A credential used by an AI service, model, or pipeline to access data, tools, or billing systems. In practice, it functions like a privileged machine secret and should be governed with ownership, scope, rotation, and revocation controls rather than treated as ordinary configuration.
Expanded Definition
An AI credential is the access material that lets an AI service, model, or pipeline act on protected resources, usually by reaching APIs, datasets, toolchains, or billing systems. In NHI security, it should be treated as a privileged machine secret, not as ordinary configuration. That distinction matters because the credential often controls real execution authority, not just read-only access.
Usage in the industry is still evolving. Some teams mean an API key bound to a model endpoint, while others include OAuth tokens, workload identities, certificates, or short-lived access tokens used by agents. The practical boundary is whether the credential allows an autonomous system to consume, modify, or spend resources without a human in the loop. For that reason, AI credentials overlap with the broader concerns described in the OWASP Non-Human Identity Top 10 and with the credential assurance logic in NIST SP 800-63 Digital Identity Guidelines.
The most common misapplication is storing an AI credential as a long-lived app setting, which occurs when teams prioritize deployment speed over scoped ownership, rotation, and revocation.
Examples and Use Cases
Implementing AI credentials rigorously often introduces operational friction, requiring organisations to weigh rapid agent deployment against tighter scoping, rotation, and incident response readiness.
- A customer-support agent uses a short-lived token to retrieve account history from a CRM, with permissions limited to a single tenant and a single tool.
- A data-processing pipeline uses an API key to call a model provider for classification, then rotates the key after each release cycle or usage threshold.
- An autonomous coding agent authenticates to a repository and CI/CD system using a workload identity, not a shared developer secret, so actions are attributable and revocable.
- A billing-integrated model uses a dedicated credential for metering and quota checks, separated from the credential used to access training data or internal tools.
- Security teams review exposed credential patterns against NHIMG research such as the CI/CD pipeline exploitation case study and compare them with guidance in the Ultimate Guide to NHIs.
These examples align with the way OWASP Non-Human Identity Top 10 treats non-human access as an identity problem rather than a simple secrets problem.
Why It Matters in NHI Security
AI credentials are a high-value target because compromise turns an AI workload into an obedient proxy for theft, data exposure, or cost abuse. When they are over-scoped, shared across environments, or left unrotated, attackers can move from a single leaked token to data exfiltration, prompt injection abuse, unauthorized tool calls, or cloud spend escalation. NHIMG research on the Guide to the Secret Sprawl Challenge shows how quickly secrets become ungovernable once they are copied into pipelines, logs, chat, and deployment artifacts.
The risk is not theoretical: Entro Security reports that when AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes, and as quickly as 9 minutes in some cases. That speed means a leaked AI credential can become an incident before a normal review cycle even starts. This is why NHI programs treat ownership, rotation, revocation, and least privilege as core controls, not optional hygiene. It also matches the reality described in the 2024 Non-Human Identity Security Report, where many organisations still lag in non-human IAM maturity.
Organisations typically encounter the impact only after a suspicious bill, a strange tool invocation, or a data access alert, at which point AI credential governance becomes operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST Zero Trust (SP 800-207) set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-02 | Covers improper secret handling and machine identity abuse for non-human access. |
| NIST CSF 2.0 | PR.AA-01 | Requires identities and authenticators to be managed before systems are allowed access. |
| NIST Zero Trust (SP 800-207) | PA-1 | Zero trust assumes no implicit trust for workload credentials or service access. |
Inventory AI credentials, scope them tightly, and rotate or revoke any secret that is shared or exposed.
Related resources from NHI Mgmt Group
- What breaks when an AI agent is given a generic service credential?
- How should security teams implement AI agent credential management?
- How can teams tell whether a suspicious AI repo has already caused credential theft?
- How should security teams handle credential sprawl across humans, NHIs, and AI workflows?
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 20, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
