Kubernetes continues to evolve as the platform of choice for generative AI, providing the foundation for containerization, ensuring that AI models, data processing pipelines, and other services can be efficiently managed and scaled. It allows for the dynamic allocation of computing resources, ensuring optimal performance and resource utilization. Cloud-native Kubernetes facilitates the seamless deployment of AI workloads across hybrid and multi-cloud environments. The vibrant ecosystem around Kubernetes is proving to be a formidable force in accelerating AI innovation and adoption. Collaboration among industry leaders, open-source projects, and cloud providers is fostering breakthroughs in AI technology. 

APIs are the linchpin in AI architectures, enabling different components and services to communicate with each other. APIs provide the connective tissue for various parts of the AI application to exchange data and instructions. For example, an AI model may leverage APIs to request data from a cloud-based storage service or send its predictions to a different component for decision-making. Additionally, OpenAI plugins leverage APIs to further enhance ChatGPT’s capabilities by enabling ChatGPT to interact with APIs defined by developers. 

Traditional data centers often struggle to handle the demanding requirements of advanced AI workloads, raising concerns about capacity and suitability for the modern digital landscape. The high volume of data produced by AI model training and fine tuning introduces “data gravity” as a significant concern for companies adopting AI. Data gravity emerges as data volume in a repository expands alongside its increasing utility. Eventually, the challenge of copying or moving this data becomes burdensome and costly. Consequently, the data inherently draws services, applications, and additional data into its repository. Data gravity impacts generative AI in two ways. First, it constrains the availability and accessibility of data for training and generation. And it simultaneously amplifies the necessity and value of data for refining and elevating generative AI models and their outcomes. 

Multi-cloud environments have become the foundation for the new class of AI-powered applications because of their ability to make private yet highly distributed data easier to harness. Multi-cloud environments further enhance the architecture’s flexibility and resilience by allowing AI applications to leverage best of breed resources from different cloud providers. Multi-cloud also lowers the risk of cloud vendor lock-in and protects against potential downtime while providing migration opportunities. Kubernetes, in conjunction with APIs, ensures that these multi-cloud environments can be efficiently managed and orchestrated, simplifying the deployment and scaling of AI workloads across diverse cloud platforms. 

While a multi-cloud approach offers flexibility and scalability, it also introduces challenges in terms of data consistency, security, and compliance. Organizations need to ensure that workloads and data can be transferred securely between clouds, on-premises data centers, or in a data lake. To ensure data sovereignty, organizations need to meet certain industry and governmental regulatory requirements, without severely impacting AI response times. Leveraging multi-cloud environments can assist with this, too, by providing global organizations access to cloud services in different geographic locations, helping to address regulatory compliance. Addressing this problem is a challenge when you consider that each AI model is built with access to a vast database to provide the right inferences for user queries in real time, and that training data can live anywhere. 

In an ideal world, companies should satisfy the rigorous connectivity, security, and scalability requirements associated with AI workloads with a unified solution that extends consistent application and security services across public clouds, private clouds, native Kubernetes, and edge. 

Since AI workloads should be deployed as close as possible to the data they require, workloads are often deployed across multiple clouds, making it a challenge to maintain centralized visibility and control. Adding generative AI workloads to an already distributed application environment further expands the enterprise threat surface. 

It is prudent to acknowledge that while AI provides tremendous benefits to the applications that use it, to fully leverage the advantages of AI companies must take the steps necessary to optimize and secure their AI workloads. This requires not only enhancing the efficiency of AI workloads, but also the management of complex Kubernetes environments, seamless and secure integration of APIs, and effective management of multi-cloud networks.  

Generative AI and other AI toolkits are becoming prime attack surfaces for cybercriminals who often use AI to deploy more novel and sophisticated attacks to access Personally Identifiable Information (PII) and other confidential data—including in-training data that has the potential to expose trade secrets or intellectual property (IP). Security operation teams must be able to detect and thwart adversarial attacks, including malicious manipulation of AI and ML models. From powering deepfakes that are nearly impossible to distinguish from reality, to launching sophisticated phishing email campaigns that spread ransomware attacks, cyber criminals are both targeting and leveraging AI for malicious gains.  

