Governance & Security for AI Development Teams: Cluster Access & Secret Management in Kubernetes

How companies can make their GPU-Kubernetes environments secure, compliant, and efficient for AI development using tools like Kyverno, Vault, and Infisical.

Introduction

AI workloads are not only computationally intensive but also sensitive in terms of security and compliance. While GPUs, MIG, and time-slicing ensure resources are used efficiently, new questions arise at the governance level:

• Who is allowed to deploy where?
• How are access rights for clusters and GPU resources assigned?
• How are secrets (API keys, tokens, database passwords, model registry credentials) managed?
• How is it prevented that sensitive data is used uncontrollably in dev or staging environments?

In our last post, we showed how MIG and time-slicing make GPU resources available for AI teams in Kubernetes. Now it’s about the governance layer above: Cluster Access and Secret Management – the two central levers for security and compliance.

Why Governance and Compliance are Crucial

1. Legal Framework & Regulation: GDPR, HIPAA, PCI DSS, or industry-specific standards enforce controlled access and traceability.
2. Multi-Tenancy: In Kubernetes, multiple teams often share the same resources. Without clear rules, chaos quickly escalates.
3. AI-Specific Risks: Training data, model weights, or API keys for external services are among a company’s most sensitive assets.
4. Productivity vs. Control: Developers want to work quickly – security and compliance demand brakes. Goal: Balance instead of blockade.

Cluster Access Management: Who Can Do What in GPU-Kubernetes?

Basic Principles

• Least Privilege: Every user receives only the rights they truly need for their work.
• Auditability: Every access must be traceable and logged.
• Automation: Manual user management is error-prone. GitOps, OIDC, and Policy-as-Code are the way forward.

Kubernetes Mechanisms

• RBAC (Role-Based Access Control): Define roles that specify which resources users or service accounts can use.
• Namespaces: Logically separate teams, projects, or stages from each other.
• NetworkPolicies: Restrict network access between Pods and Namespaces.
• OPA/Gatekeeper or Kyverno: Enforce that deployments adhere to certain rules.

Kyverno as a Policy Engine

Kyverno is a policy engine specifically designed for Kubernetes. Unlike OPA/Gatekeeper, policies are written in YAML rather than Rego, significantly lowering the entry barrier.

Examples for AI clusters:

• GPU Usage Only with Limits: No Pod may start without defined GPU limits (nvidia.com/mig-* or nvidia.com/gpu).
• Enforce NodeSelector: Pods with GPU workloads must run only on designated GPU nodes (gpu-type=h100-mig).
• Namespace Isolation: Prevent a team from uncontrollably creating resources in foreign namespaces.
• Regulate Secrets Usage: Policies can ensure that only Vault/Infisical are used as secret sources.

Practical Example: Enforcing GPU Limits with Kyverno

apiVersion: kyverno.io/v1
kind: ClusterPolicy
metadata:
  name: require-gpu-limits
spec:
  rules:
  - name: validate-gpu-limits
    match:
      resources:
        kinds:
        - Pod
    validate:
      message: "Pods must specify GPU resource limits"
      pattern:
        spec:
          containers:
          - resources:
              limits:
                nvidia.com/*:
                  any: "?*"

This prevents Pods without GPU limits from entering the cluster, protecting against resource wastage and “noisy neighbors.”

Secret Management: The Underestimated Risk Factor

Secrets are the lifeblood of any AI workflow: API keys for OpenAI, Hugging Face, or AWS, passwords for databases, tokens for model registries. Too often, they lie in plain text in ConfigMaps, Git repos, or ENV files. This is a compliance nightmare.

Requirements for Secret Management

• Encryption at Rest and in Transit
• Audit Logs for every secret query
• Rotation and Expiration of secrets
• Self-Service APIs for developers
• Integration with CI/CD and Kubernetes

Tools Comparison

HashiCorp Vault

• Enterprise Standard for secret management
• Dynamic secrets (e.g., DB credentials with TTL)
• Strong policy engine
• More complex to set up and manage

Infisical

• Modern, cloud-native secret management
• Focus on developer experience
• GitOps-friendly: Secrets can be versioned as encrypted objects
• Offers many integrations (Kubernetes, CI/CD, Serverless)

Both tools are excellent – Vault is more “enterprise-grade,” while Infisical impresses with developer focus and quick implementation.

Integration into GPU-Kubernetes

The connection between secret management and GPU workloads is clear:

• Training Jobs need access to databases or object stores.
• Inference Pods require API keys for external services.
• MLOps Pipelines access model registries.

