AI infrastructure growth is exposing privileged access gaps

At a glance

What this is: This is Keeper Security’s argument that rapid AI infrastructure growth is expanding privileged access risk faster than legacy PAM and perimeter controls can handle.

Why it matters: It matters because AI infrastructure now blends human, NHI, and privileged operational access across hybrid environments, so IAM teams need controls that address standing privilege, secrets, and session visibility together.

By the numbers:

• Global AI infrastructure spending is projected to surpass $200 billion by 2028.
• U.S. AI-specific data centers are expected to account for almost half of the country’s electricity demand growth through 2030.
• Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap.
• Systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems.

👉 Read Keeper Security's analysis of why AI infrastructure needs modern PAM

Context

AI infrastructure is no longer just a compute story. It is an identity and privileged access story, because the growth in GPUs, accelerators, APIs, datasets, and hybrid cloud orchestration expands the number of places where access can be over-granted, exposed, or left standing too long.

The primary governance problem is that legacy security assumptions were built for relatively stable systems and human-paced administration. AI-driven environments change that baseline by multiplying privileged sessions, secret usage, and control-plane access across infrastructure that is constantly moving.

Key questions

Q: How should security teams control privileged access in AI infrastructure?

A: They should treat privileged access as the primary control boundary, not a supporting control. That means inventorying every admin path into compute, storage, pipelines, and APIs, then replacing broad standing rights with time-bound elevation, session monitoring, and continuous validation. The goal is to make access temporary, attributable, and narrowly scoped.

Q: Why do AI pipelines increase secrets management risk?

A: AI pipelines depend on many machine credentials, and those secrets often live in scripts, config files, or automation tools that outlast the task they were created for. That creates durable access even in ephemeral environments. Teams should assume every embedded secret is a governed identity with lifecycle obligations, not a harmless configuration detail.

Q: What breaks when standing privilege is allowed in AI environments?

A: Standing privilege breaks the assumption that privileged access is temporary, visible, and easy to review. In AI infrastructure, that assumption fails quickly because the same credentials can reach multiple control planes, persist across deployments, and be reused long after they were intended to expire. Once that happens, blast radius grows faster than review cycles can respond.

Q: Who is accountable when privileged access compromises AI infrastructure?

A: Accountability should sit with the team that owns the privilege path, not only the team that owns the workload. In practice, that means infrastructure, platform, IAM, and application teams need shared ownership for elevated access, secrets, and session logging. Without that, review becomes fragmented and no one can explain how access was granted or retained.

Technical breakdown

Why AI infrastructure expands the privileged access surface

AI infrastructure concentrates risk because the same environment that trains or serves models also handles datasets, orchestration, API calls, and administrative operations. That creates more privileged entry points than a traditional application stack, especially when engineers, DevOps, and platform teams share elevated access across cloud and on-premise components. Once privileged accounts can reach compute, storage, and control planes, one compromise can expose the full workflow. The security issue is not AI compute itself. It is the privilege density created around the compute layer.

Practical implication: map every privileged path into AI infrastructure before scaling workloads, not after the environment is already in production.

Secrets management in AI pipelines and configuration files

AI pipelines often depend on tokens, API keys, certificates, and service credentials to move data between training systems, model services, and external tools. When those secrets are hardcoded in scripts or left in configuration files, the access model becomes durable even when the workload is ephemeral. That mismatch is why secrets management remains central to AI security. The problem is not only leakage. It is persistence, because a secret in a pipeline can be reused quietly long after the engineer who created it has moved on.

Practical implication: treat pipeline secrets as governed identities with lifecycle controls, rotation, and scope limits rather than as one-off configuration values.

Why modern PAM matters in dynamic cloud environments

Modern PAM is the control layer that reduces standing privilege in environments where access needs to be temporary, visible, and justifiable. JIT access, session monitoring, and continuous validation help replace broad permanent rights with task-scoped privilege, which is especially important when AI infrastructure spans multiple clouds and administrative domains. In this context, PAM is not only about protecting admin accounts. It is about making every privileged action attributable and time-bounded.

Practical implication: replace persistent administrative access with task-scoped access workflows and audit trails that cover both human operators and machine-initiated operations.

Threat narrative

Attacker objective: The attacker aims to gain durable privileged control over AI infrastructure so they can steal data, manipulate outputs, or broaden access across connected systems.

1. entry: Attackers enter AI infrastructure through exposed privileged credentials, hardcoded secrets, or overly broad administrative access into APIs, pipelines, or cloud control planes.
2. escalation: They reuse standing privilege to move from one AI workload or management plane into broader compute, storage, or dataset access.
3. impact: Once privileged access is established, they can exfiltrate sensitive data, disrupt model outputs, or compromise the integrity of training and deployment workflows.

Breaches seen in the wild

• BeyondTrust API key breach — compromised BeyondTrust API key led to unauthorized SaaS access.
• Cisco DevHub NHI breach — IntelBroker exploited exposed Cisco credentials, API tokens and keys in DevHub.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

AI infrastructure growth is turning privileged access into the primary control boundary. The article is right to move beyond perimeter thinking because AI environments concentrate compute, data, orchestration, and administration in the same trust zone. That makes privileged access the highest-value control point, not a secondary administrative concern. For practitioners, the question is no longer whether AI workloads need protection, but which privileged paths must be removed, narrowed, or continuously verified.