Another key security concern in AI environments is Shadow AI. Like Shadow IT, Shadow AI refers to the use of AI tools that are used outside of corporate governance. Shadow AI becomes an issue when employees “go around” IT, ignoring policies and processes put in place to protect the business, typically because they believe policies and processes slow down innovation and prevent them from taking advantage of AI for development and productivity gains. With the explosive use of generative AI by employees throughout an organization, plus the lack of proper governance, coupled with learning models that often consume sensitive data, Shadow AI presents a significant threat to the exposure of PII, corporate IP, and other sensitive company data. Organizations must implement mechanisms to protect against the dangers of Shadow AI.  

While AI may seem like magic, it isn’t: It’s really just a powerful modern application, like many others. And LLMs are simply algorithms that understand natural language and learn from large data models or data lakes to understand, summarize, create, and predict new content, leveraging ML.  

In this rapidly evolving landscape, F5 delivers solutions that include and use AI to drive and defend your AI by powering and protecting AI with AI. F5 provides industry-leading delivery, performance, and security services that extend across your entire distributed application environment.  

Whether you’re running AI workloads on a warehouse floor or in a corporate office, F5’s unified solutions extend consistent application and security services across public clouds, private clouds, native Kubernetes, and edge, helping you to reduce AI complexity while providing unmatched scale and performance. Securely interconnect the different elements of AI applications across different locations, environments, and clouds to fully leverage the benefits of this new, modern application paradigm.  

F5 allows the flexibility to deliver AI close to your data–from the data center to the edge–to maximize accuracy, insights, performance, cost efficiency, and security.  

F5® Distributed Cloud Network Connect and F5®Distributed Cloud App Connect allow training models to be deployed in private clouds or in the data center, while also provisioning secure connectivity to data sources that live in the cloud or at the edge. 

Part of the F5® Distributed Cloud Services portfolio, Distributed Cloud Network Connect provides Layer 3 connectivity across any environment or cloud provider, including on-premises data centers and edge sites, in a SaaS-based tool. It provides end-to-end visibility, automates provisioning of links and network services, and enables the creation of consistent, intent-based security policies across all sites and providers. 

F5® Distributed Cloud App Stack easily deploys, manages, and secures AI workloads with uniform production grade Kubernetes, no matter the location–from private and public clouds, to edge locations. It supports AI models at the local edge with built-in GPU support that ensures high performance availability. Distributed Cloud App Stack simplifies AI/LLM inference apps deployment by delivering apps and security across any number of edge sites with centralized workflows. 

Additionally, F5® NGINX® Connectivity Stack for Kubernetes provides fast, reliable, and secure communications for AI/ML workloads running in Kubernetes–on-premises and in the cloud. A single tool including ingress controller, load balancer, and API gateway capabilities, NGINX Connectivity Stack for Kubernetes enhances uptime, protection, and visibility at scale, while reducing complexity and operational cost. NGINX Connectivity Stack for Kubernetes helps scale, observe, govern, and secure AI workloads from edge to cloud with a collection of tools and integrations that improve customer experiences while reducing complexity, improving uptime, and enhancing real-time visibility. 

Empower SecOps to secure applications and API interfaces that are the conduit to AI workloads and adapt to adversarial attacks on AI models and environments by streamlining WAF, bot, API, and DDoS protections from a single point of control. 

Having visibility into all AI workloads across the hybrid and multi-cloud stack is critical for addressing shadow AI use, among other concerns, that put proprietary data at risk. Adding gen-AI workloads to an already distributed app environment logically expands the enterprise threat surface, creating opportunities for model denial-of service (DoS) attacks, training data poisoning, and API exploits.  

Leverage F5® Distributed Cloud Web App and API Protection (WAAP) to keep data models secure and governed in order to safeguard intellectual property from unintended use. Reap the benefits of “click to enable, run anywhere” security policies for consistent and repeatable protection, global coverage, and enforcement. An API-driven approach to workload protection enables improved collaboration between network, security operations, and developers.  

