Deploy reliable AI workload security in Amazon EKS

For a majority of organizations, Kubernetes is the platform of choice for deploying and managing containerized workloads. But AI workloads introduce new levels of complexity compared to typical microservices, which are more consistent and predictable. If organizations aren’t aware of these challenges, they might be opening themselves up to cost overruns, poor resource utilization, and security vulnerabilities that slow AI down, drain value, and amplify risk. To protect their investments, organizations need a more intelligent approach for how they use Kubernetes for AI.

AI is different from traditional workloads. Prompts can vary from simple text queries to multimedia-based analyses, resulting in variable demands on GPU resources. Container ingress controllers struggle with awareness of GPU availability, so the default round-robin distribution style leaves some GPUs congested and others underutilized.

AI also relies on a complex web of distributed services and APIs that’s harder to manage, with a larger attack surface that’s harder to secure. AI has become an attractive target because of this complexity, and cyber criminals are using the AI models themselves as attack vectors. Techniques such as prompt injection and model manipulation bypass traditional security mechanisms to extract sensitive data from AI, and attackers may flood AI with erroneous prompts to degrade model responsiveness and drain your resources even further. Traditional Kubernetes security isn’t designed to deal with these types of attacks.

To enable truly dynamic, efficient, and secure AI in Kubernetes, you need traffic management that addresses AI-specific needs and allocates workloads accordingly. This includes awareness for request complexity and GPU availability, as well as factoring in the non-linear relationship between resources and AI throughput. Container-native security controls are a must-have for protecting AI models and preventing them from becoming access points for unauthorized use and abusive tactics.

F5 solutions complement your Amazon Elastic Kubernetes Service (EKS) deployments by bridging operational, security, and performance gaps.

F5 NGINX Ingress Controller delivers AI-aware ingress and load balancing, with support for dynamic reconfiguration to maintain uptime through demand spikes and pod failures. Your teams also benefit from tools to support blue-green and canary release strategies and A/B testing for smoother deployments and optimization efforts.

F5 NGINX App Protect delivers a lightweight web application firewall (WAF), layer 7 distributed denial-of-service (DDoS) protection, and API security. This offering is packaged as part of F5 NGINX Plus along with NGINX Ingress Controller and scales seamlessly into your Kubernetes clusters.

F5 AI Gateway is another option to facilitate AI services in Kubernetes across your hybrid multicloud environment. You benefit from AI-aware traffic management capabilities including semantic caching, which reuses prompt responses across similar requests to minimize redundant processing and conserve tokens.

Layered protections defend against unique AI threats, addressing the OWASP Top 10 for LLMs, while preventing sensitive data leaks and hallucinations in outbound responses. AI Gateway supports leading AI platforms including OpenAI, Anthropic, and Ollama, along with HTTP-based language models, providing consistent protection regardless of deployment location.

By implementing F5 solutions with Amazon EKS, you enable intelligent traffic management that delivers faster model response times and protections against AI-specific threats. The list of benefits goes on:

• AI-aware workload distribution. Least-time load balancing and active health checks direct AI requests to the most responsive services.
• Comprehensive observability. F5 solutions surface key metrics such as prompt volume, token usage, inference latency, and model performance statistics to help fuel your optimization efforts.
• Traffic protection. Rate limiting prevents resource abuse, circuit breaking isolates failures, and request buffering helps deal with traffic spikes.
• AI-specific threat mitigation. Built-in safeguards block AI model attacks while preventing sensitive data leaks.
• Identity management and access controls. Support for JSON Web Tokens, OpenID Connect, and role-based access control (RBAC) ensure only authorized users and services can access AI endpoints.

When it comes to AI, no optimization is worth overlooking. F5 solutions work consistently across all environments, whether it’s AWS, on premises, or hybrid multicloud, and address the unique challenges of AI in Kubernetes.

Enable your AI to run smoothly, reliably, and with enhanced protection against the threats of today and tomorrow. Every advantage you can secure puts you one step closer to realizing success for AI projects in this highly competitive and evolving landscape.

Learn more at F5 on Amazon Web Services (AWS).