Secrets exposure in AI pipelines is really lifecycle failure in disguise. Hardcoded secrets, long-lived API keys, and configuration-file credentials all show that the identity is often treated as a static object rather than a governed asset. That creates the familiar NHI pattern of access outliving intent, especially in fast-moving development and deployment systems. Practitioners should read this as a lifecycle and ownership problem, not just a vaulting problem.

Standing privilege is the named concept this article exposes. AI environments become fragile when elevated rights remain available across the full operating window of a pipeline, cluster, or admin workflow. The article demonstrates that once standing privilege exists, session monitoring and perimeter controls cannot recover lost context after the fact. For security teams, the practical conclusion is that privilege duration is now as important as privilege scope.

Identity governance has to span human operators and machine identities together. Engineers, DevOps staff, service accounts, and API-driven automation all participate in AI infrastructure control, so separate governance models create blind spots. That is where the least-privilege principle gets diluted: one team owns the console, another owns the pipeline, and neither sees the full access chain. Practitioners need governance that follows the control path end to end.

AI infrastructure spending will force security architecture decisions before governance maturity catches up. When investment accelerates faster than access policy design, teams tend to inherit sprawling permissions and weak accountability by default. That is why modern PAM and lifecycle governance need to be treated as enabling infrastructure, not as post-deployment cleanup. For practitioners, the decisive move is to set access policy before platform expansion becomes irreversible.

From our research:

• Systems with least-privileged AI access had a 17% incident rate vs 76% for over-privileged systems, according to the 2026 Infrastructure Identity Survey.
• From our research: Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
• From our research: Explore OWASP NHI Top 10 for the control patterns that help teams govern privileged access, secret exposure, and agentic execution together.

What this signals

Standing privilege is becoming the default failure mode in AI programmes. With 70% of organisations already granting AI systems more access than they would give a human employee doing the same job, per the 2026 Infrastructure Identity Survey, the access model is drifting faster than governance can absorb it. Teams should expect more pressure to formalise JIT access, session logging, and credential scoping before AI expansion becomes operational debt.

AI infrastructure programmes need a privilege map, not just a platform roadmap. Once GPUs, data pipelines, and cloud APIs are all in play, identity decisions shape exposure as much as the architecture itself. That is why a control framework such as NIST SP 800-63 Digital Identity Guidelines remains relevant where human operators, admin authentication, and privileged approvals intersect.

The named concept here is privilege density: the amount of elevated access concentrated around a single AI workflow. As privilege density rises, the likelihood that one compromise can affect multiple control planes rises with it. Security leaders should monitor this density as a programme metric, because it tells you whether the environment is becoming harder to govern before incidents expose it.

For practitioners

• Inventory privileged paths across AI infrastructure Catalogue every administrative route into model training, orchestration, storage, and API control planes. Include human admins, service accounts, automation tokens, and vendor-managed access so you can see where privilege is standing rather than task-scoped.
• Remove hardcoded secrets from pipelines and config files Scan source repositories, deployment scripts, and environment configuration for embedded credentials, then rotate and replace them with governed secrets. Prioritise the paths that connect training systems to external APIs and cloud services.
• Enforce just-in-time privilege for operational access Convert persistent admin access into time-bounded approvals with session recording for the people and systems that manage AI workloads. Use task scope, not job title, as the basis for elevation.
• Align machine identity lifecycle with AI deployment lifecycles Tie credential issuance, rotation, revocation, and offboarding to model and pipeline changes. If a workload, environment, or vendor relationship changes, its access should change immediately rather than on a deferred review cycle.

Key takeaways

• AI infrastructure growth is turning privileged access into the main security choke point, because compute scale brings more admin paths, more secrets, and more standing rights.
• The evidence points to a governance gap, not just a tooling gap, with over-privileged AI access correlating strongly with incidents and with many teams still lacking formal policy.
• Practical response starts with task-scoped privilege, secrets lifecycle control, and a governance model that covers human administrators and machine identities together.

Key terms

• Privileged Access Management: Privileged Access Management is the set of controls used to govern elevated access to sensitive systems, data, and control planes. In AI infrastructure, it matters because administrators, service accounts, and automation often need powerful rights that must be temporary, visible, and tightly scoped.
• Standing Privilege: Standing privilege is persistent elevated access that remains available outside the moment it is needed. In AI and cloud environments, it creates unnecessary exposure because the credential or account can be reused across sessions, pipelines, or control planes long after the original task is complete.
• Secrets Management: Secrets management is the practice of storing, rotating, and governing credentials such as API keys, tokens, certificates, and passwords. For AI infrastructure, the key issue is that secrets often move through code, pipelines, and orchestration layers, so lifecycle control matters as much as storage.
• Just-in-Time Access: Just-in-time access is a privilege model that grants elevated rights only when a task requires them and removes them afterward. For AI infrastructure, JIT access helps reduce blast radius because administrative rights do not remain available throughout the full operating window of the environment.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.

This post draws on content published by Keeper Security: Why AI Infrastructure Growth Demands Next-Gen Cybersecurity and PAM. Read the original.