Securely Deliver AI Apps Everywhere
Deploy and protect AI workloads and apps everywhere, from corporate data centers, across clouds, and at the edge.
AI apps are pervasive...and complex. As organizations deploy AI apps, they add more complexity to their systems, resulting in architecture sprawl and increased risk.
To implement AI successfully, organizations must:
- Manage increased system complexity
- Implement cohesive and comprehensive security across the entire ecosystem
- Manage AI workloads, including model training and inference
F5 is the only solution provider that secures, delivers, and optimizes any app, any API, in any architecture. By powering and protecting your AI apps from the data center, across clouds, and at the edge, F5 solutions uniquely increase operational efficiency, improve resource management, and reduce time to value while addressing the new security risks associated with AI deployments.
AI Is Driving Changes to Infrastructure
The New AI Stack
The building blocks for AI apps vary depending on implementation details. Organizations with predictive AI have different needs than those using generative AI. Business that rely on third party models have different needs than those that plan to train and tune their own AI models. In all cases, organizations must consider all tiers of a new AI stack.
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Infrastructure
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Augmentation data and tools
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AI models
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Application services
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Applications
To ensure successful AI implementations, organizations must ensure that their infrastructure can support data security during ingest, efficient processing of training and inference workloads, and connectivity across all the APIs that are fundamental to their AI ecosystem. The infrastructure must also support business needs, ensuring cost reductions wherever possible. In addition to compute power and cost efficiency, security is a key consideration, as the additional complexity expands the organization’s attack surface.
AI is only as good as the data that powers it. To successfully implement AI, organizations will need to source large quantities of high-quality, well-organized data that is compliant with any relevant regulations. They may need to rely on APIs to integrate disparate data sets, plugins, and GPTs, creating additional security requirements. Telemetry and observability will be crucial to giving leaders insights into whether AI is serving their organizational goals.
With hundreds of models already available today, it is crucial for organizations to choose the AI model that aligns with their use cases and business objectives, whether it be foundational, fine-tuned, or custom. The model that you choose has implications for authentication, security, and monitoring, as well as integration with existing systems and resources.
Like any app-based service, successful AI implementation depends on having an effective strategy for supporting access, management, and optimization of the services that connect with the model that is selected. These touchpoints will also require security and compliance with relevant regulations. Large language model (LLM) apps are subject to the same risks as web apps and APIs, along with specific risks to natural language processing (NLP) interfaces, automation agents, external data sources, plugins/extensions, and downstream services. Careful selection of the right application services can help improve the long-term success of any organization’s AI implementation.
Whether an organization is standing up a customer-facing chatbot or an internal content generation tool, AI apps will need delivery and security services that align with where they are deployed, how they integrate with other services, who uses them, what data is needed to provide services to users, and what data is needed to assess the health and performance of the apps and interconnected APIs.
The Rise of Generative AI: Top Trends, Technologies, and Predictions
Solution Overviews
Secure multicloud networking for AI workloads
The F5 secure multicloud networking solution delivers secure connectivity and scalable deployment for AI workloads across any environment—cloud, edge, hybrid, and on-premises. Automate connectivity between workloads, ensure consistent security, and simplify AI deployment anywhere in the ecosystem with one solution managed through a single pane of glass. F5 Secure Multicloud Networking for AI Workloads lets you deploy training models in private clouds or in the data center while provisioning secure connectivity to data sources that live in the cloud or at the edge.
Secure model inference for Kubernetes
Kubernetes, the de facto standard for container orchestration and management, is the platform of choice for powering scalable LLM workloads and inference models across hybrid and multicloud environments. The Secure Model Inference for Kubernetes solution provides fast, reliable, and secure communications for AI/ML workloads running in Kubernetes—on-premises and in the cloud. Ingress controller, load balancer, and API gateway capabilities bring better uptime, protection, and visibility at scale while reducing complexity and operational cost.
Ecosystem Integrations
F5 Distributed Cloud Services Now Supports AIShield GuArdIan for Generative AI Applications and LLM Security
Resources
Read our deep dive into how organizations currently harness the power of AI and how those plans may change in the future.
Next Steps
