Security articles

Design multi-tenant AI agent infrastructure on Kubernetes

Separate identity, network access, data, queues, budgets, and evidence when multiple teams or customers run AI agents on shared Kubernetes infrastructure.

Sandbox AI agents on Kubernetes

Design isolated Kubernetes execution for AI agents with a threat model, stronger runtimes, scoped identity, controlled egress, bounded storage, and auditable cleanup.

What is sovereign AI? Control across the AI lifecycle

Define sovereign AI as control over data, models, compute, operations, providers, and evidence, then implement it with Kubernetes.

Build an enterprise AI security operating model

Define ownership, risk tiers, platform boundaries, exceptions, and evidence so enterprise AI security operates continuously instead of as a launch checklist.

Scan model artifacts before adding them to a registry

Add static model artifact scanning before registry promotion, preserve scan evidence, check coverage, and bind approval to immutable artifact digests.

Design production security for AI agents

Contain AI agent identity, tools, memory, network access, and delegated actions with enforceable production boundaries.

Secure enterprise AI from data to deployment

Apply security controls across data, training, evaluation, artifacts, deployment, and operation without slowing every AI workload equally.

Move faster with risk-tiered AI delivery

Use consequence-based AI risk tiers to apply proportionate data, evaluation, security, approval, deployment, monitoring, and incident controls.

Secure AIOps automation for ML platforms

Use AIOps to investigate ML incidents and automate low-risk remediation without giving an AI system unrestricted production access.

Build guardrails for AI-generated code

Control what coding agents can read, change, execute, merge, and deploy with layered checks from context selection through production.

Contain generated code with Kubernetes runtime controls

Reduce the impact of generated-code failures with Polyaxon workload configuration, Kubernetes admission and network controls, minimal credentials, and incident evidence.

How to red team AI agents

Test AI agent permissions, memory, handoffs, retries, and tool actions with a practical security matrix and reproducible evaluation workflow.

Review the trust boundary for LLM-generated code

Review the trust boundary for LLM-generated code before execution, mapping permissions, trusted controllers, output handling, and promotion controls in Polyaxon.

How to test prompt injection in LLM applications

Build prompt injection tests for user input, retrieved documents, and tool responses, with checks for data access and actual side effects.

Manage AI coding-agent sandboxes with Polyaxon

Manage Polyaxon sandbox-enabled services from versioned environment templates through readiness checks, process execution, file persistence, and resource cleanup.

Kubernetes RBAC for ML workloads

Design least-privilege Kubernetes access for ML workloads with clear subjects, namespaced roles, dedicated service accounts, permission checks, and reviewable policy.

Route multi-tenant agent workloads with Polyaxon

Route multi-tenant agent workloads through Polyaxon projects, queues, scoped connections, and explicit Kubernetes isolation and capacity policies.

Build a model trust pipeline

Establish model trust through provenance, artifact inspection, reproducible evaluation, approval, runtime verification, monitoring, and rapid revocation.

Configure Python sandboxes for untrusted LLM code

Configure Polyaxon Python sandbox services with minimal privileges, controlled writable paths, bounded resources, and separate handling of untrusted LLM code.

Secure AI-generated code from proposal to execution

Qualify AI-generated code with Polyaxon sandbox controls, tracked execution receipts, reusable evaluation jobs, artifacts, and explicit promotion boundaries.

Design data-residency controls for AI execution

Map AI execution data paths to Polyaxon compute placement, connections, artifacts, external providers, and documented residency controls.

Qualify AI-generated code before pipeline release

Qualify AI-generated code for Polyaxon pipelines using immutable candidate identity, independent evaluation, explicit approval, and versioned release artifacts.

Isolate reinforcement-learning agent rollouts

Use Polyaxon to run reinforcement-learning agent rollouts with bounded execution, separate reward evaluation, reproducible seeds, and isolated trajectory artifacts.

Manage the lifecycle of a Polyaxon sandbox service

Create a Polyaxon sandbox service, wait for readiness, track command execution, preserve outputs, configure timeouts and culling, and stop resources when work finishes.