Overcoming App Delivery And Security Challenges In AI Deployments

Partner content Organizations deploying AI applications are discovering that traditional security and delivery approaches have limitations. Unlike conventional web applications that behave predictably, AI applications generate non-deterministic responses that can vary over time. This creates new attack vectors and monitoring challenges that existing tools weren't designed to handle.

AI applications often increase this complexity by pulling data from multiple structured and unstructured sources. Each data source represents a potential entry point for attackers using techniques like prompt injection to steal sensitive information or manipulate model outputs.

Traditional API security tools and web application firewalls work well with deterministic content where request and response formats are predictable, but they face challenges with AI's inherent variability.

F5 developed the AI Gateway to address these challenges. It provides specialized protection, acceleration, and observability for AI applications while maintaining enterprise security and compliance standards.

Protecting AI applications from emerging threats

The F5 AI Gateway monitors AI traffic bidirectionally, recognizing that many organizations rely on AI-as-a-service platforms like ChatGPT, Azure OpenAI, or Google's AI services. This monitoring approach focuses on the most critical AI-specific threats that can be addressed at the gateway level.

On the inbound side, the gateway integrates with existing security frameworks to detect and block attacks including prompt injection and denial-of-service attempts targeting AI models. For outbound responses, it identifies and scrubs personally identifiable information (PII) from AI-generated content.

As AI security threats evolve rapidly, the AI Gateway's capabilities continue to expand. However, not all risks from frameworks like the OWASP Top Ten for Large Language Models can be addressed by a gateway solution. Threats such as supply chain vulnerabilities or model training data poisoning require different approaches and controls.

The solution integrates with F5's NGINX and BIG-IP platforms. It extends proven application delivery capabilities to AI workloads across traditional data centers, multi-cloud environments, and edge deployments. This integration provides familiar traffic steering policies and volumetric DDoS protection while adding AI-specific security controls.

To ensure adaptability as threats evolve, the AI Gateway includes programmable security controls that can be customized and updated as new requirements emerge. Development teams can extend functionality using SDKs for Python, Rust, and Go, allowing security policies to evolve alongside AI applications.

Optimizing performance and controlling costs

AI applications present unique operational challenges that traditional application delivery solutions aren't equipped to handle. GPU compute costs can spiral quickly, response times vary unpredictably, and new regulatory compliance requirements add complexity to deployment strategies.

The F5 AI Gateway addresses these challenges through resource management. Its unified API interface simplifies access to multiple AI models. Its sophisticated load balancing and traffic optimization is designed for AI workloads. This approach can help organizations maintain consistent performance while controlling the financial impact of AI deployments.

One significant cost optimization feature is semantic caching, which identifies duplicate or similar queries and serves cached responses without consuming expensive LLM tokens. Combined with intelligent rate limiting and traffic routing, this can reduce operational costs while improving response times.

The solution also offers observability through OpenTelemetry-based metrics, tracking everything from token consumption and request volumes to system resource usage and performance characteristics. This visibility enables organizations to optimize their AI operations while maintaining audit trails for governance and compliance requirements.

Real-world impact and infrastructure considerations

WorldTech IT, which provides professional and managed services for F5 and NGINX solutions, has seen measurable results from customer deployments. Its clients report significant cost savings from traffic routing and semantic caching alone, while the unified F5 service integration has eliminated hundreds of hours of custom integration work.

These benefits become particularly important when considering the broader context of AI infrastructure. Modern AI applications are part of what many organizations call "AI factories". These are systems that combine high-performance training and inference models to transform raw data into actionable insights.

These AI factories require massive storage, networking, and computing infrastructure to handle the volume and variety of data they process, from video and text to complex structured datasets. Without proper traffic management, they can become bottlenecks that limit the value organizations can extract from their AI investments.

Building on established traffic management foundations

F5's approach to AI Gateway development builds on more than two decades of experience in application traffic management. The company's established solutions, including BIG-IP Local Traffic Manager and next-generation hardware platforms, provide the foundation for AI-optimized traffic flows.

Specific optimizations like the FastL4 profile increase virtual server performance and throughput for AI workloads, while TCP optimizations ensure that network connections can handle the unique demands of AI traffic patterns. Features like BIG-IP's OneConnect efficiently manage connections between load balancers and back-end AI services, reducing overhead and improving overall system performance.

Meeting the demands of modern AI workloads

The F5 AI Gateway addresses some of the unique challenges of non-deterministic AI responses, multi-source data integration, and specialized threat vectors. It provides operational visibility and cost controls that can make AI deployments more sustainable and compliant with emerging regulatory requirements.

As AI continues to transform how organizations operate, an appropriate infrastructure foundation is becoming increasingly important for realizing the potential of these investments while managing their risks and costs.

Contributed by F5.