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.
At a glance
What this is: Akeyless frames AI agent security around runtime authority, arguing that static credentials and role-based controls do not govern autonomous action well enough once agents start executing workflows in production.
Why it matters: This matters because identity teams now have to govern agent behaviour at the moment of action, not just at provisioning time, which changes how NHI, IAM, and emerging agentic AI controls are designed and audited.
By the numbers:
- Akeyless says it is building on more than 220 billion machine identity interactions.
👉 Read Akeyless's analysis of runtime authority for AI agents and identity governance
Context
AI agent runtime authority is a governance problem about whether an identity control can decide, constrain, and revoke actions while an agent is executing. The article argues that static credentials, OAuth tokens, and role-based access controls were built for access decisions, not for runtime judgement across production systems.
The article ties that gap to a broader shift in AI agent behaviour: agents are no longer only reading information, they are modifying systems, running workflows, and touching internal databases and infrastructure. That makes the control point the moment of action, not the enrollment or provisioning step.
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. Fixed permissions do not evaluate the immediate context, so they can miss privilege escalation, indirect prompt injection, or other unexpected execution paths. Long-lived credentials also widen exposure, because the agent keeps more access than the current task requires.
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. It does not prove that the specific action was authorised for that task, in that environment, at that moment. AI agents can chain tools and cross systems, so the authorisation problem moves to runtime intent and not just initial authentication.
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. Another indicator is weak visibility into who is using which tools and what data they are sending. If teams cannot answer those questions, governance is not working as intended.
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.
How it works in practice
Why static credentials fail for autonomous AI agents
Static credentials and bearer tokens assume that possession plus policy equals safe use. That model works when access requests are bounded and predictable, but it breaks when an AI agent can select actions dynamically during execution. OAuth tokens, API keys, and ordinary RBAC can prove that a subject is allowed to enter a system, yet they do not evaluate the intent behind each discrete action or the context in which it is taken. In agentic environments, the control problem shifts from authentication to action authorisation, and that is a different enforcement surface entirely.
Practical implication: treat agent authorisation as an execution-time control problem, not a one-time access grant.
How runtime enforcement differs from JIT and ZSP
Just-in-time access and zero standing privilege reduce persistence, but they still assume the request can be safely approved before work begins. Runtime authority goes further by checking each action as it occurs and by allowing the session to be interrupted mid-task. That is important for AI agents because they can traverse multiple systems in milliseconds, and the risk is not only long-lived privilege but also rapid privilege use across clouds, databases, and internal tools. The security boundary moves from credential issuance to continuous decisioning during the session.
Practical implication: align JIT and ZSP with per-action policy checks when agents can move faster than human review cycles.
Why forensic traceability becomes part of authorisation
The article also links runtime control to evidence. If every action is tied back to the originating prompt and continuously monitored, the identity record becomes both a control plane and an audit trail. That matters because agent behaviour can be distributed across prompts, tools, and downstream systems, making post-event reconstruction difficult unless lineage is preserved. In practical terms, agent governance needs traceability for accountability, not just blocking. Otherwise, organisations may know an action happened without being able to explain which instruction, identity, or policy decision allowed it.
Practical implication: preserve prompt-to-action lineage so policy decisions and post-event reviews can be reconciled.
NHI Mgmt Group analysis
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.
Static privilege models were designed for predictable subjects, not for actors that can change behaviour mid-session. Role-based access control, OAuth tokens, and ordinary credentialing were built for identities whose intent is legible at provisioning time. That assumption fails when an agent decides what to do next after observing live system state. The implication is that least privilege becomes an execution property, not just a grant-time property.
Ephemeral access is not enough when the agent can still misuse it instantly. Zero standing privilege and just-in-time access reduce persistence, but they do not by themselves solve action-level misuse in milliseconds. A new named concept emerges here: runtime authority gap: the space between approved access and safe action when the control plane stops at entry. Practitioners should treat that gap as a governance boundary, not a tooling nuance.
Traceability is now part of authorisation, not just post-incident forensics. When agent actions must be tied back to prompts, policy decisions, and session state, the audit record becomes the proof of control. That changes identity governance from a static inventory exercise into an evidence model for autonomous behaviour. The practitioner conclusion is that agent identities need explainable lineage as much as constrained privilege.
AI agent governance is converging with machine identity governance, but it is not identical. The same NHI discipline still applies to secrets, lifecycle, and privilege scope, yet runtime behaviour introduces a new layer that classic machine identity controls do not cover. That means identity teams cannot simply extend workload patterns to agents and expect coverage to hold. The practitioner conclusion is to govern agent identity as a runtime system, not a static workload.
From our research library:
- 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.
- Read next: AI Agent Authorisation Guide
What this signals
Runtime authority gap: AI agent governance now depends on closing the space between authorised access and safe action. When agents can decide and execute in the same session, access reviews alone cannot prove control because the risky decision may already be over before review begins.
The practical shift for identity programmes is from entitlement management to execution governance. Organisations that keep treating agents like ordinary service accounts will miss the fact that intent, context, and action timing are now part of the access decision, not just the audit trail.
For practitioners
- 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.
- Preserve prompt-to-action lineage Record the originating prompt, policy decision, and executed action so governance teams can explain why a specific step was allowed.
Key takeaways
- AI agents that can modify production systems expose a governance gap that static credentials and RBAC cannot close on their own.
- The article positions runtime authority and continuous monitoring as the missing layer between access approval and safe execution.
- Identity teams need to govern agent behaviour at the moment of action, with lineage and revocation built into the control model.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | ASI03 — Identity & Privilege Abuse | The article centres on agent identity, delegated privilege, and runtime misuse of authorised access. |
| Recommendation — Apply ASI03 to bound agent privileges by action and session, not just by initial access grant. | ||
| OWASP Non-Human Identity Top 10 | NHI-04 — Insecure Authentication | The article challenges authentication and authorisation patterns used for AI agents and delegated access. |
| NHI-05 — Overprivileged NHI | The launch focuses on eliminating excess reach and constraining what agent identities can do. | |
| NHI-07 — Long-Lived Secrets | The article contrasts static credentials with runtime-controlled access for autonomous systems. | |
| Recommendation — Review AI agent authentication flows for assumptions that stop at login and ignore runtime action control. Map each agent identity to the minimum action set it needs and remove broad standing reach. Replace long-lived agent secrets with short-lived credentials and enforce session-level revocation. | ||
| NIST AI RMF | GOVERN — AI Governance and Accountability | The article is fundamentally about governance, accountability, and controlled AI use in production. |
| Recommendation — Establish governance roles and escalation paths for AI agent actions before deployment. | ||
Key terms
- Runtime authority: Runtime authority is the permission an AI system has while it is actively deciding and acting, not just when it is approved. In governance terms, it is the point where access, tool use, and action scope become operational, which is why build-time review alone cannot prove safety.
- Agent Identity: An agent identity is the set of attributes, credentials and permissions assigned to an autonomous software entity. It is treated as a non-human identity because it can authenticate, act on systems and accumulate access over time, which creates governance, audit and lifecycle obligations similar to other production identities.
- Zero Standing Privilege: A control model in which an identity does not keep persistent access unless it is actively needed. For NHIs, this means credentials and permissions are issued for a narrow task and then removed. It reduces the time window and reuse value of stolen access.
- Prompt-to-Action Lineage: Prompt-to-action lineage is the traceable chain from the instruction given to an AI agent through the policy decision and the action it actually performed. It is essential for accountability because it shows how intent, authority, and execution relate when behaviour changes in real time.
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
Deepen your knowledge
NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.
Published by the NHIMG editorial team on June 23, 2026.
Updated on October 7, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org