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06/09/2026 5:18 pm
TL;DR: Traditional IAM fails AI systems because it assumes static roles, one-time authorization, and human-paced workflows, while AI agents act dynamically across APIs, data, and tools, according to LEVO. The result is over-permissioned agents, weak auditability, and compliance gaps, and access control must shift toward continuous, context-aware identity validation.
NHIMG editorial — based on content published by LEVO: How IAM, purpose built for AI protects against breaches, simplifies compliance, and unlocks safe, scalable deployment of AI systems
By the numbers:
- 73% of enterprises have already experienced at least one AI related breach.
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities , 46% confirmed, 26% suspected.
Questions worth separating out
Q: Why do static IAM controls break down for AI agent execution?
A: Static IAM assumes privileges can be defined and reviewed in advance, but AI agents make decisions at runtime and can change their execution path during the session.
Q: Why do AI systems make compliance harder for security and risk teams?
A: AI systems make compliance harder because they change quickly, connect to many services, and often access data through delegated identities rather than direct human logins.
Q: How do organisations know whether AI identity monitoring is actually working?
A: Monitoring is working when teams can see which agent initiated each action, which tool was used, what data was touched, and whether the sequence matches the approved purpose.
Practitioner guidance
- Define AI systems as governed non-human identities Classify each AI workload, agent, or assistant as a distinct identity subject with named ownership, scoped authority, and a lifecycle record.
- Replace static roles with runtime policy checks Apply request-time decisions for tool calls, dataset access, and workflow actions so privilege is evaluated against context instead of inherited from a broad login role.
- Preserve delegation chains in logs and tokens Carry origin, agent instance, and sub-agent context through every hop so audit and incident response can reconstruct how an action was authorised and executed.
What's in the full article
LEVO's full blog covers the operational detail this post intentionally leaves for the source:
- How the vendor maps AI-native controls to runtime identity, delegation, and auditability patterns.
- The specific examples used to distinguish static IAM from continuous, context-aware access control.
- Implementation detail on how the source frames AI-native identity across APIs, RAG flows, and autonomous workflows.
- The article's final positioning on how AI identity control supports compliance and scalable deployment.
👉 Read LEVO's analysis of why traditional IAM fails AI systems →
AI native IAM: are static roles keeping up with agent behavior?
Explore further
06/09/2026 5:19 pm
Static IAM is no longer a sufficient control plane for AI-native applications. The core problem is not simply that AI is faster, but that it alters the shape of access after authorisation has already been granted. Traditional IAM assumes the access request is known in advance and that the session will remain within its original boundaries. AI systems break that assumption by selecting tools and data dynamically, which means governance must follow runtime behaviour rather than static entitlement.
A few things that frame the scale:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, according to The 2024 ESG Report: Managing Non-Human Identities.
- The average organisation believes more than 1 in 5 of their non-human identities are insufficiently secured, which shows how broad the exposure window already is.
A question worth separating out:
Q: What should teams do when AI agent tool access changes mid-session?
A: Teams should treat mid-session tool changes as an access event, not a routine operational detail. The access state should be re-evaluated immediately, and any correlated runtime behaviour should be reassessed before the agent continues. Otherwise, the system may continue acting under an outdated understanding of privilege.
👉 Read our full editorial: Why traditional IAM fails AI systems and what must replace it
