Topic starter
22/01/2026 5:14 pm
TL;DR: Global AI infrastructure spending is set to exceed $200 billion by 2028, while AI-specific data centres could drive almost half of U.S. electricity demand growth through 2030, and expanding hybrid AI stacks are widening privileged access gaps, according to Keeper Security. Identity-centred PAM, not perimeter security, becomes the control plane for AI infrastructure.
Editorial analysis by NHI Mgmt Group, based on content published by Keeper Security: “Why AI Infrastructure Growth Demands Next-Gen Cybersecurity and PAM”.
By the numbers:
- Global AI infrastructure spending is projected to surpass $200 billion by 2028.
- U.S. AI-specific data centres could account for almost half of the country's electricity demand growth through 2030.
Key questions
Q: Why do AI infrastructure environments need PAM instead of perimeter security alone?
A: AI infrastructure spreads privileged activity across cloud services, APIs, GPUs, and administrative tooling, so the network edge is no longer the best control point.
Q: What breaks when API keys and privileged credentials are left unmanaged in AI-driven environments?
A: Unmanaged API keys and privileged credentials create hidden access paths that are hard to inventory, revoke, or audit.
Q: How should organisations handle identity and secrets risk in AI-assisted pipelines?
A: Treat AI-generated code as a higher-volume source of identity and secrets exposure.
Practitioner guidance
- Map privileged AI access paths Inventory every administrative path into AI infrastructure, including engineers, DevOps accounts, service accounts, API keys, and cloud console roles.
- Replace standing access with JIT control Use just-in-time access for setup, model maintenance, and troubleshooting so privileged access expires when the task ends.
- Harden secrets in AI pipelines Find credentials embedded in scripts, notebooks, and configuration files, then move them into governed secrets management with rotation and ownership assigned.
Bottom line: AI infrastructure growth is turning privileged access into the primary governance issue because compute, APIs, and cloud services now share the same control plane.
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This topic was modified 2 days ago by NHI Mgmt Group
11/10/2026 2:02 am
AI infrastructure privilege sprawl is becoming the new control-plane problem. The security issue is no longer just capacity growth, but the accumulation of privileged paths across GPUs, APIs, cloud services, and administrative tooling. Traditional perimeter logic does not describe this environment well because the decisive control point is identity, not network location. Practitioners should treat AI infrastructure as a privilege-governed domain with a larger blast radius than conventional application stacks.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: Should organisations prioritise JIT access or session recording first for AI workloads?
A: JIT access should come first when the main problem is persistent privilege, because it removes unnecessary standing access before a session begins. Session recording adds a second layer of accountability, but it does not reduce exposure if access remains permanently available.
👉 Read our full editorial: AI infrastructure growth is exposing privileged access gaps