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Mitigate AI Scraping bots using F5 Distributed Cloud Web Application Firewall
This short article demonstrates how to detect and mitigate AI scraping bots using F5 Distributed Cloud WAF.
Janibasha
Jul 18, 2025 Place Technical Articles
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F5 and MinIO: AI Data Delivery for the hybrid enterprise
Introduction Modern application architectures demand solutions that not only handle exponential data growth but also enable innovation and drive business results. As AI/ML workloads take center stage in industries ranging from healthcare to finance, application designers are increasingly turning to S3-compliant object storage because of its ability to provide scalable management of unstructured data. Whether it’s for ingesting massive datasets, running iterative training models, or delivering high-throughput predictions, S3-compatible storage systems play a foundational role in supporting these advanced data pipelines. MinIO has emerged as a leader in this space, offering high-performance, S3-compatible object storage built for modern-scale applications. MinIO is designed to easily work with AI/ML workflows. It is lightweight and cloud-based, so it is a good choice for businesses that are building infrastructure to support innovation. From storing petabyte-scale datasets to providing the performance needed for real-time AI pipelines, MinIO delivers the reliability and speed required for data-intensive work. While S3-compliant storage like MinIO forms the backbone of data workflows, robust traffic management and application delivery capabilities are essential for ensuring continuous availability, secure pipelines, and performance optimization. F5 BIG-IP, with its advanced suite of traffic routing, load balancing, and security tools, complements MinIO by enabling organizations to address these challenges. Together, F5 and MinIO create a resilient, scalable architecture where applications and AI/ML systems can thrive. This solution empowers businesses to: Build secure and highly-available storage pipelines for demanding workloads. Ensure fast and reliable delivery of data, even at exascale. Simplify and optimize their infrastructure to drive innovation faster. In this article, we’ll explore how to leverage F5 BIG-IP and MinIO AIStor clusters to enable results-driven application design. Starting with an architecture overview, we’ll cover practical steps to set up BIG-IP to enhance MinIO’s functionality. Along the way, we’ll highlight how this combination supports modern AI/ML workflows and other business-critical applications. Architecture Overview To validate the solution of F5 BIG-IP and MinIO AIStor effectively, this setup incorporates a functional testing environment that simulates real-world behaviors while remaining controlled and repeatable. MinIO’s warp benchmarking tool is used for orchestrating and running tests across the architecture. The addition of benchmarking tools ensures that the functional properties of the stack (traffic management, application-layer security, and object storage performance) are thoroughly evaluated in a way that is reproducible and credible. The environment consists of: A F5 VELOS chassis with BX110 blades, running BIG-IP instances configured using F5’s AS3 extension, for traffic management and security policies using LTM (Local Traffic Manager) and ASM (Application Security Manager). A MinIO AIStor cluster consisting of four bare-metal nodes equipped with high-performance NVMe drives, bringing the environment close to real-world customer deployments. Three benchmarking nodes for orchestrating and running tests: One orchestration node directs the worker nodes with benchmark test configuration and aggregates test results. Two worker nodes run warp in client mode to simulate workloads against the MinIO cluster. Warp Benchmarking Tool The warp benchmarking tool (https://github.com/minio/warp) from MinIO is designed to simulate real-world S3 workloads while also generating measurable metrics about the testing environment. In this architecture: A central orchestration node is used to coordinate the benchmarking process. This ensures that each test is consistent and runs under comparable conditions. Two worker nodes running warp in client mode send simulated traffic to the F5 BIG-IP virtual server. These nodes act as workload generators, allowing for the simulation of read-heavy, write-heavy, or mixed object storage exercises. Warp’s distributed design enables the scaling of workload generation, ensuring that the MinIO backend is tested under real-world-like conditions. This three-node configuration ensures that benchmarking tests are distributed effectively. It also provides insights into object storage behavior, traffic management, and the impact of security enforcement in the environment. Traffic Management and Security with BIG-IP At the center of this setup is the F5 VELOS chassis, running BIG-IP instances configured to handle both traffic management (LTM) and application-layer security (ASM). The addition of ASM (Application Security Manager) ensures that the MinIO cluster is protected from malicious or malformed requests while maintaining uninterrupted service for legitimate traffic. Key functions of BIG-IP in this architecture include: Load Balancing: Avoid overloading specific MinIO nodes by using adaptive algorithms, ensuring even traffic distribution, and preventing bottlenecks and hotspots. Apply advanced load balancing methods like least connections, dynamic ratio, and least response time. These methods intelligently account for backend load and performance in real time, ensuring reliable and efficient resource utilization. SSL/TLS Termination: Terminate SSL/TLS traffic to offload encryption workloads from backend clients. Re-encryption is optionally enabled for secure communication to MinIO nodes, depending on performance and security requirements. Health Monitoring: Intelligently performs continuous monitoring of the availability and health of the backend MinIO nodes. It reroutes traffic away from unhealthy nodes as necessary and restores traffic as service is restored. Application-Layer Security: Protect the environment via Web Application Firewall (WAF) policies that block malicious traffic, including injection attacks, malformed API calls, and DDoS-style app-layer threats. BIG-IP acts as the gateway for all requests coming from S3 clients, ensuring that security, health checks, and traffic policies are all applied before requests reach the MinIO nodes. Traffic Flow Through the Full Architecture The test traffic flows through several components in this architecture, with BIG-IP and warp playing vital roles in managing and generating requests, respectively: Benchmark Orchestration: The warp orchestration node initiates tests and distributes workload configurations to the worker nodes. The warp orchestration node also aggregates test data results from the worker nodes. Warp manages benchmarking scenarios, such as read-heavy, write-heavy, or mixed traffic patterns, targeting the MinIO storage cluster. Simulated Traffic from Worker Nodes: Two worker nodes, running warp in client mode, generate S3-compatible traffic such as object PUT, GET, DELETE, or STAT requests. These requests are transmitted through the BIG-IP virtual server. The load generation simulates the kind of requests an AI/ML pipeline or data-driven application might send under production conditions. BIG-IP Processing: Requests from the worker nodes are received by BIG-IP, where they are subjected to: Traffic Control: LTM distributes the traffic among the four MinIO nodes while handling SSL termination and monitoring node health. Security Controls: ASM WAF policies inspect requests for signs of application-layer threats. Only safe, valid traffic is routed to the MinIO environment. Environment Configuration Prerequisites BIG-IP (physical or virtual) Hosts for the MinIO cluster, including configured operating systems (and scheduling systems if optionally selected) Hosts for the warp worker nodes and warp orchestration node, including configured operating systems All required networking gear to connect the BIG-IP and the nodes A copy of the AS3 template at https://github.com/f5businessdevelopment/terraform-kvm-minio/blob/main/as3manualtemplate.json A copy of the warp configuration file at https://github.com/minio/warp/blob/master/yml-samples/mixed.yml Step 1: Set up MinIO Cluster Follow MinIO’s install instructions at https://min.io/docs/minio/linux/index.html The link is for a Linux deployment but choose the deployment target that’s appropriate for your environment. Record the addresses and ports of the MinIO consoles and APIs configured in this step for use as input to the next steps. Step 2: Configure F5 BIG-IP for Traffic Management and Security Following the steps documented in https://github.com/f5businessdevelopment/terraform-kvm-minio/blob/main/MANUALAS3.md and using the template file downloaded from GitHub, create and apply an AS3 declaration to configure your BIG-IP. Step 3: Deploy and Configure MinIO Warp for Benchmarking Retrieve API Access and Secret keys Log into your MinIO Cluster and click the 'Access' icon Once in 'Access', click the 'Access Keys' button In ‘Access Keys’, click the ‘Create Access Keys’ button and follow the steps to create and record your access and secret key values. Update warp key and secret value In your warp configuration file, find the access-key and secret-key fields and update the values with those you recorded in the previous step. Update warp client addresses In your warp configuration file, find the warp-client field and update the value with the addresses of the worker nodes. Update warp s3 host address In your warp configuration file, find the host field and update the value with the address and port of the VIP listener on the BIG-IP Step 4: Verify and Monitor the Environment Start the warp on each of the worker nodes with the command warp client Once the warp clients respond that they are listening on the warp orchestrator node, start the warp benchmark test warp run test.yaml Replace test.yaml with the name of your configuration file Summary of Test Results Functional tests done in F5’s lab, using the method described above, show how the F5 + MinIO solution works and behaves. These results highlight important considerations that apply to both AI/ML pipelines and data repatriation workflows. This enables organizations to make informed design choices when deploying similar architectures. The testing goals were: Validate that BIG-IP security and traffic management policies function properly with MinIO AIStor in a simulated real-world configuration. Compare the impact of various load-balancing, security, and storage strategies to determine best practices. Test Methodology Four test configurations were executed to identify the effects of: Threads Per Worker: Testing both 1-thread and 20-thread configurations for workload generation. Multi-Part GETs and PUTs: Comparing scenarios with and without multi-part requests for better parallelization. BIG-IP Profiles: Evaluating Layer 7 (ASM-enabled security) versus Layer 4 (performance-optimized) profiles. Test Results Test Configuration Throughput Benefits 20 