AI data centers and GPU clouds
The infrastructure around your models, held to the same standard.
Training and inference run on the same building blocks as everything else: GPU nodes, Kubernetes, object storage, networks and identities. They are often stood up fast, by teams outside your usual review. ACSE checks that configuration and governs the fix, in AI data centers, GPU clouds and the public clouds alike.
Training data that stays private
Buckets holding datasets and model weights are checked for public access, encryption, versioning and retention, like any other sensitive store.
GPU capacity without blind spots
GPU node pools and the clusters they belong to are checked for exposed endpoints, over-privileged workloads and missing limits.
Fast teams, same review
A fix for an ML platform goes through the same pull request, approval and evidence as the rest of your estate.
What ACSE checks
Four places AI estates go wrong
This is configuration security for the infrastructure that runs AI. ACSE does not test models, prompts or their outputs.
Model and training-data storage
Public access, encryption at rest, TLS-only access, versioning and retention on the buckets and volumes that hold datasets, checkpoints and weights.
GPU node pools and clusters
Public cluster endpoints, audit logging, secrets encryption, and workloads that run privileged or as root to reach a GPU.
Network exposure
Inference and notebook endpoints reachable from the internet, firewall rules open to administration ports, and missing flow logs.
Identities
Service accounts and roles on GPU nodes with administrator rights, user-managed keys, and broad access to training data.
Risk paths
From an open endpoint to your training data
An inference endpoint open to the internet is a finding. The same endpoint on a node whose identity can read your training-data bucket is a path, and ACSE ranks it above findings that lead nowhere.
Close one link, such as the endpoint’s exposure or the identity’s access, and the path closes with it.
- Paths scored from internet entry to data
- The weakness at each step named
- Shapes and counts only leave your environment
Exceptions
When a workload needs more than usual
GPU drivers and some training jobs need privileges ordinary workloads do not. ACSE does not hide those findings or fail them forever.
Your team requests an exception with an owner and an expiry. A security approver decides. The score shows the governed figure with the raw one beside it, so the accepted risk is always visible.
Where it applies
One connection model, every GPU estate
- Public clouds
- GPU instances, node pools and storage on AWS, Google Cloud and Azure
- GPU clouds and AI data centers
- Kubernetes clusters and the storage and networks around them
- Access
- Read-only, keyless, set up by your own administrators
- Fixes
- Terraform and Kubernetes manifests, as draft pull requests
Questions
What people ask us
Does ACSE secure my models or prompts?
No. ACSE checks the configuration of the infrastructure your models run on: storage, clusters, networks and identities. It does not test models, prompts or outputs.
Is this a separate product?
No. AI infrastructure uses the same checks, frameworks, fix queue and evidence as the rest of ACSE. It adds the context that tells you which resources hold training data or serve inference.
Does ACSE read my datasets or weights?
No. Checks read configuration only, never object contents. Detailed findings stay in your environment.
Keep reading
Related
Kubernetes
ACSE checks Kubernetes workloads and managed clusters for risky settings and fixes them in your YAML, Helm values or Kustomize overlays, dry-run before review.
ReadPlatformRisk graph
ACSE links resources into a risk graph and traces the paths from the internet to your data, so your team fixes the misconfiguration that breaks the chain.
ReadTrustData boundary
ACSE runs in your account and sends only shapes and counts. Names, ids and credentials stay with you, and a deny-by-default registry governs what leaves.
ReadTalk to us
See ACSE on your cloud.
Tell us what you run and what your auditors ask for. We’ll show you the loop end to end: a real finding, a fix as code, an approval with MFA and the sealed record.