As AI workloads grow larger and more distributed, the need for scaling and bursting AI inference securely across clouds and clusters becomes critical. Yet scaling without strong security can open the door to vulnerabilities—especially now that inference happens across more dynamic, multi-cluster environments.

In early 2025, the OWASP Foundation released the Top 10 Security Risks for Large Language Model (LLM) Applications, highlighting new, urgent threats during AI inference and deployment. These include prompt injections, model denial-of-service attacks, training data poisoning, and insecure output handling—threats that can compromise data privacy, model integrity, and enterprise compliance. 

At Avesha, we believe that scaling AI must come with security baked in from the ground up. Here's how KubeSlice helps enterprises burst and scale AI inference safely and efficiently.

Why Scaling Inference Without Security is Dangerous

When inference workloads burst across multiple clouds or clusters, security gaps appear in critical places during the inference phase itself — not just at input or data ingestion. 

Here’s what happens if security is missing during scaling: 

 🔒 Inference must scale. Risks must not.

How Avesha Secures Scaling AI Inference with KubeSlice

KubeSlice, part of Avesha’s AI infrastructure automation platform, delivers a defense-in-depth architecture to protect AI inference pipelines across clouds, clusters, and edge locations.

1. Zero Trust Slicing and RBAC Enforcement

 KubeSlice logically segments Kubernetes workloads into isolated slices, applying Zero Trust principles:

• Fine-grained Role-Based Access Control (RBAC) scoped to each slice.
• Isolation of namespaces, preventing unauthorized cross-slice access.
• Application teams operate only within their authorized slice.

 Impact: If an inference node is compromised, the attacker cannot laterally move to other AI models, pipelines, or datasets.

2. Secure Networking Across Clusters (mTLS + VPN)

Scaling inference across distributed clusters introduces security risks. KubeSlice counters them with: 

• Mutual TLS (mTLS) encryption for pod-to-pod communication.
• FIPS 140-2 compliant VPN tunnels between clusters.
• Automated certificate management to ensure continuous cryptographic integrity. 

Impact: Inference traffic between clusters is encrypted, authenticated, and protected against man-in-the-middle (MITM) attacks.

3. Blast Radius Reduction and Guardrails

 KubeSlice limits exposure by sharding Kubernetes environments:

• Micro-segmentation prevents cross-slice compromise.
• Enforced security policies per slice minimize attack surface.
• If an inference workload is compromised, blast radius is strictly limited.

Impact: AI inference pipelines stay resilient even under targeted attacks.

4. Runtime Protection with Kata Containers

While KubeSlice secures the infrastructure, Kata Containers secure the runtime:

• Containers are wrapped in lightweight VMs, preventing kernel sharing.
• Each VM has isolated network interfaces, memory, and file systems.
• Prevents container breakout and kernel exploitation attacks.

Impact: Even if a containerized inference job is compromised, attackers can't jump to the underlying node or other workloads.

Defense-in-Depth for Secure AI Scaling

By combining KubeSlice and Kata Containers, enterprises can:

✅ Enforce strong network segmentation across AI inference workloads.         
✅ Protect against lateral movement and privilege escalation.         
✅ Secure inference traffic across hybrid and multi-cloud environments.         
✅ Maintain compliance and cryptographic integrity automatically. 

Conclusion: Scaling Inference Needs Built-In Security

As AI inference scales across hybrid, edge, and cloud environments, so do the risks.

Following the lessons from OWASP's 2025 Top 10 LLM Risks, enterprises must move beyond traditional cluster security—and adopt slice-based segmentation, encrypted communications, and runtime isolation as default practices.

At Avesha, we’re innovating the future of AI infrastructure—automating scaling while securing every layer of your inference pipeline. 

🔒 Learn more about how Avesha KubeSlice can protect your AI workloads → www.avesha.io/products/kubeslice 

📅 Schedule a Free Security Assessment →