How should security teams handle AI credentials in secrets management programmes?

Why This Matters for Security Teams

AI credentials belong in the same risk conversation as production secrets, but they behave differently enough that generic secret handling often misses the real exposure. They may unlock model hosting, inference endpoints, training data, orchestration layers, and billing surfaces, so misuse can create data loss, service abuse, or sudden cost escalation. Current guidance suggests treating them as high-value operational secrets rather than routine API keys, with controls that reflect their blast radius. The OWASP Non-Human Identity Top 10 is useful here because it frames the identity and secret risks that appear once software starts acting on behalf of systems, not people.

Security teams also need to account for how quickly exposed credentials are abused. NHIMG research on Shai Hulud npm malware campaign and the LLMjacking: How Attackers Hijack AI Using Compromised NHIs analysis both show that exposed secrets are often weaponised quickly, not eventually. In practice, many security teams encounter AI credential abuse only after spend spikes, model misuse, or data egress has already occurred, rather than through intentional secret lifecycle review.

How It Works in Practice

Handling AI credentials well starts with classification. Teams should separate credentials that access models, inference services, fine-tuning pipelines, vector stores, and agent toolchains from ordinary app secrets, then apply tighter policy, ownership, and monitoring. The Guide to the Secret Sprawl Challenge is a useful reminder that fragmentation is itself a control failure: once AI credentials are scattered across CI/CD, notebooks, container images, and developer laptops, rotation and revocation become unreliable.

A practical programme usually combines five controls:

• Inventory all AI-related secrets separately, including service accounts, API keys, model provider tokens, and tool access tokens.
• Prefer short-lived or scoped credentials over static long-lived keys, especially where workload identity is available.
• Use strong ownership and approval for issuance, with access limited to named services or pipelines.
• Monitor for unusual usage patterns such as abnormal token volume, geographic anomalies, or unexpected model calls.
• Automate rotation and revocation on deployment, staff change, incident response, and provider compromise.

Where possible, align this with centralised secrets management and the broader control objectives described in NIST Cybersecurity Framework 2.0, while using the lifecycle approach in NHIMG’s Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs to make issuance, review, and retirement explicit. These controls tend to break down when AI credentials are embedded inside fast-moving developer workflows because teams cannot distinguish legitimate automation from credential reuse.

Common Variations and Edge Cases

Tighter secret control often increases workflow friction, requiring organisations to balance developer velocity against blast-radius reduction. That tradeoff is especially visible in experimentation-heavy AI teams, where notebooks, sandboxes, and one-off integrations can tempt engineers to reuse persistent tokens. Best practice is evolving here, and there is no universal standard for how much autonomy should be allowed in non-production AI environments.

One edge case is agentic AI. If an AI agent can chain tools, trigger workflows, or request new access mid-task, static secret rotation alone is not enough. In those environments, current guidance suggests moving toward workload identity, context-aware authorisation, and just-in-time credentials so access is issued for a specific task and revoked on completion. That is more aligned with the operational intent in the NIST SP 800-63 Digital Identity Guidelines and the threat framing in the Top 10 NHI Issues.

Another common exception is vendor-managed AI services, where customers may not control the full credential lifecycle. In those cases, teams should focus on least privilege, segmented provider accounts, and fast revocation paths. The right question is not whether a secret exists, but whether its scope, lifetime, and observability match the operational risk of the AI workload.

Related resources from NHI Mgmt Group

• How should security teams handle exposed secrets in AI-driven environments?
• How should security teams handle secrets in AI-generated code?
• How should security teams handle AI client access to governed data without shared secrets?
• How should security teams handle credentials inside AI coding agent sandboxes?