Topic starter
31/10/2025 12:18 pm
TL;DR: Akeyless argues that AI agents outpace traditional IAM because static roles, standing credentials and delayed review cannot constrain autonomous actions that complete in milliseconds. The governance shift is from access review to runtime authority, where intent, scope and auditability become the control points.
Editorial analysis by NHI Mgmt Group, based on content published by Akeyless: “Identity Security for AI Agents”.
Key questions
Q: How should security teams govern AI agents that can change actions at runtime?
A: Security teams should govern runtime AI by correlating identity, data, and intent before trusting an action path.
Q: Why do autonomous agents make traditional access reviews less effective?
A: Access reviews assume permissions persist long enough to be observed, challenged, and recertified.
Q: What breaks when AI agents keep standing credentials?
A: The access model breaks because the agent can continue acting after the human has moved on, the workflow has shifted, or the original approval is no longer relevant.
Practitioner guidance
- Map every AI agent and its connected identities Build an inventory of agents, the credentials they use, and the systems they can touch across cloud, Kubernetes, SaaS and development environments.
- Remove standing credentials from agent workflows Replace persistent secrets with just-in-time, task-scoped issuance so agents never store reusable credentials in code, memory or configuration.
- Enforce intent-based policy at execution time Evaluate each agent request against approved purpose before access is granted, and block commands that exceed the declared scope.
Bottom line: AI agents expose a governance gap that static IAM was never designed to close, because control has to follow runtime intent rather than provisioning history.
What's in the full article
Akeyless's full article covers the operational detail this post intentionally leaves for the source:
- Gateway-level interception and inspection of live agent commands across SSH, databases, Kubernetes and cloud APIs
- How the platform traces prompt, decision, policy and outcome into a tamper-proof forensic record
- The discovery model for mapping AI agents, the identities they use and the systems they touch
- How secretless just-in-time issuance is positioned alongside runtime authority in a continuous control loop
👉 Read Akeyless's analysis of AI agent runtime identity controls and intent-based access →
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This topic was modified 11 months ago by Abdelrahman
This topic was modified 5 days ago by NHI Mgmt Group
07/10/2026 6:21 pm
Intent is becoming the new authorization boundary for autonomous systems: static roles do not answer the question that now matters most, which is whether an AI agent is trying to do something policy-aligned at runtime. The article is pointing to a control model where identity alone is insufficient because the execution choice is made dynamically. That makes intent-aware enforcement a governance issue, not a product feature.
A few things that frame the scale:
- Only 37% of organisations had policies for managing AI or detecting shadow AI, according to IBM's 2025 Cost of a Data Breach Report.
A question worth separating out:
Q: How do organizations prove AI agent controls are actually working?
A: Organizations prove control effectiveness by showing which agents accessed which data, what actions they executed, and whether those actions stayed within approved task boundaries. Useful evidence includes logs, policy decisions, anomaly alerts, and review records. Without that chain, governance is mostly declarative.
👉 Read our full editorial: AI agent runtime identity controls are shifting IAM to intent