Topic starter
20/04/2026 7:26 pm
TL;DR: AI agents are non-deterministic, goal-oriented identities that can pursue an objective through any available path, making historical-behaviour IAM patterns a poor fit; Token Security says its response is intent-based access management, according to its RSAC Conference 2026 Brand Spotlight. The governance problem is now visibility, accountability, and least-privilege enforcement for autonomous agents, not just deployment speed.
Editorial analysis by NHI Mgmt Group, based on content published by Token Security: “Giving AI Agents an Identity — and a Leash | A Brand Spotlight at RSAC Conference 2026 with Itamar Apelblat and Ido Shlomo of Token Security”.
Key questions
Q: What breaks when AI agents are treated like standard human users?
A: You lose visibility into effective permissions, expected behaviour, and real blast radius.
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 signals show that AI agent access is outside governance boundaries?
A: Look for first-time role assumptions, unusual secret retrieval, access to endpoints outside the normal workflow, and activity that appears only in partial telemetry.
Practitioner guidance
- Inventory every deployed agent Map where AI agents exist, which business applications they touch, and who is accountable for each identity before writing policy exceptions.
- Bind permissions to task intent Define what each agent is allowed to accomplish, then constrain actions and target systems to that stated purpose rather than to prior behaviour.
- Split broad agents into narrow roles Break multi-purpose agents into smaller units with clearly bounded responsibilities so a single identity cannot reach every system needed for the workflow.
Bottom line: AI agent governance fails when teams assume runtime behaviour can be inferred from past activity, because goal-driven agents can choose different paths to the same outcome.
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This topic was modified 6 days ago by NHI Mgmt Group
06/10/2026 4:28 pm
Intent, not history, becomes the governance primitive for AI agents: historical-behaviour IAM assumes the actor's future access can be inferred from its past actions. That assumption collapses when the actor is an AI agent because runtime choices can change from session to session while the objective stays the same. The implication is that identity governance must stop treating prior behaviour as a reliable permission model for agents.
A few things that frame the scale:
- 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.
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Should organisations use a dedicated AI agent identity model or extend current NHI controls?
A: Extend current NHI controls first, but only if they include ownership, scope, lifecycle, and revocation discipline. The mistake is treating AI agents as just another service account when they may combine permissions dynamically at runtime. A dedicated model is warranted when delegation chains span multiple applications and control ownership is unclear.
👉 Read our full editorial: AI agent identity and intent-based access management at RSAC 2026