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AI and GPU cloud providers

GPU estates that grow fast, held to a steady standard.

AI infrastructure grows faster than the controls around it. New clusters, model storage and tenant environments appear every week, and your enterprise customers want proof they are configured safely. ACSE checks that infrastructure, fixes misconfigurations through your own code and keeps the evidence your customers’ security reviews ask for.

Talk to usSee how it works
AI infrastructure, held to the same barRows of GPU nodes with their control plane. One finding is flagged: model storage readable by every node.CONTROL PLANEmodel storage readable by every nodeGPU NODEStraining data · model weights · network
01

Model weights stay private

Storage readable by every node, or by the internet, is found and closed with a reviewed fix.

02

Speed without shortcuts

Fixes arrive as code in your repositories, so your platform team keeps moving without manual cleanup.

03

Answers for customer reviews

SOC 2 and ISO 27001 posture with sealed evidence, ready for the questionnaires your enterprise customers send.

What ACSE checks

Where AI infrastructure drifts

ACSE checks configuration. It does not scan workloads, images or model files.

01

Model and dataset storage

Buckets and volumes shared more widely than intended, or left without encryption.

02

Cluster workloads

Privileged containers and permissive pod settings in the manifests that run training and inference.

03

Network exposure

Control planes and endpoints reachable from the internet without need.

04

Identity

Roles and service accounts that reach far more than the job requires.

Kubernetes

Fixed in the manifest, not the cluster

GPU workloads usually run on Kubernetes. ACSE patches the manifest, Helm values or Kustomize overlay that defines the workload, dry-runs it offline against the API schemas, and opens it for review.

The cluster changes only when your pipeline deploys the merged change.

Kubernetes, fixed in the manifestA cluster of three nodes with their pods. One pod runs a privileged container; the fix is a patch to its manifest.privileged containerMANIFEST PATCHsecurityContext:- privileged: true+ privileged: falseCLUSTER · NODES · PODS

Clouds and data centers

Hyperscaler, GPU cloud or your own racks

Many AI providers run across a hyperscaler, a specialist GPU cloud and their own data centers. ACSE applies one set of controls and one approval flow across them, and keeps one record.

The runtime runs in your environment and connects outbound only, so it fits where your capacity lives.

Every estate you run, one governed loopAn isometric landscape of AWS, Google Cloud, Azure, Kubernetes and an AI data center. A public storage bucket is detected on a path from the internet, a fix arrives as a draft pull request, a person approves it with MFA, and the evidence chain seals in the customer’s account.AI DATA CENTERgpu nodes · model storageAWSGOOGLE CLOUDAZUREKUBERNETES · GKE · AKS · EKSpublic access · criticalinternetthree steps to your dataDRAFT PR · YOUR REPO+ resource "aws_s3_bucket_public_+ access_block" "data" {+ restrict_public_buckets = truevalidated: plan, policy re-checkDECISIONsecurity approver · approve✓ step-up mfa · bound to viewSEALED · IN YOUR ACCOUNT

ACSE’s own AI

Our AI drafts. Your people decide.

ACSE’s optional AI is off by default. When you turn it on, it can explain a finding or draft a fix that has no rule, and every draft goes through the same validation, guardrails and human approval.

Every step is labelled as rule, person or AI-assisted, with the model recorded.

AI that drafts, people who decideA model sits inside guardrails that apply the same checks as a rule-based fix. Its draft passes to a person, who decides. The AI is off until a customer turns it on.MODELGUARDRAILS · SAME CHECKS AS ANY FIXA PERSON DECIDESdrafts and explains · off until you turn it on

Questions

What people ask us

Does ACSE scan our models or training data?

No. ACSE checks the configuration of the infrastructure around them, such as who can read the storage. It does not read model files or datasets.

Can ACSE help with our own SOC 2 or ISO 27001 audit?

Yes. Both ship as signed framework packs, and sealed cases feed audit packages organized by control.

Does ACSE’s AI see our data?

Only if you turn it on, and then it runs on the model service in your own account with sanitized metadata. It never decides.

Keep reading

Related

Clouds

AI infrastructure

ACSE checks the cloud configuration around AI: GPU node pools, model and training-data storage, network exposure and the identities that reach them.

ReadClouds

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.

ReadTrust

Responsible AI

ACSE’s optional AI runs on Amazon Bedrock in your own account, stays off until two admins turn a step on, and never decides. Every AI step names its model.

Read

Talk 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.

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