With Distributed Cloud WAAP, organizations can simplify their path to effective AI workload security without sacrificing continued business innovation. This includes delivering a comprehensive approach to runtime analysis and protection of APIs with a combination of management and enforcement functionality. Easily and effectively monitor all API endpoints and application paths—discover and track unknown or shadow APIs, and secure them with continuous inspection and schema enforcement. F5’s API security solutions protect the APIs that enable AI-specific interactions, mitigate the risks associated with unauthorized access, data breaches, abuse, and critical vulnerabilities. This ensures that applications and any critical AI workloads operate seamlessly and securely. 

Defend against malicious bots, including those attempting to manipulate LLMs, with a platform that adapts to an attacker’s retooling attempts across thousands of the world’s most highly trafficked applications and AI workloads. Achieve highly effective bot protection based on unparalleled analysis of devices and behavioral signals to unmask and mitigate automated malicious bot attacks. Plus, ensure your data is safe in transit with F5’s Distributed Cloud Network Connect. Gain universal visibility, dynamic discovery, AI-based insights, and auto remediation with F5. 

Remove complexity to unleash business agility and innovation by automatically connecting distributed AI workloads across environments by abstracting the underlying infrastructure. 

F5® Distributed Cloud Secure Multi-Cloud Network (MCN) reduces the complexity of managing and deploying AI workloads.  Automatically connect distributed AI workloads across your environment—cloud, multi-cloud, edge—without having to worry about the underlying infrastructure. Optimize the value of your AI initiative by pulling data analytics that combine and correlate data across your workloads. Establish a central point of control for managing policies for any application or AI workload anywhere. 

Customers who are running enterprise-class AI applications and demand a powerful, enterprise-class solution will want to leverage the benefits of F5’s Distributed Cloud Secure MCN to extend application security and services across public and hybrid deployments, native Kubernetes, and edge sites.  

Connect, secure, and manage apps and workloads in the cloud, at the edge, or in the F5 global network. Distributed Cloud App Stack simplifies how AI training apps are managed, deployed, and delivered. Push software and OS updates to sites—all with a few simple clicks. 

AI workloads, especially those related to generative AI, demand substantial computational resources. F5’s application acceleration solutions are tailored to optimize the performance of AI workloads, including efficiently sharing GPU resources.  By optimizing efficiencies, reducing latency, and improving response times, F5 accelerates the delivery of AI predictions, ensuring a seamless user experience and supporting real-time decision-making in AI-driven applications. 

For organizations with a global footprint, F5’s global traffic management solutions play a pivotal role in optimizing the placement of AI workloads and ensuring data sovereignty. These solutions efficiently distribute AI workloads across geographically dispersed data centers and cloud regions, enhancing performance while ensuring high availability and redundancy for mission-critical AI and AI-driven applications. 

F5 harnesses the power of AI and ML to provide actionable insights into the performance and security of AI workloads. Continuous monitoring and analysis of traffic patterns and application behavior enable organizations to make data-driven decisions about workload placement and resource allocation, ensuring optimal performance of AI workloads. 

Artificial Intelligence continues its march into every facet of modern business and life—with generative AI taking the lead. To support this evolution, companies must ensure their infrastructure can leverage the benefits of AI to minimize lag, latency, and risk. As organizations navigate the complexities of AI—including generative AI and AI workloads—F5 remains a trusted partner, empowering enterprises to fully leverage the wonders and benefits of AI across the vast generative AI ecosystem.

F5 powers and protects your AI with AI, delivering industry-leading performance, and delivery and security services that extend across the entire distributed application environment. Whether you’re running AI workloads on a warehouse floor or in a corporate office, put powerful tools in the hands of employees and partners to gain new insights and drive new efficiencies. With robust security measures fueled by extensive big-data telemetry, customers acquire proactive measures to mitigate evolving AI risks. This empowers organizations to either maintain or reach the forefront of innovation while being well-prepared for the challenges and opportunities within this rapidly evolving and transformative technological landscape.