Topic starter
24/07/2026 4:07 pm
TL;DR: Over-permissioned AI agents are usually the product of access drift after deployment, not deliberate over-granting, according to Elisity, and teams must inventory every agent identity, start from default-deny, and reconcile granted policy against observed traffic to revoke unused access. The deeper issue is that grant-time approval alone cannot control machine speed behaviour once privileges begin to drift.
Editorial analysis by NHI Mgmt Group, based on content published by Elisity: “How do I stop AI agents from becoming over-permissioned?”.
By the numbers:
- Non-human identities, including AI agents, service accounts, and workloads, now outnumber human identities by more than 80 to 1.
Key questions
Q: How should security teams control over-permissioned AI apps?
A: Treat the app as an access path that must inherit the smallest possible credential scope.
Q: Why do AI agents become more risky after deployment than at approval time?
A: Because the risk usually comes from entitlement drift.
Q: What are the signs that an AI agent is failing or drifting outside its intended mission?
A: Common warning signs include goal drift, repeated no-progress calls, unusual tool use, privilege escalation attempts, credential misuse, and unexpected agent-to-agent communication.
Practitioner guidance
- Inventory every agent identity Count all AI agents, service accounts, and workload identities so you can assign ownership and governance to the full non-human population.
- Start each identity at default-deny Provision new agent identities with no standing reach, then add only the specific systems and actions the task requires.
- Reconcile granted policy against observed traffic On a fixed cadence, compare what each agent is allowed to reach with what it actually reaches on the network, then split the diff into revoke and investigate queues.
Bottom line: Over-permissioned AI agents are usually the result of access drift after deployment, not a deliberate decision to over-grant.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
24/07/2026 4:12 pm
Over-permissioned AI agents are an entitlement drift problem, not a grant-time decision problem. The article’s core finding is that access is often broadened under deadline and then left in place after the immediate task has changed. That means the relevant control point is not only approval, but post-deployment scope convergence between intent and observed behaviour. Practitioners should stop treating the access request as the end of governance.
A few things that frame the scale:
- Systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems. Organisations failing to scope AI access properly are 4.5x more likely to experience a security incident, according to the 2026 Infrastructure Identity Survey.
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How do IAM teams account for AI agent access in a Zero Trust model?
A: Treat the agent as a non-human identity that begins with no standing access and earns specific reach only when needed. Zero Trust for agents means per-task, per-system authorisation, plus ongoing comparison of granted scope and real behaviour. The model fails if it stops at provisioning.
👉 Read our full editorial: Over-permissioned AI agents expose a drifting identity control gap