Agentic AI security for Kubernetes

Review the security chain behind Kubernetes AI agents.

SecureMinder looks at what your agent can reach through tools, MCP, ServiceAccounts, RBAC, egress, secrets, and cloud workload identity.

Request Exposure Review See the review
prompt injection MCP RBAC egress cloud IAM
Scope AI agents running on Kubernetes
Delivery Fixed review in 5 to 7 business days
Output Risk matrix, evidence, fixes, and executive summary

Threat path

Follow the path from prompt to permissions.

We map each step and show which control should stop the path.

01 Prompt injection
02 Tool or MCP abuse
03 Pod identity
04 RBAC and egress
05 Cloud data exposure

What we test

The parts that set the blast radius.

We check the controls that decide how far an agent action can go.

01

Tool authorization

Which actions can the agent trigger, and who approved them?

02

MCP boundaries

Server trust, token handling, confused deputy, and tool metadata risk.

03

ServiceAccounts

Mounted tokens, pod defaults, namespace assumptions, and token exposure.

04

Kubernetes RBAC

Can the agent pod read secrets, list resources, or cross namespace boundaries?

05

Egress paths

Internet access, internal services, metadata endpoints, and data exits.

06

Cloud identity

Workload identity scope across storage, queues, databases, and APIs.

Core offer

A focused review in one week.

For teams that want a clear read on an AI agent running in Kubernetes.

Delivered in 5 to 7 business days

fixed scope

We review your agent architecture, tool list, MCP servers, Kubernetes manifests, RBAC, NetworkPolicy, secrets, and workload identity setup.

  • Executive summary
  • Prompt-to-infra threat model
  • Risk register
  • Important misconfigurations
  • Remediation checklist
  • Safer reference architecture
Request review Partner delivery inquiry

Research

Research-backed, practical by default.

AgentGoat

Open lab

A safe Kubernetes lab for prompt injection, tool abuse, token exposure, and cloud pivot simulations.

Field notes

Technical writing

Short research notes on AI agents, MCP, Kubernetes identity, RBAC, egress, and cloud IAM.

Reports

Decision-ready output

Every finding is written with evidence, impact, fix, owner, and detection guidance.

For

Teams shipping AI agents on cloud-native infrastructure

  • B2B SaaS with internal copilots or workflow agents
  • Platform teams running LangGraph, LlamaIndex, CrewAI, AutoGen, or MCP
  • Security teams extending LLM red teaming into infrastructure review
Boundaries

Clear scope from the start

  • Fixed assessment scope
  • Authorized testing only
  • Research and reporting first

Next step

Get a clear read on your agent's exposure.

If your AI agents run in Kubernetes and call internal tools, SecureMinder can review the chain from prompt injection to cloud workload identity.

Request Exposure Review