threads, multi-part, Layer 7 28.1 Gbps Security, High-performance reliability 20 threads, multi-part, Layer 4 81.5 Gbps High-performance reliability 1 thread, no multi-part, Layer 7 3.7 Gbps Security, Reliability 1 thread, no multi-part, Layer 4 7.8 Gbps Reliability Note: The testing results provide insights into the solution and behavior of this setup, though they are not intended as production performance benchmarks. Key Insights Multi-Part GETs and PUTs Are Critical for Throughput Optimization: Multi-part operations split objects into smaller parts for parallel processing. This allows the architecture to better utilize MinIO’s distributed storage capabilities and worker thread concurrency. Without multi-part GETs/PUTs, single-threaded configurations experienced severely reduced throughput. Recommendation: Ensure multi-part operations are enabled in applications or tools interacting with MinIO when handling large objects or high IOPS workloads. Balance Security with Performance: Layer 7 security provided by ASM is essential for sensitive data and workloads that interact with external endpoints. However, it introduces processing overhead. Layer 4 performance profiles, while lacking application-layer security features, deliver significantly higher throughput. Recommendation: Choose BIG-IP profiles based on specific workload requirements. For AI/ML data ingest and model training pipelines, consider enabling Layer 4 optimization during bulk read/write phases. For workloads requiring external access or high-security standards, deploy Layer 7 profiles. In some cases, consider horizontal scaling of load balancing and object storage tiers to add throughput capacity. Threads Per Worker Impact Throughput: Scaling up threads at the worker level significantly increased throughput in the lab environment. This demonstrates the importance of concurrency for demanding workloads. Recommendation: Optimize S3 client configurations for higher connection counts where workloads permit, particularly when performing bulk data transfers or operationally intensive reads. Example Use Cases Use Case #1: AI/ML Pipeline AI and machine learning pipelines rely heavily on storage systems that can ingest, process, and retrieve vast amounts of data quickly and securely. MinIO provides the scalability and performance needed for storage, while F5 BIG-IP ensures secure, optimized data delivery. Pipeline Workflow An enterprise running a typical AI/ML pipeline might include the following stages: Data Ingestion: Large datasets (e.g., images, logs, training corpora) are collected from various sources and stored within the MinIO cluster using PUT operations. Model Training: Data scientists iterate on AI models using the stored training datasets. These training processes generate frequent GET requests to retrieve slices of the dataset from the MinIO cluster. Model Validation and Inference: During validation, the pipeline accesses specific test data objects stored in the cluster. For deployed models, inference may require low-latency reads to make predictions in real time. How F5 and MinIO Support the Workflow This combined architecture enables the pipeline by: Ensuring Consistent Availability: BIG-IP distributes PUT and GET requests across the four nodes in the MinIO cluster using intelligent load balancing. With health monitoring, BIG-IP proactively reroutes traffic away from any node experiencing issues, preventing delays in training or inference. Optimizing Performance: NVMe-backed storage in MinIO ensures fast read and write speeds. Together with BIG-IP's traffic management, the architecture delivers reliable throughput for iterative model training and inference. Securing End-to-End Communication: ASM protects the MinIO storage APIs from malicious requests, including malformed API calls. At the same time, SSL/TLS termination secures communications between AI/ML applications and the MinIO backend. Use Case #2: Enterprise Data Repatriation Organizations increasingly seek to repatriate data from public clouds to on-premises environments. Repatriation is often driven by the need to reduce cloud storage costs, regain control over sensitive information, or improve performance by leveraging local infrastructure. This solution supports these workflows by pairing MinIO’s high-performance object storage with BIG-IP’s secure and scalable traffic management. Repatriation Workflow A typical enterprise data repatriation workflow may look like this: Bulk Data Migration: Data stored in public cloud object storage systems (e.g., AWS S3, Google Cloud Storage) is transferred to the MinIO cluster running on on-premises infrastructure using tools like MinIO Gateway or custom migration scripts. Policy Enforcement: Once migrated, BIG-IP ensures that access to the MinIO cluster is secured, with ASM enforcing WAF policies to protect sensitive data during local storage operations. Ongoing Storage Optimization: The migrated data is integrated into workflows like backup and archival, analytics, or data access for internal applications. Local NVMe drives in the MinIO cluster reduce latency compared to cloud solutions. How F5 and MinIO Support the Workflow This architecture facilitates the repatriation process by: Secure Migration: MinIO Gateway, combined with SSL/TLS termination on BIG-IP, allows data to be transferred securely from public cloud object storage services to the MinIO cluster. ASM protects endpoints from exploitation during bulk uploads. Cost Efficiency and Performance: On-premises MinIO storage eliminates expensive cloud storage costs while providing faster access to locally stored data. NVMe-backed nodes ensure that repatriated data can be rapidly retrieved for internal applications. Scalable and Secure Access: BIG-IP provides secure access control to the MinIO cluster, ensuring only authorized users or applications can use the repatriated data. Health monitoring prevents disruptions in workflows by proactively managing node unavailability. The F5 and MinIO Advantage Both use cases reflect the flexibility and power of combining F5 and MinIO: AI/ML Pipeline: Supports data-heavy applications and iterative processes through secure, high-performance storage. Data Repatriation: Empowers organizations to reduce costs while enabling seamless local storage integration. These examples provide adaptable templates for leveraging F5 and MinIO to solve problems relevant to enterprises across various industries, including finance, healthcare, agriculture, and manufacturing. Conclusion The collaboration of F5 BIG-IP and MinIO provides a high-performance, secure, and scalable architecture for modern data-driven use cases such as AI/ML pipelines and enterprise data repatriation. Testing in the lab environment validates the functionality of this solution, while highlighting opportunities for throughput optimization via configuration tuning. To bring these insights to your environment: Test multi-part configurations using tools like MinIO warp benchmark or production applications. Match BIG-IP profiles (Layer 4 or Layer 7) with the specific priorities of your workloads. Use these findings as a baseline while performing further functional or performance testing in your enterprise. The flexibility of this architecture allows organizations to push the boundaries of innovation while securing critical workloads at scale. Whether driving new AI/ML pipelines or reducing costs in repatriation workflows, the F5 + MinIO solution is well-equipped to meet the demands of modern enterprises. Further Content For more information about F5's partnership with MinIO, consider looking at the informative overview by buulam on DevCentral's YouTube channel. We also have the steps outlined in this video.
Mark_Menger
Jul 17, 2025 Place Technical Articles
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Introducing F5 AI Gateway
This article introduces F5 AI Gateway and shows a demo of the main use cases: route abstraction, context routing, transaction logging for audit purposes and security controls tailored for GenAI traffic.
Valentin_Tobi
Jul 16, 2025 Place Technical Articles
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MCP Session Affinity With F5 NGINX Plus
Scaling out MCP servers presents a challenge with session management, which can be addressed by NGINX Plus' session tracking ability to provide session affinity.
Leon_Seng
Jun 24, 2025 Place Technical Articles
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Getting Started With n8n For AI Automation
First, what is n8n? If you're not familiar with n8n yet, it's a workflow automation utility that allows us to use nodes to connect services quite easily. It's been the subject of quite a bit of Artificial Intelligence hype because it helps you construct AI Agents. I'm going to be diving more into n8n, what it can do with AI, and how to use our AI Gateway to defend against some difficult AI threats today. My hope is that you can use this in your own labs to prove out some of these things in your environment. Here's an example of how someone could use Ollama to control multiple Twitter accounts, for instance: How do you install it? Well... It’s all node, so the best way to install it in any environment is to ensure you have node version 22 (on Mac, homebrew install node@22) installed on your machine, as well as nvm (again, for mac, homebrew install nvm) and then do an npm install -g n8n. Done! Really...That simple. How much does it cost? While there is support and expanded functionality for paid subscribers, there is also a community edition that I have used here and it's free. How to license:
AubreyKingF5
Jun 09, 2025 Place Technical Articles
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Getting Started With n8n For AI Automation
First, what is n8n? If you're not familiar with n8n yet, it's a workflow automation utility that allows us to use nodes to connect services quite easily. It's been the subject of quite a bit of Artificial Intelligence hype because it helps you construct AI Agents. I'm going to be diving more into n8n, what it can do with AI, and how to use our AI Gateway to defend against some difficult AI threats today. My hope is that you can use this in your own labs to prove out some of these things in your environment. Here's an example of how someone could use Ollama to control multiple Twitter accounts, for instance: How do you install it? Well... It’s all node, so the best way to install it in any environment is to ensure you have node version 22 (on Mac, homebrew install node@22) installed on your machine, as well as nvm (again, for mac, homebrew install nvm) and then do an npm install -g n8n. Done! Really...That simple. How much does it cost? While there is support and expanded functionality for paid subscribers, there is also a community edition that I have used here and it's free. How to license:
AubreyKingF5
Jun 09, 2025 Place Technical Articles
299Views
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Introducing AI Assistant for F5 Distributed Cloud, F5 NGINX One and BIG-IP
This article is an introduction to AI Assistant and shows how it improves SecOps and NetOps speed across all F5 platforms (Distributed Cloud, NGINX One and BIG-IP) , by solving the complexities around configuration, analytics, log interpretation and scripting.
