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01/08/2026 10:46 pm
TL;DR: Agentic AI systems decompose tasks and call tools at runtime, so identity scope, ownership, and revocation now determine blast radius more than authentication alone, according to Akto and joint Five Eyes guidance. Existing IAM models were built for predictable service accounts, not identities whose access can expand, persist, and be misused mid-session.
NHIMG editorial — based on content published by Akto: Governing AI Agent Identities: An Identity Maturity Model for AI Agents
Questions worth separating out
Q: How should security teams govern AI agents that inherit authority from other identities?
A: Security teams should govern AI agents by tracking identity lineage, not just credentials.
Q: Why do AI agents complicate least-privilege access?
A: AI agents can change their path to a goal, so a role that looks narrow at provisioning time may still be too broad at runtime.
Q: What breaks when organisations audit AI agents like service accounts?
A: Audit trails break when teams record only the API call and ignore the prompts, tools, and model outputs that caused it.
Practitioner guidance
- Separate agent identities from human IAM reporting Track AI agent identities as a distinct population with their own owners, scopes, and revocation targets.
- Bind each agent to a named owner and stated purpose Create an accountable owner at creation time and record the specific task the agent is permitted to perform.
- Set a revocation-time objective for every agent credential Measure how fast a credential becomes useless after compromise, misuse, or end of pilot.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- The five-stage AI agent identity maturity model and the defining criterion for each stage.
- The six pre-production requirements Akto says a deployment should meet before going live.
- The distinction between discovery, contextualisation, right-sizing, and governed control.
- Why the article argues time to revocation is the metric that matters most.
👉 Read Akto's analysis of AI agent identity maturity and IAM limits →
AI agent identity maturity: what breaks in traditional IAM?
Explore further
01/08/2026 10:54 pm
AI agent identity governance fails when organisations treat runtime behaviour like static service accounts. The article's core point is that an agent does not just hold access, it changes what access means by decomposing tasks and selecting tools mid-session. That breaks the assumption that least privilege can be set once at provisioning time. The implication is that identity governance for agents has to start from runtime scope, not inherited role design.
A few things that frame the scale:
- 88.5% of organisations acknowledge that their non-human IAM practices lag behind or are merely on par with their human identity and access management efforts, according to The 2024 Non-Human Identity Security Report.
- 59.8% of organisations see value in a solution that simplifies non-human access management and introduces dynamic ephemeral credentials.
A question worth separating out:
Q: Who should own revocation for AI agent and service account access?
A: Ownership should sit with the team that can revoke access in time and understand the operational purpose of the identity. If no one can act before the chain completes, accountability is only theoretical and the control model is already too slow.
👉 Read our full editorial: AI agent identity maturity exposes the limits of traditional IAM
