A credential that lets software authenticate to AI or machine-learning services without a human in the loop. In practice, this includes tokens, keys, and service secrets used for model hosting, inference, or MLOps platforms, where exposure can create direct access to sensitive workloads and usage spend.
Expanded Definition
An AI/ML credential is the machine-authenticating secret that allows software to access model endpoints, training pipelines, feature stores, vector databases, and MLOps tooling without a human present. In NHI security, the credential itself is the identity proof, so its scope, rotation, and storage model matter as much as the workload it unlocks.
Definitions vary across vendors because some teams use the term narrowly for API keys and bearer tokens, while others include cloud access keys, service account secrets, certificates, and short-lived federation tokens. NHI Management Group treats the term as a workload credential class, not a product-specific artifact. That distinction matters because the same credential may authorize inference calls, model deployment, experiment tracking, or data export, each with different blast radius and audit requirements. The most useful reference points are the OWASP Non-Human Identity Top 10 and NIST SP 800-63 Digital Identity Guidelines, even though neither was written specifically for AI workloads.
The most common misapplication is treating an AI/ML credential like a normal developer API key, which occurs when teams grant broad, persistent access to production model services and shared MLOps platforms.
Examples and Use Cases
Implementing AI/ML credentials rigorously often introduces lifecycle complexity, requiring organisations to weigh automation and uptime against tighter secret control and faster revocation.
- A training job uses a short-lived token to pull data from object storage and write model artifacts into a registry, reducing exposure compared with a long-lived static secret. See the distinction between static and dynamic secrets in the Ultimate Guide to NHIs — Static vs Dynamic Secrets.
- An inference service authenticates to a hosted model endpoint with a service secret, but the credential is limited to one environment and one workload to prevent lateral access.
- A data science platform uses a federated identity flow rather than a manually copied key, aligning better with modern identity guidance from NIST SP 800-63 Digital Identity Guidelines.
- During incident review, investigators trace an exposed token to over-privileged access across notebooks, registries, and experiment tracking. Similar exposure patterns appear in the CI/CD pipeline exploitation case study.
- Security teams also study the Guide to the Secret Sprawl Challenge when credentials are copied into build logs, chat tools, or shared configuration files.
Why It Matters in NHI Security
AI/ML credentials matter because they collapse identity, authorization, and cost exposure into a single secret. If one is leaked, an attacker may not just view a service. They can invoke models, exfiltrate prompts or outputs, poison pipelines, or generate spend spikes that look like legitimate usage until the damage is already underway. NHIMG research shows 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, which is why AI workload secrets must be treated as immediate-response assets rather than passive configuration.
This risk is amplified in shared MLOps environments where developers, data scientists, and automation agents reuse the same identity material across notebooks, jobs, and deployment steps. The LLMjacking: How Attackers Hijack AI Using Compromised NHIs report highlights how compromised non-human identities become a direct path into AI services, while the Guide to the Secret Sprawl Challenge shows how unmanaged secret distribution increases that attack surface.
Organisations typically encounter the operational impact only after an exposed token is abused, at which point AI/ML credential scope, rotation, and revocation become 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 SP 800-63 and NIST CSF 2.0 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 for non-human identities and workload access. |
| NIST SP 800-63 | Provides identity assurance principles that inform machine authentication strength. | |
| NIST CSF 2.0 | PR.AC-1 | Identity and access control principles apply directly to workload credentials. |
Use federated, short-lived credentials where possible and avoid reusable shared secrets.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 9, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