Valentin_Tobi
May 26, 2025 Place Technical Articles
415Views
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Scaling and Traffic-Managed Model Context Protocol (MCP) with BIG-IP Next for K8s
Introduction As AI models get more advanced, running them at scale—especially in cloud-native environments like Kubernetes—can be tricky. That’s where the Model Context Protocol (MCP) comes in. MCP makes it easier to connect and interact with AI models, but managing all the traffic and scaling these services as demand grows is a whole different challenge. In this article and demo video, I will show how F5's BIG-IP Next for K8s (BNK), a powerful cloud native traffic management platform from F5 can solve that and keep things running smoothly and scale your MCP services as needed. Model Context Protocol (MCP) in a nutshell. There were many articles explaining what is MCP on the internet. Please refer to those in details. In a nutshell, it is a standard framework or specification to securely connect AI apps to your critical data, tools, and workflow. The specification allow Tracking of context across multiple conversation Tool integration — model call external tools Share memory/state — remember information. MCP’s "glue" model to tools through a universal interface "USB-C for AI" What EXACTLY does MCP solve? MCP addresses many challenges in the AI ecosystem. I believe two key challenges it solve Complexities of integrating AI Model (LLM) with external sources and tools By standardization with a universal connector ("USB-C for AI") Everyone build "USB-C for AI" port so that it can be easily plug in each other Interoperability. Security with external integration Framework to establish secure connection Managing permission and authorization. What is BIG-IP’s Next for K8s (BNK)? BNK is F5 modernized version of the well-known Big-IP platform, redesigned to work seamlessly in cloud-native environments like Kubernetes. It is a scalable networking and security solution for ingress and egress traffic control. It builds on decades of F5's leadership in application delivery and security. It powers Kubernetes networking for today's complex workloads. BNK can be deployed on X86 architecture or ARM architecture - Nvidia Data Processing Unit (DPU) Lets see how F5's BNK scale and traffic managed an AIOps ecosystem. DEMO Architecture Setup Video Key Takeaways BIGIP Next for K8s, the backbone of the MCP architecture Technology built on decades of market-leading application delivery controller technology Secure, Deliver, and Optimize your AI infrastructure Provides deep insight through observability and visibility of your MCP traffic.
Foo-Bang_Chan
May 19, 2025 Place Technical Articles
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F5 AI Gateway to Strengthen LLM Security and Performance in Red Hat OpenShift AI
In my previous article, we explored how F5 Distributed Cloud (XC) API Security enhances the perimeter of AI model serving in Red Hat OpenShift AI on ROSA by protecting against threats such as DDoS attacks, schema misuse, and malicious bots. As organizations move from piloting to scaling GenAI applications, a new layer of complexity arises. Unlike traditional APIs, LLMs process free-form, unstructured inputs and return non-deterministic responses—introducing entirely new attack surfaces. Conventional web or API firewalls fall short in detecting prompt injection, data leakage, or misuse embedded within model interactions. Enter F5 AI Gateway—a solution designed to provide real-time, LLM-specific security and optimization within the OpenShift AI environment. Understanding the AI Gateway Recent industry leaders have said that an AI Gateway layer is coming into use. This layer is between clients and LLM endpoints. It will handle dynamic prompt/response patterns, policy enforcement, and auditability. Inspired by these patterns, F5 AI Gateway brings enterprise-grade capabilities such as: Inspecting and Filtering Traffic: Analyzes both client requests and LLM responses to detect and mitigate threats such as prompt injection and sensitive data exposure. Implementing Traffic Steering Policies: Directs requests to appropriate LLM backends based on content, optimizing performance and resource utilization. Providing Comprehensive Logging: Maintains detailed records of all interactions for audit and compliance purposes. Generating Observability Data: Utilizes OpenTelemetry to offer insights into system performance and security events. These capabilities ensure that AI applications are not only secure but also performant and compliant with organizational policies. Integrated Architecture for Enhanced Security The combined deployment of F5 Distributed Cloud API Security and F5 AI Gateway within Red Hat OpenShift AI creates a layered defense strategy: F5 Distributed Cloud API Security: Acts as the first line of defense, safeguarding exposed model APIs from external threats. F5 AI Gateway: Operates within the OpenShift AI cluster, providing real-time inspection and policy enforcement tailored to LLM traffic. This layered design ensures multi-dimensional defense, aligning with enterprise needs for zero-trust, data governance, and operational resilience. Key Benefits of F5 AI Gateway Enhanced Security: Mitigates risks outlined in the OWASP Top 10 for LLM Applications - such as prompt injection (LLM01) - by detecting malicious prompts, enforcing system prompt guardrails, and identifying repetition-based exploits, delivering contextual, Layer 8 protection. Performance Optimization: Boosts efficiency through intelligent, context-aware routing and endpoint abstraction, simplifying integration across multiple LLMs. Scalability and Flexibility: Supports deployment across various environments, including public cloud, private cloud, and on-premises data centers. Comprehensive Observability: Provides detailed metrics and logs through OpenTelemetry, facilitating monitoring and compliance. Conclusion The rise of LLM applications requires a new architectural mindset. F5 AI Gateway complements existing security layers by focusing on content-level inspection, traffic governance, and compliance-grade visibility. It is specifically tailored for AI inference traffic. When used with Red Hat OpenShift AI, this solution provides not just security, but also trust and control. This helps organizations grow GenAI workloads in a responsible way. For a practical demonstration of this integration, please refer to the embedded demo video below. If you’re planning to attend this year’s Red Hat Summit, please attend an F5 session and visit us in Booth #648. Related Articles: Securing model serving in Red Hat OpenShift AI (on ROSA) with F5 Distributed Cloud API Security
Eric_Ji
May 16, 2025 Place Technical Articles
352Views
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AI, Red Teaming, and Post-Quantum Cryptography: Key Insights from RSA 2025
Join Aubrey and Byron at RSA Conference 2025 as they dive into transformative topics like artificial intelligence, red-teaming strategies, and post-quantum cryptography. From exploring groundbreaking OWASP sessions to analyzing emerging AI threats, this episode highlights key insights that shape the future of cybersecurity. Discover the challenges in red team AI testing, the implications of APIs in multi-cloud environments, and how quantum-resistant cryptography is rising to meet AI-driven threats. Don't miss this exciting recap of RSA 2025!
AubreyKingF5
May 06, 2025 Place Technical Articles
114Views
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AI Scraping bots using F5 Distributed Cloud Web Application Firewall","conversation":{"__ref":"Conversation:conversation:342190"},"id":"message:342190","entityType":"TKB_ARTICLE","eventPath":"category:Articles/community:zihoc95639board:TechnicalArticles/message:342190","revisionNum":12,"uid":342190,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:194786"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" This short article demonstrates how to detect and mitigate AI scraping bots using F5 Distributed Cloud WAF. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":98},"postTime":"2025-07-14T08:00:00.041-07:00","lastPublishTime":"2025-07-18T08:47:05.235-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction \n Web scraping isn’t a new phenomenon, but the advent of AI and using scraped data to train AI models has elevated concerns associated with it. Previously, generative AI models could only use data that was being fine-tuned. With recent improvements like RAG, AI Agents, MCP, and others, LLMs can now get dynamic content from websites. \n IETF already published a standard way to control the data access for generic bots using directives like Disallow. Bots conforming to this RFC 9309 should follow these rules by looking into robots.txt file in root folder. To deal specifically with AI Scraping, a new draft (draft-canel-robots-ai-control-00) was suggested to control the data access for these automated AI crawlers. It added 2 more directives: DisallowAITraining which prohibits data access and AllowAITraining directive, which permits data fetching. \n \n Mitigation Solutions \n Let's see some of the mitigation solutions available in F5 Distributed Cloud Web Application Firewall (F5 XC WAF). For this demo, we will focus on the below 5 solutions and will dive into each solution one by one. \n \n First Solution: \n This solution focuses on preventing bots, which follow the new draft (draft-canel-robots-ai-control-00). For this demo, we are using the Python Scrapy library, which allows users to customize the existing code. \n \n Open the Scrapy settings file and update fields to modify user-agents, follow robots rules, etc. Scrapy uses Protego as the default parser, and by adding our new directives in Protego file, we can direct Scrapy to follow these new AI directives. \n Below is a sample script we have written to extract title and response data from a web application page using Scrapy. \n \n Let's assume you want to use F5 Distributed Cloud WAF to control the generation of the robots.txt file. To enable F5 XC to respond with the robots.txt file, we will create a Direct Response Route, which is a way to specify the response F5 XC will send back to the client. In this case, we will specify the contents of the robots.txt file and add the new disallow ai training directive. \n So let’s configure the load balancer route configuration as shown below \n \n Note the second route, which is used for accessing all other web resources. First route is used to respond data for the robots.txt file content to allow or disallow AI Training as part of direct response body. \n \n Next we will run the crawler with default value of not obeying robots file and we can access the website content. \n If we change the robots directives to obey and rerun the crawler, we can see the request is restricted. We can also modify the robots directives to allow data to be scraped for AI training purposes. We can do this by changing DisallowAITraining directive to AllowAITraining . \n \n Second Solution \n Crawlers, which are