How should security teams govern AI service credentials in production?

Why This Matters for Security Teams

AI service credentials are not just application plumbing when the workload can act autonomously, chain tools, or make its own decisions about what to do next. They become production identities with real blast radius. That is why current guidance treats them more like an NHI control problem than a developer convenience problem. The moment a token, key, or certificate is left broad, long-lived, or undocumented, it becomes a path to data movement, model abuse, or infrastructure misuse.

The risk is not theoretical. Entro Security reported that when AWS credentials are exposed publicly, attackers attempt access in an average of 17 minutes, and sometimes in as little as 9 minutes, in its research on LLMjacking: How Attackers Hijack AI Using Compromised NHIs. That speed matters because AI services often run continuously and may touch code, data, and internal APIs in one workflow. For governance, this means teams need owner assignment, expiry, and usage traceability from day one, not after deployment. The control model should align with OWASP Non-Human Identity Top 10 and the identity discipline described in NIST Cybersecurity Framework 2.0.

In practice, many security teams encounter credential abuse only after an agent has already moved beyond its original task and touched systems nobody expected it to reach.

How It Works in Practice

Effective governance starts by treating every AI service credential as a workload identity with a defined owner, purpose, and expiry. Long-lived static secrets should be the exception, not the default. Where possible, issue dynamic secrets or short-lived tokens that are bound to a service, a job, or a specific runtime context. This is especially important for systems that call external tools or operate as agents, because their access needs can change per task.

Good practice is usually a stack of controls rather than one control:

• Assign a business and technical owner for each credential, including renewal and revocation responsibility.
• Limit scope to the minimum API, environment, or data domain required for the service to function.
• Use JIT issuance where possible, so credentials exist only for the duration of the job or session.
• Track where secrets are stored, injected, rotated, and logged, including CI/CD, orchestration, and agent toolchains.
• Prefer workload identity patterns and policy evaluation at request time over static allowlists alone.

NHI governance also needs visibility into where credentials spread. The Guide to the Secret Sprawl Challenge shows why secrets tend to multiply across pipelines, repos, and runtime layers, which makes inventory and rotation inseparable. For control design, pair that with NIST SP 800-63 Digital Identity Guidelines for authentication assurance and with identity governance patterns in Top 10 NHI Issues. These controls tend to break down when credentials are baked into containers, copied into notebooks, or shared across multi-agent pipelines because revocation and attribution stop being reliable.

Common Variations and Edge Cases

Tighter credential control often increases operational overhead, so organisations have to balance security benefit against deployment friction and release speed. That tradeoff is real, especially in environments with fast-moving AI experimentation or many ephemeral jobs. Current guidance suggests that the answer is not to relax control, but to automate it so developers are not manually handling secrets.

One common edge case is batch or event-driven AI processing, where a service only runs for minutes but still needs access to sensitive tools. In those cases, JIT credentials and automatic revocation fit well, but the policy engine must be able to evaluate context at runtime. Another edge case is autonomous agents that can select tools dynamically. Static RBAC alone often becomes too blunt, because the agent’s next action is not fully known in advance. Best practice is evolving toward intent-based authorisation, where access is granted based on what the agent is trying to do, in that moment, under policy.

There is no universal standard for this yet, so teams should anchor governance in auditability, expiry, and least privilege while tracking emerging guidance from LLMjacking lessons and the control expectations reflected in OWASP Non-Human Identity Top 10 and NIST Cybersecurity Framework 2.0. The hardest failures usually show up when an AI service credential outlives the service, especially after a model, pipeline, or vendor integration changes without a corresponding access review.

Related resources from NHI Mgmt Group

• How should security teams govern API keys used for generative AI access?
• How should security teams govern Active Directory service accounts?
• How should security teams govern non-human identities at scale?
• How should security teams govern non-human identities for compliance?