Topic starter
22/09/2026 3:43 pm
TL;DR: AI agents are now making live decisions, updating records, triggering workflows, and calling external services, so credential security has shifted from static access management to task-scoped identity and runtime policy enforcement, according to Akeyless. The decisive issue is not whether the agent can act, but whether it should have been allowed to act at that moment, under that intent, with that scope.
NHIMG editorial — based on content published by Akeyless: AI Agent Credential Security and Runtime Access Control
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing access credentials.
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface.
Questions worth separating out
Q: What breaks when AI agents rely on shared service accounts or API keys?
A: Shared credentials hide which actor actually performed the action, make revocation coarse, and blur accountability across humans and machines.
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: How can teams tell whether AI access is actually under control?
A: Look for evidence that access is limited by purpose, not just by account.
Practitioner guidance
- Define a unique identity for each production agent Issue identities per agent and connect each one to an accountable owner and a specific task path.
- Move credentials out of agent-visible context Keep secrets out of prompts, memory, logs, and configuration that the agent can inspect.
- Enforce intent at the request path Evaluate whether the proposed action still matches the assigned objective before the target receives the request.
What's in the full article
Akeyless' full article covers the operational detail this post intentionally leaves for the source:
- Deployment guidance for provisioning a verifiable agent identity without relying on a shared account.
- Runtime enforcement details for intent-based access control on the agent request path.
- Gateway-based access brokering patterns for legacy databases, on-prem systems, and federated targets.
- Checklist questions for production readiness across identity, scope, credential handling, and evidence.
👉 Read Akeyless' guide to AI agent credential security and runtime access control →
AI agent credentials and runtime access control , are your policies ready?
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22/09/2026 3:43 pm
AI agent access breaks the assumption that identity is only a login event. In human and classic service-account governance, identity is usually checked before the workflow begins and then treated as stable for the remainder of the session. That assumption fails when the actor can choose tools and actions at runtime. The implication is that governance has to move from session start to action time, because the decision point has changed.
A few things that frame the scale:
- 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface, according to Ultimate Guide to NHIs.
- Only 20% have formal processes for offboarding and revoking API keys, and even fewer have procedures for rotating them.
A question worth separating out:
Q: Should organisations treat agentic AI access differently from service account access?
A: Yes. Service accounts are usually persistent and can be managed through lifecycle controls, while agentic AI access is often ephemeral, runtime-selected, and initiated on demand. The right governance model is different because the identity behaviour is different. Treating both as the same class leads to control gaps and delayed policy decisions.
👉 Read our full editorial: AI agent credential security is becoming an identity problem