not supporting new AI directives but still obeying RFC 9309 can be mitigated using Disallow directive in response. \n \n Modify the load balancer configuration of Direct Response Route as shown above and rerun the script. \n We can validate request will download or blocked as per robots obey field. \n \n Third Solution \n This solution focuses on bots and scraping tools, which don’t obey robots directives. For example, let’s assume we know the User-Agent header and we only want to block this specific scraping tool. To test this scenario, update the user-agent field in the settings. \n \n We can configure F5 XC with a client blocking rule for this specific User-Agent header value as shown below. \n \n Rerunning the script after these changes shows that the request is blocked and F5 XC Security logs confirm the request is blocked because of the client blocking rule. \n \n \n Fourth solution \n This solution covers blocking entire bot categories using F5 WAF, which can block all suspicious bots. First we will create a WAF with bot suspicious action configured field set to Block and enable this WAF on your load balancer as below. \n \n If we rerun the same script, the request is blocked by XC WAF. Security logs show the request was blocked because it was identified as coming from a suspicious bot. \n \n \n Fifth Solution \n We can also create a service policy with combination of TLS fingerprint and User-Agent header to block bots that try to masquerade their User-Agent header by pretending to be a legitimate client. \n First let's go through the Security Analytics logs, examine the JSON view and copy the fingerprint as shown below for the bot which needs to be blocked. \n \n Next, we will configure a service policy on Load Balancer with 2 rules. \n One rule for blocking the requests matching the fingerprint and a second rule to allow all other requests. \n \n If needed, we can also add a combination of user-agents, paths, etc. \n \n We will save the changes and rerun the script. We can see request is blocked and the F5 XC event logs show the request is blocked because of our service policy. \n \n \n Conclusion: \n In this article, we have covered an introduction to AI Scraping bots. Later, we showed different ways to block AI Scraping bots using F5 Distributed Cloud WAF. \n \n Video Link: \n 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and MinIO: AI Data Delivery for the hybrid enterprise","conversation":{"__ref":"Conversation:conversation:342423"},"id":"message:342423","entityType":"TKB_ARTICLE","eventPath":"category:Articles/community:zihoc95639board:TechnicalArticles/message:342423","revisionNum":7,"uid":342423,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:195347"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":144},"postTime":"2025-07-17T04:00:00.033-07:00","lastPublishTime":"2025-07-17T04:00:00.033-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction \n Modern application architectures demand solutions that not only handle exponential data growth but also enable innovation and drive business results. As AI/ML workloads take center stage in industries ranging from healthcare to finance, application designers are increasingly turning to S3-compliant object storage because of its ability to provide scalable management of unstructured data. Whether it’s for ingesting massive datasets, running iterative training models, or delivering high-throughput predictions, S3-compatible storage systems play a foundational role in supporting these advanced data pipelines. \n MinIO has emerged as a leader in this space, offering high-performance, S3-compatible object storage built for modern-scale applications. MinIO is designed to easily work with AI/ML workflows. It is lightweight and cloud-based, so it is a good choice for businesses that are building infrastructure to support innovation. From storing petabyte-scale datasets to providing the performance needed for real-time AI pipelines, MinIO delivers the reliability and speed required for data-intensive work. \n While S3-compliant storage like MinIO forms the backbone of data workflows, robust traffic management and application delivery capabilities are essential for ensuring continuous availability, secure pipelines, and performance optimization. F5 BIG-IP, with its advanced suite of traffic routing, load balancing, and security tools, complements MinIO by enabling organizations to address these challenges. Together, F5 and MinIO create a resilient, scalable architecture where applications and AI/ML systems can thrive. \n This solution empowers businesses to: \n \n Build secure and highly-available storage pipelines for demanding workloads. \n Ensure fast and reliable delivery of data, even at exascale. \n Simplify and optimize their infrastructure to drive innovation faster. \n \n In this article, we’ll explore how to leverage F5 BIG-IP and MinIO AIStor clusters to enable results-driven application design. Starting with an architecture overview, we’ll cover practical steps to set up BIG-IP to enhance MinIO’s functionality. Along the way, we’ll highlight how this combination supports modern AI/ML workflows and other business-critical applications. \n \n Architecture Overview \n To validate the solution of F5 BIG-IP and MinIO AIStor effectively, this setup incorporates a functional testing environment that simulates real-world behaviors while remaining controlled and repeatable. MinIO’s warp benchmarking tool is used for orchestrating and running tests across the architecture. The addition of benchmarking tools ensures that the functional properties of the stack (traffic management, application-layer security, and object storage performance) are thoroughly evaluated in a way that is reproducible and credible. \n The environment consists of: \n \n A F5 VELOS chassis with BX110 blades, running BIG-IP instances configured using F5’s AS3 extension, for traffic management and security policies using LTM (Local Traffic Manager) and ASM (Application Security Manager). \n A MinIO AIStor cluster consisting of four bare-metal nodes equipped with high-performance NVMe drives, bringing the environment close to real-world customer deployments. \n Three benchmarking nodes for orchestrating and running tests: \n \n One orchestration node directs the worker nodes with benchmark test configuration and aggregates test results. \n Two worker nodes run warp in client mode to simulate workloads against the MinIO cluster. \n \n \n Warp Benchmarking Tool \n The warp benchmarking tool (https://github.com/minio/warp) from MinIO is designed to simulate real-world S3 workloads while also generating measurable metrics about the testing environment. In this architecture: \n \n A central orchestration node is used to coordinate the benchmarking process. This ensures that each test is consistent and runs under comparable conditions. \n Two worker nodes running warp in client mode send simulated traffic to the F5 BIG-IP virtual server. These nodes act as workload generators, allowing for the simulation of read-heavy, write-heavy, or mixed object storage exercises. \n Warp’s distributed design enables the scaling of workload generation, ensuring that the MinIO backend is tested under real-world-like conditions. \n \n This three-node configuration ensures that benchmarking tests are distributed effectively. It also provides insights into object storage behavior, traffic management, and the impact of security enforcement in the environment. \n \n Traffic Management and Security with BIG-IP \n At the center of this setup is the F5 VELOS chassis, running BIG-IP instances configured to handle both traffic management (LTM) and application-layer security (ASM). The addition of ASM (Application Security Manager) ensures that the MinIO cluster is protected from malicious or malformed requests while maintaining uninterrupted service for legitimate traffic. \n Key functions of BIG-IP in this architecture include: \n \n Load Balancing: \n \n Avoid overloading specific MinIO nodes by using adaptive algorithms, ensuring even traffic distribution, and preventing bottlenecks and hotspots. \n Apply advanced load balancing methods like least connections, dynamic ratio, and least response time. These methods intelligently account for backend load and performance in real time, ensuring reliable and efficient resource utilization. \n \n SSL/TLS Termination: \n \n Terminate SSL/TLS traffic to offload encryption workloads from backend clients. Re-encryption is optionally enabled for secure communication to MinIO nodes, depending on performance and security requirements. \n \n Health Monitoring: \n \n Intelligently performs continuous monitoring of the availability and health of the backend MinIO nodes. It reroutes traffic away from unhealthy nodes as necessary and restores traffic as service is restored. \n \n Application-Layer Security: \n \n Protect the environment via Web Application Firewall (WAF) policies that block malicious traffic, including injection attacks, malformed API calls, and DDoS-style app-layer threats. \n \n \n BIG-IP acts as the gateway for all requests coming from S3 clients, ensuring that security, health checks, and traffic policies are all applied before requests reach the MinIO nodes. \n Traffic Flow Through the Full Architecture \n The test traffic flows through several components in this architecture, with BIG-IP and warp playing vital roles in managing and generating requests, respectively: \n \n Benchmark Orchestration: \n The warp orchestration node initiates tests and distributes workload configurations to the worker nodes. The warp orchestration node also aggregates test data results from the worker nodes. \n Warp manages benchmarking scenarios, such as read-heavy, write-heavy, or mixed traffic patterns, targeting the MinIO storage cluster. \n Simulated Traffic from Worker Nodes: \n Two worker nodes, running warp in client mode, generate S3-compatible traffic such as object PUT, GET, DELETE, or STAT requests. These requests are transmitted through the BIG-IP virtual server. \n The load generation simulates the kind of requests an AI/ML pipeline or data-driven application might send under production conditions. \n BIG-IP Processing: \n Requests from the worker nodes are received by BIG-IP, where they are subjected to: \n \n Traffic Control: LTM distributes the traffic among the four MinIO nodes while handling SSL termination and monitoring node health. \n Security Controls: ASM WAF policies inspect requests for signs of application-layer threats. Only safe, valid traffic is routed to the MinIO environment. \n \n \n Environment Configuration \n Prerequisites \n \n BIG-IP (physical or virtual) \n Hosts for the MinIO cluster, including configured operating systems (and scheduling systems if optionally selected) \n Hosts for the warp worker nodes and warp orchestration node, including configured operating systems \n All required networking gear to connect the BIG-IP and the nodes \n A copy of the AS3 template at https://github.com/f5businessdevelopment/terraform-kvm-minio/blob/main/as3manualtemplate.json \n A copy of the warp configuration file at https://github.com/minio/warp/blob/master/yml-samples/mixed.yml \n \n Step 1: Set up MinIO Cluster \n Follow MinIO’s install instructions at https://min.io/docs/minio/linux/index.html \n The link is for a Linux deployment but choose the deployment target that’s appropriate for your environment. \n Record the addresses and ports of the MinIO consoles and APIs configured in this step for use as input to the next steps. \n Step 2: Configure F5 BIG-IP for Traffic Management and Security \n Following the steps documented in https://github.com/f5businessdevelopment/terraform-kvm-minio/blob/main/MANUALAS3.md and using the template file downloaded from GitHub, create and apply an AS3 declaration to configure your BIG-IP. \n Step 3: Deploy and Configure MinIO Warp for Benchmarking \n Retrieve API Access and Secret keys \n \n Log into your MinIO Cluster and click the 'Access' icon \n \n Once in 'Access', click the 'Access Keys' button \n \n In ‘Access Keys’, click the ‘Create Access Keys’ button and follow the steps to create and record your access and secret key values. \n Update warp key and secret value \n \n In your warp configuration file, find the access-key and secret-key fields and update the values with those you recorded in the previous step. \n Update warp client addresses \n \n In your warp configuration file, find the warp-client field and update the value with the addresses of the worker nodes. \n Update warp s3 host address \n \n In your warp configuration file, find the host field and update the value with the address and port of the VIP listener on the BIG-IP \n Step 4: Verify and Monitor the Environment \n Start the warp on each of the worker nodes with the command \n warp client \n Once the warp clients respond that they are listening on the warp orchestrator node, start the warp benchmark test \n warp run test.yaml \n Replace test.yaml with the name of your configuration file \n \n Summary of Test Results \n Functional tests done in F5’s lab, using the method described above, show how the F5 + MinIO solution works and behaves. These results highlight important considerations that apply to both AI/ML pipelines and data repatriation workflows. This enables organizations to make informed design choices when deploying similar architectures. \n The testing goals were: \n \n Validate that BIG-IP security and traffic management policies function properly with MinIO AIStor in a simulated real-world configuration. \n Compare the impact of various load-balancing, security, and storage strategies to determine best practices. \n \n Test Methodology \n Four test configurations were executed to identify the effects of: \n \n Threads Per Worker: Testing both 1-thread and 20-thread configurations for workload generation. \n Multi-Part GETs and PUTs: Comparing scenarios with and without multi-part requests for better parallelization. \n BIG-IP Profiles: Evaluating Layer 7 (ASM-enabled security) versus Layer 4 (performance-optimized) profiles. \n \n Test Results \n \n Test Configuration \n \n Throughput \n \n Benefits \n \n 20 threads, multi-part, Layer 7 \n \n 28.1 Gbps \n \n Security, High-performance reliability \n \n 20 threads, multi-part, Layer 4 \n \n 81.5 Gbps \n \n High-performance reliability \n \n 1 thread, no multi-part, Layer 7 \n \n 3.7 Gbps \n \n Security, Reliability \n \n 1 thread, no multi-part, Layer 4 \n \n 7.8 Gbps \n \n Reliability \n \n Note: The testing results provide insights into the solution and behavior of this setup, though they are not intended as production performance benchmarks. \n Key Insights \n Multi-Part GETs and PUTs Are Critical for Throughput Optimization: Multi-part operations split objects into smaller parts for parallel processing. This allows the architecture to better utilize MinIO’s distributed storage capabilities and worker thread concurrency. Without multi-part GETs/PUTs, single-threaded configurations experienced severely reduced throughput. \n Recommendation: Ensure multi-part operations are enabled in applications or tools interacting with MinIO when handling large objects or high IOPS workloads. \n Balance Security with Performance: Layer 7 security provided by ASM is essential for sensitive data and workloads that interact with external endpoints. However, it introduces processing overhead. Layer 4 performance profiles, while lacking application-layer security features, deliver significantly higher throughput. \n Recommendation: Choose BIG-IP profiles based on specific workload requirements. For AI/ML data ingest and model training pipelines, consider enabling Layer 4 optimization during bulk read/write phases. For workloads requiring external access or high-security standards, deploy Layer 7 profiles. In some cases, consider horizontal scaling of load balancing and object storage tiers to add throughput capacity. \n Threads Per Worker Impact Throughput: Scaling up threads at the worker level significantly increased throughput in the lab environment. This demonstrates the importance of concurrency for demanding workloads. \n Recommendation: Optimize S3 client configurations for higher connection counts where workloads permit, particularly when performing bulk data transfers or operationally intensive reads. \n \n Example Use Cases \n Use Case #1: AI/ML Pipeline \n AI and machine learning pipelines rely heavily on storage systems that can ingest, process, and retrieve vast amounts of data quickly and securely. MinIO provides the scalability and performance needed for storage, while F5 BIG-IP ensures secure, optimized data delivery. \n Pipeline Workflow \n An enterprise running a typical AI/ML pipeline might include the following stages: \n \n Data Ingestion: \n \n Large datasets (e.g., images, logs, training corpora) are collected from various sources and stored within the MinIO cluster using PUT operations. \n \n Model Training: \n \n Data scientists iterate on AI models using the stored training datasets. These training processes generate frequent GET requests to retrieve slices of the dataset from the MinIO cluster. \n \n Model Validation and Inference: \n \n During validation, the pipeline accesses specific test data objects stored in the cluster. For deployed models, inference may require low-latency reads to make predictions in real time. \n \n \n How F5 and MinIO Support the Workflow \n This combined architecture enables the pipeline by: \n \n Ensuring Consistent Availability: \n \n BIG-IP distributes PUT and GET requests across the four nodes in the MinIO cluster using intelligent load balancing. \n With health monitoring, BIG-IP proactively reroutes traffic away from any node experiencing issues, preventing delays in training or inference. \n \n Optimizing Performance: \n \n NVMe-backed storage in MinIO ensures fast read and write speeds. Together with BIG-IP's traffic management, the architecture delivers reliable throughput for iterative model training and inference. \n \n Securing End-to-End Communication: \n \n ASM protects the MinIO storage APIs from malicious requests, including malformed API calls. At the same time, SSL/TLS termination secures communications between AI/ML applications and the MinIO backend. \n \n \n Use Case #2: Enterprise Data Repatriation \n Organizations increasingly seek to repatriate data from public clouds to on-premises environments. Repatriation is often driven by the need to reduce cloud storage costs, regain control over sensitive information, or improve performance by leveraging local infrastructure. This solution supports these workflows by pairing MinIO’s high-performance object storage with BIG-IP’s secure and scalable traffic management. \n Repatriation Workflow \n A typical enterprise data repatriation workflow may look like this: \n \n Bulk Data Migration: \n \n Data stored in public cloud object storage systems (e.g., AWS S3, Google Cloud Storage) is transferred to the MinIO cluster running on on-premises infrastructure using tools like MinIO Gateway or custom migration scripts. \n \n Policy Enforcement: \n \n Once migrated, BIG-IP ensures that access to the MinIO cluster is secured, with ASM enforcing WAF policies to protect sensitive data during local storage operations. \n \n Ongoing Storage Optimization: \n \n The migrated data is integrated into workflows like backup and archival, analytics, or data access for internal applications. Local NVMe drives in the MinIO cluster reduce latency compared to cloud solutions. \n \n \n How F5 and MinIO Support the Workflow \n This architecture facilitates the repatriation process by: \n \n Secure Migration: \n \n MinIO Gateway, combined with SSL/TLS termination on BIG-IP, allows data to be transferred securely from public cloud object storage services to the MinIO cluster. \n ASM protects endpoints from exploitation during bulk uploads. \n \n Cost Efficiency and Performance: \n \n On-premises MinIO storage eliminates expensive cloud storage costs while providing faster access to locally stored data. \n NVMe-backed nodes ensure that repatriated data can be rapidly retrieved for internal applications. \n \n Scalable and Secure Access: \n \n BIG-IP provides secure access control to the MinIO cluster, ensuring only authorized users or applications can use the repatriated data. \n Health monitoring prevents disruptions in workflows by proactively managing node unavailability. \n \n \n The F5 and MinIO Advantage \n Both use cases reflect the flexibility and power of combining F5 and MinIO: \n \n AI/ML Pipeline: Supports data-heavy applications and iterative processes through secure, high-performance storage. \n Data Repatriation: Empowers organizations to reduce costs while enabling seamless local storage integration. \n \n These examples provide adaptable templates for leveraging F5 and MinIO to solve problems relevant to enterprises across various industries, including finance, healthcare, agriculture, and manufacturing. \n Conclusion \n The collaboration of F5 BIG-IP and MinIO provides a high-performance, secure, and scalable architecture for modern data-driven use cases such as AI/ML pipelines and enterprise data repatriation. Testing in the lab environment validates the functionality of this solution, while highlighting opportunities for throughput optimization via configuration tuning. \n To bring these insights to your environment: \n \n Test multi-part configurations using tools like MinIO warp benchmark or production applications. \n Match BIG-IP profiles (Layer 4 or Layer 7) with the specific priorities of your workloads. \n Use these findings as a baseline while performing further functional or performance testing in your enterprise. \n \n The flexibility of this architecture allows organizations to push the boundaries of innovation while securing critical workloads at scale. Whether driving new AI/ML pipelines or reducing costs in repatriation workflows, the F5 + MinIO solution is well-equipped to meet the demands of modern enterprises. \n Further Content \n For more information about F5's partnership with MinIO, consider looking at the informative overview by buulam on DevCentral's YouTube channel. \n \n We also have the steps outlined in this video. \n \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"19373","kudosSumWeight":2,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDI0MjMtV0I5ckdv?