With Vault or Infisical, these secrets are not stored in the Pod manifest but dynamically injected.

Example: Vault-Agent Injector

A Pod receives secrets as a sidecar:

apiVersion: v1
kind: Pod
metadata:
  name: inference-pod
  annotations:
    vault.hashicorp.com/agent-inject: "true"
    vault.hashicorp.com/role: "ml-inference"
    vault.hashicorp.com/agent-inject-secret-api: "secret/data/ml/api-key"
spec:
  containers:
  - name: model-server
    image: nvcr.io/nvidia/cuda:12.0-base
    resources:
      limits:
        nvidia.com/mig-3g.40gb: 1

This way, the Pod dynamically receives its API key without it being in the YAML manifest.

Example: Infisical Secret Sync

Infisical can automatically synchronize secrets into Kubernetes secrets:

apiVersion: v1
kind: Secret
metadata:
  name: model-registry
  annotations:
    infisical.com/secret-sync: "true"
type: Opaque

data:
  token: <auto-managed>

Developers no longer need to make manual updates – rotation runs automatically.

Interaction with GPU Slicing & Governance

The mechanisms described in the last blog (MIG, time-slicing, node pools) perfectly complement access and secret management:

• Kyverno ensures that Pods only make valid GPU requests.
• Vault/Infisical ensures that only authorized workloads access sensitive data.
• RBAC & Namespaces prevent a team from claiming foreign GPU resources.

Example scenario:

• Team A gets access to MIG slices (nvidia.com/mig-3g.40gb) in the team-a namespace.
• Through Vault, it receives time-limited credentials for the model registry.
• Kyverno enforces that only Pods with GPU limits are deployed.
• Auditing shows at any time who used which resources when.

Compliance Aspects

Especially for regulated industries (finance, healthcare, automotive), these concepts are crucial:

• Audit-Proof Logging of all accesses (Vault audit logs, Kubernetes API server audit).
• Data Minimization: Secrets are only provided temporarily and encrypted.
• Role-Based Isolation: Teams are clearly separated, even in shared GPU clusters.
• Repeatability: Policies and secrets are declaratively versioned and verifiable.

This way, companies meet regulatory requirements without blocking innovation and agility.

Developer Experience: Security Without Friction

A governance model is only successful if developers can work productively with it. Vault and Infisical score here:

• CLI & SDKs for quick testing.
• Automated Secret Injection in Pods or CI/CD pipelines.
• Self-Service Mechanisms: Developers request access rights or secrets without having to write tickets.
• Seamless Integration with GPU Scheduling: A Pod requests GPU resources and secrets in the same manifest file.

The result: Security is no longer a hindrance but an integral part of the developer experience.

Conclusion

Kubernetes enables flexible and efficient use of GPUs – thanks to MIG and time-slicing, even across teams. But without governance and secret management, risks arise that can be costly for companies.

With tools like Kyverno (policy enforcement) and Vault/Infisical (secret management), these risks can be managed – in a way that does not slow down developers. Decision-makers gain a clear picture: the central levers for security, compliance, and efficiency in AI development teams are access and secret management.

Those who consistently implement these components create an environment where AI workloads can be developed, tested, and operated securely, compliantly, and highly productively.

Next Steps for Companies

1. Develop and document an RBAC & Namespace Strategy.
2. Introduce Kyverno Policies that enforce GPU usage and cluster policies.
3. Roll out Vault or Infisical for secret management.
4. Establish GitOps Integration for policies and secrets.
5. Define an Audit & Compliance Framework (dashboards, reports, alerts).

This creates a modern, cloud-native security model for AI teams that is flexible, scalable, and regulatory robust.

Further Reading

• GPUs in Kubernetes: Practical Guide
• Zero Trust Network Access (ZTNA) with NetBird
• Developer Platforms by ayedo

What Can We Do for You?

ayedo’s role as a Managed Service Provider is to pave the way for companies to a secure, efficient, and compliant AI-Kubernetes cluster operation. We take on architecture consulting, consistently implement GPU slicing (MIG/TS), and seamlessly integrate tools like Kyverno and Vault/Infisical into existing DevSecOps processes. Through GitOps-based operating models, continuous monitoring, and automated policy enforcement mechanisms, we ensure that development and AI teams can work productively without jeopardizing governance or compliance. The result: a scalable, audit-proof, and cost-efficient platform that accelerates innovation and meets regulatory requirements.