Topic starter
08/04/2026 3:06 pm
TL;DR: Akeyless says AI agents are moving beyond static credentials and OAuth tokens into systems that modify production environments, while traditional RBAC cannot evaluate intent, context, or real-time behaviour. The governance assumption that access can be decided once and reviewed later breaks when agents act at execution speed, not human pace.
Editorial analysis by NHI Mgmt Group, based on content published by Akeyless: “Akeyless Launches Runtime Authority for AI Agents, Introducing Intent-Aware Security for Autonomous Systems”.
By the numbers:
- Akeyless says it is building on more than 220 billion machine identity interactions.
Key questions
Q: What breaks when multi-agent AI systems rely only on static RBAC and long-lived credentials?
A: Static RBAC breaks down when an LLM chooses a novel sequence of tool calls or crosses into a new task path.
Q: Why do AI agents increase identity risk even when the login succeeds?
A: A successful login only proves that the agent reached the system.
Q: What are the signs that AI governance is failing in the enterprise?
A: Common warning signs include rapid growth in AI use without matching policy coverage, sensitive files being copied into personal accounts, and a large share of AI apps carrying high or critical risk.
Practitioner guidance
- Define per-action authorisation for AI agents Map each agent task to a policy decision that evaluates intent, context, and target system before the action executes.
- Bind sessions to continuous revocation Ensure agent sessions can be terminated or revoked instantly when an action deviates from the approved scope.
- Inventory every agent and its delegated reach Maintain a current record of agent identities, connected tools, and the systems each agent can touch across environments.
Bottom line: AI agents that can modify production systems expose a governance gap that static credentials and RBAC cannot close on their own.
What's in the full announcement
Akeyless's full article covers the operational detail this post intentionally leaves for the source:
- Intent-aware runtime policy examples for AI agents moving across cloud and on-premises systems
- How Agentic Identity Intelligence tracks agent identities, access paths, and data lineage
- Operational notes on Zero Standing Privilege and just-in-time access for autonomous systems
- The live webinar and product demonstration details for teams evaluating the capability
👉 Read Akeyless's analysis of runtime authority for AI agents and identity governance →
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This topic was modified 3 days ago by NHI Mgmt Group
07/10/2026 8:07 pm
Runtime authority is a control-plane answer to an execution-time identity problem. The article is really about the collapse of the old assumption that access decisions happen before meaningful risk begins. Once an AI agent can select actions dynamically during a task, identity governance has to follow execution rather than merely provision access. The practitioner conclusion is simple: control points must move to the moment of action.
A few things that frame the scale:
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: How should teams govern AI agents that run across multiple runtimes?
A: Teams should govern them with a shared trace schema, consistent evaluation criteria, and clear ownership for tool access. Portability changes the execution layer, but it does not remove the need to prove what the agent did, why it did it, and whether the behaviour stayed inside policy across environments.
👉 Read our full editorial: Akeyless runtime authority reframes AI agent identity governance