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDI0MjMtS0VtVGxa?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDI0MjMtbGQxOGEy?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDI0MjMtUmtuM1Vt?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDU","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDI0MjMtb2dWcXo3?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDY","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDI0MjMtR2xsMmlP?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDc","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDI0MjMtTEs2Y1Z5?revision=7\"}"}}],"totalCount":7,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3lvdXR1LmJlL1pXT3BPQXVPeVpBLzE3NTI2MTY2NTU1NTR8MHwyNTsyNXx8","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://youtu.be/ZWOpOAuOyZA/1752616655554","thumbnail":"https://i.ytimg.com/vi/ZWOpOAuOyZA/hqdefault.jpg","uploading":false,"height":240,"width":320,"title":null},"videoAssociationType":"INLINE_BODY"}},{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3d3dy55b3V0dWJlLmNvbS93YXRjaD92PXZsWG04V09ITXpNLzE3NTI3MzI5OTg5NDV8MXwyNTsyNXx8","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://www.youtube.com/watch?v=vlXm8WOHMzM/1752732998945","thumbnail":"https://i.ytimg.com/vi/vlXm8WOHMzM/hqdefault.jpg","uploading":false,"height":240,"width":320,"title":null},"videoAssociationType":"INLINE_BODY"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:341072":{"__typename":"Conversation","id":"conversation:341072","topic":{"__typename":"TkbTopicMessage","uid":341072},"lastPostingActivityTime":"2025-07-16T04:00:00.048-07:00","solved":false},"User:user:305638":{"__typename":"User","uid":305638,"login":"Valentin_Tobi","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://community.f5.com/t5/s/zihoc95639/images/dS0zMDU2MzgtMjE5NThpMzEwNzRGNTRCM0ZCREU4Rg"},"id":"user:305638"},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDEwNzItc0o0SmQ3?revision=6\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDEwNzItc0o0SmQ3?revision=6","title":"AIGW_ELK.jpeg","associationType":"BODY","width":5446,"height":3302,"altText":""},"TkbTopicMessage:message:341072":{"__typename":"TkbTopicMessage","subject":"Introducing F5 AI Gateway","conversation":{"__ref":"Conversation:conversation:341072"},"id":"message:341072","entityType":"TKB_ARTICLE","eventPath":"category:Articles/community:zihoc95639board:TechnicalArticles/message:341072","revisionNum":6,"uid":341072,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:305638"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" This article introduces F5 AI Gateway and shows a demo of the main use cases: route abstraction, context routing, transaction logging for audit purposes and security controls tailored for GenAI traffic. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":585},"postTime":"2025-05-15T05:00:00.027-07:00","lastPublishTime":"2025-07-16T04:00:00.048-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" This article is a beginner’s guide to F5 AI Gateway, briefly introducing its main use cases and features. We will start with the processors, since they implement some of the most important features of F5 AI Gateway. \n The 4 processors that come out-of-the-box with AI Gateway are: \n \n prompt-injection: detecting and blocking prompt-injection attacks with support for English language \n system-prompt: allowing to set tighter guardrails that improve the security posture of the downstream LLM \n language-id: predicting the language used in the prompt and supporting sophisticated context-based routing \n repetition-detect: detecting and blocking attacks that rely on repeating strings in the input prompt \n \n Of course, custom processors can be added, using the available SDK. \n Supported main use cases \n Route Abstraction \n AI Gateway can decouple the client connection from the downstream LLM one. It presents a single endpoint for client connections, regardless of the LLM that is ultimately servicing the request. This allows for excellent code portability, as the Generative AI application developers don't need to change the GenAI app code every time the downstream LLM changes. AI Gateway transparently performs this task. \n Context Routing \n Being able to make routing decisions based on the prompt context is a powerful feature that allows AI Gateway to support complex policies easily. AI Gateway will look at certain traffic characteristics, for example the HTTP headers, the JWT claims, or the tags generated by the AI Gateway itself, when making a decision to route the input prompt to a downstream LLM. \n Telemetry & Observability \n Audit logging is critical in running GenAI applications in production, both from regulatory and operations perspectives. It facilitates troubleshooting in a non-deterministic environment and provides an audit trail that matches the input prompt and the LLM response. AI Gateway can record transactions for auditing. It records not only the input prompt and the LLM response, but also the AI Gateway’s internal stages and processors’ states. \n One of the many ways to instrument AI Gateway for telemetry & observability can be seen in the example below, where MinIO integration is being used for transaction storage, while OpenTelemetry and ELK are being used for the export of logs, metrics and traces. \n \n More information about this setup and steps to reproduce it can be found in the F5 AI Use Case Examples GitHub repository. \n Security \n Security is core to AI Gateway, and the security features that come out-of-the-box act at Layer 8, understanding the prompt context, as opposed to regular API Gateways that cannot understand the semantics of the submitted prompt. \n \n F5 AI Gateway in action \n \n \n Conclusion \n F5 AI Gateway natively understands the prompt context, enabling security controls tailored for GenAI traffic, route abstraction, and context routing, as well as supporting transaction logging for audit purposes. \n \n Resources \n F5 AI Gateway product page \n F5 AI Gateway product documentation \n F5 AI Use Case Examples GitHub Repository ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"3214","kudosSumWeight":6,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDEwNzItc0o0SmQ3?revision=6\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3d3dy55b3V0dWJlLmNvbS93YXRjaD92PVY0d2hSZkU2bWl3LzE3NDYwMzg3MjA0ODB8MHwyNTsyNXx8","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://www.youtube.com/watch?v=V4whRfE6miw/1746038720480","thumbnail":"https://i.ytimg.com/vi/V4whRfE6miw/hqdefault.jpg","uploading":false,"height":240,"width":320,"title":null},"videoAssociationType":"INLINE_BODY"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:341961":{"__typename":"Conversation","id":"conversation:341961","topic":{"__typename":"TkbTopicMessage","uid":341961},"lastPostingActivityTime":"2025-06-24T08:00:00.037-07:00","solved":false},"User:user:172159":{"__typename":"User","uid":172159,"login":"Leon_Seng","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://community.f5.com/t5/s/zihoc95639/images/dS0xNzIxNTktMjA5NDNpQTcyQThGQzE5NUQ0MzZFMg"},"id":"user:172159"},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE5NjEtdjVNU1BM?revision=6\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE5NjEtdjVNU1BM?revision=6","title":"image.png","associationType":"BODY","width":1420,"height":709,"altText":""},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE5NjEtRTBlN3RH?revision=6\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE5NjEtRTBlN3RH?revision=6","title":"image.png","associationType":"BODY","width":1267,"height":826,"altText":""},"TkbTopicMessage:message:341961":{"__typename":"TkbTopicMessage","subject":"MCP Session Affinity With F5 NGINX Plus","conversation":{"__ref":"Conversation:conversation:341961"},"id":"message:341961","entityType":"TKB_ARTICLE","eventPath":"category:Articles/community:zihoc95639board:TechnicalArticles/message:341961","revisionNum":6,"uid":341961,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:172159"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Scaling out MCP servers presents a challenge with session management, which can be addressed by NGINX Plus' session tracking ability to provide session affinity. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":189},"postTime":"2025-06-24T08:00:00.037-07:00","lastPublishTime":"2025-06-24T08:00:00.037-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" As MCP (Model Context Protocol) adoption grows, ensuring high availability and performance to support a large number of clients concurrently becomes critical. \n The most direct way to address this is simply to deploy more MCP servers in the backend, sitting behind a load balancer to distribute the load. With the latest MCP specification (2025-03-26) introducing Streamable HTTP , the communications between MCP clients and servers are predominantly a series of exchanges via HTTP POST requests and responses. Such a stateless nature makes load balancing straightforward for many of the use cases where MCP is treated as a wrapper for REST API. \n \n MCP Session Management Woe \n However, there are cases where persistent client-server relationships are required, which is why the MCP specification addresses include a section on session management: \n \n During the initialization handshake, the MCP server issues a session identifier via the mcp-session-id header in the response. \n All subsequent requests from the client must include this session ID in the request headers. \n MCP servers validate incoming session IDs and reject requests with unknown or invalid identifiers. \n \n This does introduce another problem: when a basic load balancer is deployed in front of the MCP servers, session continuity will be broken if POST requests after the initial setup are sent to another MCP server that has no knowledge of the session ID: \n \n Thus, some form of session affinity, or session persistence is required. \n \n MCP Session Affinity Options \n Most load balancers support session stickiness through the following mechanisms, but there are some downsides: \n \n Session cookies - though simple, do not work well for programmatic access via API, or any non-browser-based client. \n \n \n Client/source IP and port - again, works well if clients are directly connecting to the server, but the reality is many clients sit behind common proxies or end up sharing NAT-ed IPs, creating uneven load distribution towards the server. \n \n A more intelligent session tracking solution is required, which is where F5 NGINX Plus comes in. \n NGINX Plus Enabling MCP Session Affinity \n Leveraging sticky learn directive, NGINX Plus can monitor the session ID issued by MCP servers and dynamically maintain session-to-server mappings: \n \n NGINX Plus extracts the session ID from the mcp-session-id header in the Initialization response from the MCP server. \n NGINX Plus records the session ID and its corresponding upstream server in its internal session store. \n When subsequent requests arrive, NGINX Plus looks up the session ID in the request header and routes traffic to the corresponding MCP server, as mapped in the internal session store \n \n \n The setup enables session affinity without requiring changes to client-side code, allowing MCP servers to be scaled out transparently. \n Below is a sample F5 NGINX configuration to achieve the outcome: \n http {\n upstream mcp-server {\n sticky learn\n create=$sent_http_mcp_session_id\n lookup=$http_mcp_session_id\n zone=client_sessions:1m;\n zone backend 64k;\n\n server server-a:8001;\n server server-b:8002;\n }\n\n server {\n listen 8000;\n\n location /mcp/ {\n proxy_pass http://mcp-server/mcp/;\n }\n }\n} \n Other considerations that can be made on NGINX to further improve the architecture include: \n \n Improving system uptime with NGINX's health check capabilities, ensuring MCP servers that are healthy always handle client requests, \n Protecting MCP servers against attacks with F5 NGINX App Protect WAF and DoS, \n \n to name a few. \n \n Conclusion \n NGINX Plus's configurable session tracking capability enables a large number of clients to be served concurrently by multiple MCP servers, while maintaining client-server mappings, ultimately enabling organizations to deploy scalable MCP server architecture and address use cases that require stateful sessions. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"3977","kudosSumWeight":1,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE5NjEtdjVNU1BM?revision=6\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE5NjEtRTBlN3RH?revision=6\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:341697":{"__typename":"Conversation","id":"conversation:341697","topic":{"__typename":"TkbTopicMessage","uid":341697},"lastPostingActivityTime":"2025-06-09T08:00:00.033-07:00","solved":false},"User:user:173018":{"__typename":"User","uid":173018,"login":"AubreyKingF5","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://community.f5.com/t5/s/zihoc95639/images/dS0xNzMwMTgtM1pXcDFQ?image-coordinates=0%2C0%2C2316%2C2315"},"id":"user:173018"},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE2OTctRkpNeXRv?revision=7\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE2OTctRkpNeXRv?revision=7","title":"Screenshot 2025-06-03 at 10.05.02 AM.png","associationType":"BODY","width":1361,"height":861,"altText":""},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE2OTctcVRQaTV0?revision=7\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE2OTctcVRQaTV0?revision=7","title":"Screenshot 2025-06-03 at 10.10.05 AM.png","associationType":"BODY","width":836,"height":525,"altText":""},"TkbTopicMessage:message:341697":{"__typename":"TkbTopicMessage","subject":"Getting Started With n8n For AI Automation","conversation":{"__ref":"Conversation:conversation:341697"},"id":"message:341697","entityType":"TKB_ARTICLE","eventPath":"category:Articles/community:zihoc95639board:TechnicalArticles/message:341697","revisionNum":7,"uid":341697,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:173018"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":68},"postTime":"2025-06-03T10:14:53.008-07:00","lastPublishTime":"2025-06-09T08:00:00.033-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" First, what is n8n? \n If you're not familiar with n8n yet, it's a workflow automation utility that allows us to use nodes to connect services quite easily. It's been the subject of quite a bit of Artificial Intelligence hype because it helps you construct AI Agents. \n I'm going to be diving more into n8n, what it can do with AI, and how to use our AI Gateway to defend against some difficult AI threats today. My hope is that you can use this in your own labs to prove out some of these things in your environment. \n Here's an example of how someone could use Ollama to control multiple Twitter accounts, for instance: \n \n \n How do you install it? \n Well... It’s all node, so the best way to install it in any environment is to ensure you have node version 22 (on Mac, homebrew install node@22) installed on your machine, as well as nvm (again, for mac, homebrew install nvm) and then do an npm install -g n8n. Done! Really...That simple. \n \n \n How much does it cost? \n While there is support and expanded functionality for paid subscribers, there is also a community edition that I have used here and it's free. \n \n How to license: \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1168","kudosSumWeight":0,"repliesCount":0,"readOnly":true,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE2OTctRkpNeXRv?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE2OTctcVRQaTV0?revision=7\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3lvdXR1LmJlL2tDN0ljcndWZDNRLzE3NDg5NTk5NzI1Mjl8MHwyNTsyNXx8","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://youtu.be/kC7IcrwVd3Q/1748959972529","thumbnail":"https://i.ytimg.com/vi/kC7IcrwVd3Q/hqdefault.jpg","uploading":false,"height":240,"width":320,"title":null},"videoAssociationType":"INLINE_BODY"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:341721":{"__typename":"Conversation","id":"conversation:341721","topic":{"__typename":"TkbTopicMessage","uid":341721},"lastPostingActivityTime":"2025-06-09T08:00:00.032-07:00","solved":false},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE3MjEtRkpNeXRv?revision=1\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE3MjEtRkpNeXRv?revision=1","title":"Screenshot 2025-06-03 at 10.05.02 AM.png","associationType":"BODY","width":1361,"height":861,"altText":""},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE3MjEtcVRQaTV0?revision=1\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE3MjEtcVRQaTV0?revision=1","title":"Screenshot 2025-06-03 at 10.10.05 AM.png","associationType":"BODY","width":836,"height":525,"altText":""},"TkbTopicMessage:message:341721":{"__typename":"TkbTopicMessage","subject":"Getting Started With n8n For AI Automation","conversation":{"__ref":"Conversation:conversation:341721"},"id":"message:341721","entityType":"TKB_ARTICLE","eventPath":"category:Articles/community:zihoc95639board:TechnicalArticles/message:341721","revisionNum":1,"uid":341721,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:173018"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":299},"postTime":"2025-06-09T08:00:00.032-07:00","lastPublishTime":"2025-06-09T08:00:00.032-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" First, what is n8n? \n If you're not familiar with n8n yet, it's a workflow automation utility that allows us to use nodes to connect services quite easily. It's been the subject of quite a bit of Artificial Intelligence hype because it helps you construct AI Agents. \n I'm going to be diving more into n8n, what it can do with AI, and how to use our AI Gateway to defend against some difficult AI threats today. My hope is that you can use this in your own labs to prove out some of these things in your environment. \n Here's an example of how someone could use Ollama to control multiple Twitter accounts, for instance: \n \n \n How do you install it? \n Well... It’s all node, so the best way to install it in any environment is to ensure you have node version 22 (on Mac, homebrew install node@22) installed on your machine, as well as nvm (again, for mac, homebrew install nvm) and then do an npm install -g n8n. Done! Really...That simple. \n \n \n How much does it cost? \n While there is support and expanded functionality for paid subscribers, there is also a community edition that I have used here and it's free. \n \n How to license: \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1168","kudosSumWeight":5,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE3MjEtRkpNeXRv?revision=1\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDE3MjEtcVRQaTV0?revision=1\"}"}}],"totalCount":2,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3lvdXR1LmJlL2tDN0ljcndWZDNRLzE3NDg5NTk5NzI1Mjl8MHwyNTsyNXx8","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://youtu.be/kC7IcrwVd3Q/1748959972529","thumbnail":"https://i.ytimg.com/vi/kC7IcrwVd3Q/hqdefault.jpg","uploading":false,"height":240,"width":320,"title":null},"videoAssociationType":"INLINE_BODY"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:341078":{"__typename":"Conversation","id":"conversation:341078","topic":{"__typename":"TkbTopicMessage","uid":341078},"lastPostingActivityTime":"2025-05-25T21:23:47.370-07:00","solved":false},"TkbTopicMessage:message:341078":{"__typename":"TkbTopicMessage","subject":"Introducing AI Assistant for F5 Distributed Cloud, F5 NGINX One and BIG-IP","conversation":{"__ref":"Conversation:conversation:341078"},"id":"message:341078","entityType":"TKB_ARTICLE","eventPath":"category:Articles/community:zihoc95639board:TechnicalArticles/message:341078","revisionNum":3,"uid":341078,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:305638"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" This article is an introduction to AI Assistant and shows how it improves SecOps and NetOps speed across all F5 platforms (Distributed Cloud, NGINX One and BIG-IP) , by solving the complexities around configuration, analytics, log interpretation and scripting. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":415},"postTime":"2025-05-20T08:00:00.029-07:00","lastPublishTime":"2025-05-20T08:00:00.029-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" F5 empowers the SecOps and NetOps teams by simplifying operations and allowing users to better exploit the wealth of information at their disposal across all F5 platforms: F5 Distributed Cloud, F5 NGINX One, and BIG-IP. \n Starting with F5 Distributed Cloud Web App and API protection solution, it is important to note the main challenges SecOps teams are facing in maintaining complex WAAP solutions: \n \n Understanding the security posture of every application protected by the WAAP \n Availability of customizable views of security analytics for each particular application, as the threats facing them might not be the same \n Logs and security events overload, making the availability of an \"expert\" tool almost mandatory \n Lack of automatically-generated suggestions for improving the security posture, as this reduces the amount of time SecOps teams have to spend on research, vendor-specific configuration \n \n Similarly, NetOps teams are challenged by growing estates, increasingly fragmented environments using different technologies, and sheer complexity. \n Distributed Cloud AI Assistant helps to reduce these complexities and enable the users to quickly see the state of their resources, understand the security events and overall security posture, and also provides users with suggestions on how to further improve the security posture of their estate. \n NGINX One is another product that boosts the efficiency and productivity of DevSecOps with the help of NGINX’s AI Assistant, focusing on explaining complex configurations and offering a guided configuration mode that speeds up development tremendously. \n The upcoming F5 BIG-IP AI Assistant will help with one of the most challenging tasks when configuring the BIG-IP platform: creating and maintaining iRules. AI Assistant will help BIG-IP administrators by analyzing, generating, and optimizing iRules, shielding the user from the complexities associated with traffic management scripting. \n AI Assistant at work across all these platforms: \n \n Conclusion: \n F5 AI Assistant hugely improves SecOps and NetOps speed across all F5 platforms, by solving the complexities around configuration, analytics, log interpretation, and scripting. