate Guide to NHIs , 2025 Outlook and Predictions.
- Our research also shows that 27 days is the average time to remediate a leaked secret, which is long enough for unmanaged access to become an incident rather than a review finding.
A question worth separating out:
Q: Who should own AI agent access reviews and lifecycle decisions?
A: Ownership should sit with the business application team and the identity function together, because the workflow owner understands the task and the identity team understands privilege, audit, and offboarding. Without that split accountability, access reviews become generic checklists that miss the real operational risk.
👉 Read our full editorial: Over-permissioned AI agents expose a drifting identity control gap
