How should security teams govern service accounts in AI factories?

Security teams should treat service accounts in AI factories as high-value non-human identities with clear owners, scope, and expiry dates. They should be provisioned through a central directory or identity platform, rotated on schedule, and removed when the workload ends. If an AI pipeline can run without anyone knowing who owns the account, governance has already failed.

Why This Matters for Security Teams

AI factories concentrate service accounts at the exact point where data, models, orchestration, and deployment pipelines meet. That makes them high-value non-human identities, not administrative conveniences. When those accounts are shared, long-lived, or undocumented, they become ideal targets for credential theft, lateral movement, and silent pipeline tampering. NIST Cybersecurity Framework 2.0 frames this as a governance and access control problem, but in AI environments the blast radius often expands faster because automated workflows keep running after human oversight is lost.

NHIMG research shows how quickly exposed credentials can be abused in practice, and the same pattern applies when AI pipeline secrets are left without clear ownership or expiry. The Top 10 NHI Issues and the Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs both reinforce the same operational point: service accounts must be governed as identities with lifecycle controls, not as static config entries. In practice, many security teams discover service account sprawl only after a pipeline has already been abused or a leaked key has already been reused.

How It Works in Practice

Governance starts by assigning every service account an owner, a purpose, a scope, and an expiry condition. That record should live in the central identity platform or directory, not in a wiki page or a pipeline note. Each account should be tied to a specific workload, environment, and change ticket, with the minimum permissions needed to complete its function. For AI factories, that usually includes model training jobs, feature stores, artifact repositories, evaluation pipelines, deployment orchestrators, and retrieval systems.

Security teams should prefer short-lived credentials over static secrets wherever the platform allows it. Just-in-time issuance reduces the window for abuse, and scheduled rotation limits the value of any credential that is exposed. Current guidance suggests combining directory-backed identity management with secrets inventorying so that accounts, tokens, certificates, and API keys can be reviewed together rather than as separate problems. The Ultimate Guide to NHIs — Regulatory and Audit Perspectives is useful here because it frames lifecycle evidence, review cadence, and deprovisioning as audit artifacts, not optional hygiene.

• Require named ownership for every service account, including a technical owner and a business owner.
• Bind credentials to a workload identity where possible, rather than embedding long-lived secrets in code or CI/CD variables.
• Set rotation SLAs and expiry dates that are short enough to matter, then verify revocation after job completion.
• Log every token issuance, privilege change, and access to model or dataset resources.
• Remove or disable the account automatically when the workload is retired, migrated, or no longer approved.

The NIST Cybersecurity Framework 2.0 supports this model by emphasizing governance, asset management, and access control as continuous functions rather than one-time setup tasks. These controls tend to break down when AI factories use ad hoc service accounts embedded in notebooks, unmanaged CI jobs, or cross-team automation because no single system can reliably enforce ownership or revocation.

Common Variations and Edge Cases

Tighter service account governance often increases operational overhead, requiring organisations to balance automation speed against auditability and revocation discipline. That tradeoff becomes sharper in AI factories because experimentation teams often want rapid access to datasets, GPUs, and deployment endpoints while security teams need bounded identity scope and traceable change history.

There is no universal standard for every platform pattern yet. Some environments can move to federated workload identity quickly, while others still depend on vaulted secrets for legacy tooling. In those cases, best practice is evolving toward compensating controls: shorter TTLs, stronger segmentation, more frequent review, and aggressive cleanup of dormant accounts. The key is to avoid confusing temporary exceptions with permanent architecture.

Where service accounts touch multi-tenant training environments, external model APIs, or shared vector databases, hidden privilege creep is a common failure mode. The 52 NHI Breaches Analysis and Ultimate Guide to NHIs — What are Non-Human Identities both support the same conclusion: if the account cannot be traced, scoped, and retired with confidence, it is already a governance gap, even if the pipeline still appears healthy.

Related resources from NHI Mgmt Group

• How should security teams govern Active Directory service accounts?
• How should security teams govern non-human identities alongside human accounts?
• How should security teams govern API keys used for generative AI access?
• How should security teams govern AI agents that use service accounts and MCP tools?