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2218","kudosSumWeight":3,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[],"totalCount":0,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3d3dy55b3V0dWJlLmNvbS93YXRjaD92PTZXeGFXZkF3OUE4LzE3NDYwNTgyNDY5MTF8MHwyNTsyNXx8","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://www.youtube.com/watch?v=6WxaWfAw9A8/1746058246911","thumbnail":"https://i.ytimg.com/vi/6WxaWfAw9A8/hqdefault.jpg","uploading":false,"height":240,"width":320,"title":null},"videoAssociationType":"INLINE_BODY"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:341283":{"__typename":"Conversation","id":"conversation:341283","topic":{"__typename":"TkbTopicMessage","uid":341283},"lastPostingActivityTime":"2025-05-19T08:00:00.047-07:00","solved":false},"User:user:171064":{"__typename":"User","uid":171064,"login":"Foo-Bang_Chan","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://community.f5.com/t5/s/zihoc95639/images/dS0xNzEwNjQtSzhEcmtx?image-coordinates=62%2C0%2C1665%2C1603"},"id":"user:171064"},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDEyODMtU3JBRElS?revision=6\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDEyODMtU3JBRElS?revision=6","title":"diagram.png","associationType":"BODY","width":3388,"height":1912,"altText":""},"TkbTopicMessage:message:341283":{"__typename":"TkbTopicMessage","subject":"Scaling and Traffic-Managed Model Context Protocol (MCP) with BIG-IP Next for K8s","conversation":{"__ref":"Conversation:conversation:341283"},"id":"message:341283","entityType":"TKB_ARTICLE","eventPath":"category:Articles/community:zihoc95639board:TechnicalArticles/message:341283","revisionNum":6,"uid":341283,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:171064"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":401},"postTime":"2025-05-19T08:00:00.047-07:00","lastPublishTime":"2025-05-19T08:00:00.047-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction \n As AI models get more advanced, running them at scale—especially in cloud-native environments like Kubernetes—can be tricky. That’s where the Model Context Protocol (MCP) comes in. MCP makes it easier to connect and interact with AI models, but managing all the traffic and scaling these services as demand grows is a whole different challenge. \n In this article and demo video, I will show how F5's BIG-IP Next for K8s (BNK), a powerful cloud native traffic management platform from F5 can solve that and keep things running smoothly and scale your MCP services as needed. \n \n Model Context Protocol (MCP) in a nutshell. \n There were many articles explaining what is MCP on the internet. Please refer to those in details. \n In a nutshell, it is a standard framework or specification to securely connect AI apps to your critical data, tools, and workflow. The specification allow \n \n Tracking of context across multiple conversation \n Tool integration — model call external tools \n Share memory/state — remember information. \n \n MCP’s \"glue\" model to tools through a universal interface \"USB-C for AI\" \n \n What EXACTLY does MCP solve? \n MCP addresses many challenges in the AI ecosystem. I believe two key challenges it solve \n Complexities of integrating AI Model (LLM) with external sources and tools \n \n By standardization with a universal connector (\"USB-C for AI\") \n Everyone build \"USB-C for AI\" port so that it can be easily plug in each other \n Interoperability. \n \n Security with external integration \n \n Framework to establish secure connection \n Managing permission and authorization. \n \n \n What is BIG-IP’s Next for K8s (BNK)? \n BNK is F5 modernized version of the well-known Big-IP platform, redesigned to work seamlessly in cloud-native environments like Kubernetes. It is a scalable networking and security solution for ingress and egress traffic control. It builds on decades of F5's leadership in application delivery and security. It powers Kubernetes networking for today's complex workloads. BNK can be deployed on X86 architecture or ARM architecture - Nvidia Data Processing Unit (DPU) \n \n Lets see how F5's BNK scale and traffic managed an AIOps ecosystem. \n DEMO \n Architecture Setup \n \n \n Video \n \n \n \n Key Takeaways \n \n BIGIP Next for K8s, the backbone of the MCP architecture \n Technology built on decades of market-leading application delivery controller technology \n Secure, Deliver, and Optimize your AI infrastructure \n Provides deep insight through observability and visibility of your MCP traffic. \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"2610","kudosSumWeight":1,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuNHwyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDEyODMtU3JBRElS?revision=6\"}"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}},"videos":{"__typename":"VideoConnection","edges":[{"__typename":"VideoEdge","cursor":"MHxodHRwczovL3lvdXR1LmJlL1UyeS01WmVScDIwP3NpPXFOTVhtZmd2ZlV2RzBidkkvMTc0NzE3MjcwNjUxN3wwfDI1OzI1fHw","node":{"__typename":"AssociatedVideo","videoTag":{"__typename":"VideoTag","vid":"https://youtu.be/U2y-5ZeRp20?si=qNMXmfgvfUvG0bvI/1747172706517","thumbnail":"https://i.ytimg.com/vi/U2y-5ZeRp20/hqdefault.jpg","uploading":false,"height":240,"width":320,"title":null},"videoAssociationType":"INLINE_BODY"}}],"totalCount":1,"pageInfo":{"__typename":"PageInfo","hasNextPage":false,"endCursor":null,"hasPreviousPage":false,"startCursor":null}}},"Conversation:conversation:341126":{"__typename":"Conversation","id":"conversation:341126","topic":{"__typename":"TkbTopicMessage","uid":341126},"lastPostingActivityTime":"2025-05-16T08:02:32.735-07:00","solved":false},"User:user:326558":{"__typename":"User","uid":326558,"login":"Eric_Ji","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://community.f5.com/t5/s/zihoc95639/images/dS0zMjY1NTgtaDBOSjVu?image-coordinates=0%2C0%2C1110%2C1110"},"id":"user:326558"},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDExMjYtUnU2UWJM?revision=18\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zNDExMjYtUnU2UWJM?revision=18","title":"image.png","associationType":"BODY","width":2041,"height":1129,"altText":""},"TkbTopicMessage:message:341126":{"__typename":"TkbTopicMessage","subject":"F5 AI Gateway to Strengthen LLM Security and Performance in Red Hat OpenShift AI","conversation":{"__ref":"Conversation:conversation:341126"},"id":"message:341126","entityType":"TKB_ARTICLE","eventPath":"category:Articles/community:zihoc95639board:TechnicalArticles/message:341126","revisionNum":18,"uid":341126,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:326558"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":352},"postTime":"2025-05-15T08:06:08.561-07:00","lastPublishTime":"2025-05-16T08:02:32.735-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" In my previous article, we explored how F5 Distributed Cloud (XC) API Security enhances the perimeter of AI model serving in Red Hat OpenShift AI on ROSA by protecting against threats such as DDoS attacks, schema misuse, and malicious bots. \n As organizations move from piloting to scaling GenAI applications, a new layer of complexity arises. Unlike traditional APIs, LLMs process free-form, unstructured inputs and return non-deterministic responses—introducing entirely new attack surfaces. Conventional web or API firewalls fall short in detecting prompt injection, data leakage, or misuse embedded within model interactions. \n Enter F5 AI Gateway—a solution designed to provide real-time, LLM-specific security and optimization within the OpenShift AI environment. \n \n Understanding the AI Gateway \n Recent industry leaders have said that an AI Gateway layer is coming into use. This layer is between clients and LLM endpoints. It will handle dynamic prompt/response patterns, policy enforcement, and auditability. \n Inspired by these patterns, F5 AI Gateway brings enterprise-grade capabilities such as: \n \n Inspecting and Filtering Traffic: Analyzes both client requests and LLM responses to detect and mitigate threats such as prompt injection and sensitive data exposure. \n Implementing Traffic Steering Policies: Directs requests to appropriate LLM backends based on content, optimizing performance and resource utilization. \n Providing Comprehensive Logging: Maintains detailed records of all interactions for audit and compliance purposes. \n Generating Observability Data: Utilizes OpenTelemetry to offer insights into system performance and security events. \n \n These capabilities ensure that AI applications are not only secure but also performant and compliant with organizational policies. \n \n Integrated Architecture for Enhanced Security \n The combined deployment of F5 Distributed Cloud API Security and F5 AI Gateway within Red Hat OpenShift AI creates a layered defense strategy: \n \n F5 Distributed Cloud API Security: Acts as the first line of defense, safeguarding exposed model APIs from external threats. \n F5 AI Gateway: Operates within the OpenShift AI cluster, providing real-time inspection and policy enforcement tailored to LLM traffic. \n \n \n This layered design ensures multi-dimensional defense, aligning with enterprise needs for zero-trust, data governance, and operational resilience. \n \n Key Benefits of F5 AI Gateway \n \n Enhanced Security: Mitigates risks outlined in the OWASP Top 10 for LLM Applications - such as prompt injection (LLM01) - by detecting malicious prompts, enforcing system prompt guardrails, and identifying repetition-based exploits, delivering contextual, Layer 8 protection. \n Performance Optimization: Boosts efficiency through intelligent, context-aware routing and endpoint abstraction, simplifying integration across multiple LLMs. \n Scalability and Flexibility: Supports deployment across various environments, including public cloud, private cloud, and on-premises data centers. \n Comprehensive Observability: Provides detailed metrics and logs through OpenTelemetry, facilitating monitoring and compliance. \n \n \n Conclusion \n The rise of LLM applications requires a new architectural mindset. F5 AI Gateway complements existing security layers by focusing on content-level inspection, traffic governance, and compliance-grade visibility. It is specifically tailored for AI inference traffic. \n When used with Red Hat OpenShift AI, this solution provides not just security, but also trust and control. 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