Kubernetes has become the default platform for running modern applications, and AI agents are increasingly part of that mix. Organizations are deploying autonomous workloads directly inside clusters to keep inference close to data, reduce latency, and retain greater control over sensitive operations. This approach delivers performance and flexibility, but it also introduces new security challenges that traditional Kubernetes controls were never designed to address.
As AI agents scale across pods and services, the question is no longer whether Kubernetes can run them. The question is whether access to those agents can be governed with the same rigor as the rest of the enterprise.
Why AI Agents Are Moving into Kubernetes
Running AI agents locally inside Kubernetes offers clear advantages. Teams can deploy lightweight models as containerized services, integrate inference directly into application workflows, and scale dynamically based on demand. Sensitive data stays inside the cluster rather than flowing to external APIs, improving both performance and privacy.
Common use cases include natural language processing, computer vision, recommendation engines, and predictive analytics. These workloads often interact with internal services, APIs, and data stores, making Kubernetes an attractive execution environment.
But this proximity also increases risk. AI agents often expose service endpoints. Pods communicate freely by default. Identities are ephemeral. Without additional controls, a compromised workload can move laterally across the cluster or access resources far beyond its intended scope.
The Limits of Native Kubernetes Security
Kubernetes provides powerful orchestration, but its security model was not built to enforce Zero Trust principles on autonomous workloads. Network policies help restrict traffic, but they rely on IPs and labels that change constantly. Service accounts authenticate workloads, but they do not govern network-level access or continuously evaluate risk.
For AI agents, this creates several gaps:
- Limited isolation between workloads once inside the cluster
- Broad trust relationships between services
- Minimal protection for exposed APIs and internal dashboards
- Difficulty enforcing least-privilege access at runtime
As clusters grow and workloads become more autonomous, these gaps become harder to manage manually.
Extending Zero Trust into Kubernetes
This is where Agentic AI Core Protection, a new capability within AppGate ZTNA, extends Zero Trust directly into Kubernetes environments.
Rather than treating the cluster as a trusted zone, AppGate ZTNA enforces identity-based access at the workload level. AI agents and the services that interact with them are authenticated and authorized before any connection is established, and access is continuously evaluated against policy.
Key capabilities include:
Kubernetes-Native Enforcement: AppGate integrates with Kubernetes using sidecar and node-level controls that apply Zero Trust protections without disrupting orchestration. Enforcement scales automatically as pods are created, updated, or terminated.
Identity-Based Access for Workloads: Policies are tied to workload identity rather than network location. Each AI agent is permitted to communicate only with the services and resources explicitly defined by policy.
Cloaked Services and APIs: Internal services remain invisible until authenticated and authorized, reducing exposure to reconnaissance and unauthorized access attempts.
Micro-Perimeters at Pod Scale: Each workload operates within a tightly defined access boundary, preventing lateral movement even inside the cluster.
In this model, Kubernetes remains responsible for orchestration and scaling, while AppGate ZTNA governs access and trust.
What Agentic AI Core Protection Enables at Scale
For platform and security teams, this approach brings structure to environments that are otherwise difficult to control. For AI and application teams, it removes friction by replacing static rules and manual segmentation with identity-driven enforcement that adapts as workloads change.
Organizations gain:
- Stronger isolation for AI agents running inside clusters
- Reduced attack surface for internal APIs and services
- Clear visibility into workload access patterns
- Consistent Zero Trust controls across servers, VMs, and Kubernetes
Most importantly, security becomes part of the platform rather than an afterthought.
Building Secure AI Platforms on Kubernetes
Kubernetes makes it easy to deploy AI agents at scale. Securing those agents requires a different approach, one that treats every workload as untrusted until proven otherwise.
By extending Zero Trust into Kubernetes environments, AppGate ZTNA enables organizations to run autonomous AI workloads with confidence, control, and clarity, even as clusters grow more dynamic and complex.
Learn how AppGate ZTNA secures local AI agents in Kubernetes without disrupting orchestration or scale. Read the solution brief.