Kubernetes AI Security — Contain Agents, Secure MCP, and Isolate AI Workloads

Prompt injection and tool-abuse containment, agent privilege escalation and overprivileged service accounts, code-execution and container-breakout risk, secret exfiltration and network egress control, cross-tenant leakage, model and dependency supply-chain risk, and MCP server authentication/authorization. We design the RBAC, policy, isolation, and auditing that keep an unintended model action from becoming a breach.

No — and we are clear about that. Prompt injection is an application-layer problem. What Kubernetes and the platform can do is limit what a compromised or manipulated agent can reach: least-privilege RBAC and workload identity, egress restrictions, sandboxed tool execution, and policy enforcement. Defence spans the model/application layer and the infrastructure layer together.

For untrusted or agent-generated code we help you evaluate and deploy gVisor and Kata Containers for stronger isolation than default runc, plus Seccomp/AppArmor profiles, rootless and non-root containers, read-only root filesystems, dropped capabilities, and network policies. We match the isolation strength to the threat model so you are neither exposed nor paying for isolation you do not need.

We help you put MCP servers behind proper authentication and authorization (OAuth/OIDC where appropriate), scope what each agent identity can call, apply rate limits and network policy, and audit every tool invocation. Agent identity is treated as a first-class workload identity with least privilege, not a shared high-privilege credential.

Message us on WhatsApp with your setup — agent framework, MCP servers, cluster, and cloud — and your concern, whether it is an upcoming security review, a pen-test finding, or an incident. We will assess the blast radius and help you close the gaps, same-day.