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Secure AI RAG using F5 Distributed Cloud in Red Hat OpenShift AI and NetApp ONTAP Environment
Introduction Retrieval Augmented Generation (RAG) is a powerful technique that allows Large Language Models (LLMs) to access information beyond their training data. The “R” in RAG refers to the data retrieval process, where the system retrieves relevant information from an external knowledge base based on the input query. Next, the “A” in RAG represents the augmentation of context enrichment, as the system combines the retrieved relevant information and the input query to create a more comprehensive prompt for the LLM. Lastly, the “G” in RAG stands for response generation, where the LLM generates a response with a more contextually accurate output based on the augmented prompt as a result. RAG is becoming increasingly popular in enterprise AI applications due to its ability to provide more accurate and contextually relevant responses to a wide range of queries. However, deploying RAG can introduce complexity due to its components being located in different environments. For instance, the datastore or corpus, which is a collection of data, is typically on-premise for enhanced control over data access and management due to data security, governance, and compliance with regulations within the enterprise. Meanwhile, inference services are often deployed in the cloud for their scalability and cost-effectiveness. In this article, we will discuss how F5 Distributed Cloud can simplify the complexity and securely connect all RAG components seamlessly for enterprise RAG-enabled AI applications deployments. Specifically, we will focus on Network Connect, App Connect, and Web App & API Protection. We will demonstrate how these F5 Distributed Cloud features can be leveraged to secure RAG in collaboration with Red Hat OpenShift AI and NetApp ONTAP. Example Topology F5 Distributed Cloud Network Connect F5 Distributed Cloud Network Connect enables seamless and secure network connectivity across hybrid and multicloud environments. By deploying F5 Distributed Cloud Customer Edge (CE) at site, it allows us to easily establish encrypted site-to-site connectivity across on-premises, multi-cloud, and edge environment. Jensen Huang, CEO of NVIDIA, has said that "Nearly half of the files in the world are stored on-prem on NetApp.”. In our example, enterprise data stores are deployed on NetApp ONTAP in a data center in Seattle managed by organization B (Segment-B: s-gorman-production-segment), while RAG services, including embedding Large Language Model (LLM) and vector database, is deployed on-premise on a Red Hat OpenShift cluster in a data center in California managed by Organization A (Segment-A: jy-ocp). By leveraging F5 Distributed Cloud Network Connect, we can quickly and easily establish a secure connection for seamless and efficient data transfer from the enterprise data stores to RAG services between these two segments only: F5 Distributed Cloud CE can be deployed as a virtual machine (VM) or as a pod on a Red Hat OpenShift cluster. In California, we deploy the CE as a VM using Red Hat OpenShift Virtualization — click here to find out more on Deploying F5 Distributed Cloud Customer Edge in Red Hat OpenShift Virtualization: Segment-A: jy-ocp on CE in California and Segment-B: s-gorman-production-segment on CE in Seattle: Simply and securely connect Segment-A: jy-ocp and Segment-B: s-gorman-production-segment only, using Segment Connector: NetApp ONTAP in Seattle has a LUN named “tbd-RAG”, which serves as the enterprise data store in our demo setup and contains a collection of data. After these two data centers are connected using F5 XC Network Connect, a secure encrypted end-to-end connection is established between them. In our example, “test-ai-tbd” is in the data center in California where it hosts the RAG services, including embedding Large Language Model (LLM) and vector database, and it can now successfully connect to the enterprise data stores on NetApp ONTAP in the data center in Seattle: F5 Distributed Cloud App Connect F5 Distributed Cloud App Connect securely connects and delivers distributed applications and services across hybrid and multicloud environments. By utilizing F5 Distributed Cloud App Connect, we can direct the inference traffic through F5 Distributed Cloud's security layers to safeguard our inference endpoints. Red Hat OpenShift on Amazon Web Services (ROSA) is a fully managed service that allows users to develop, run, and scale applications in a native AWS environment. We can host our inference service on ROSA so that we can leverage the scalability, cost-effectiveness, and numerous benefits of AWS’s managed infrastructure services. For instance, we can host our inference service on ROSA by deploying Ollama with multiple AI/ML models: Or, we can enable Model Serving on Red Hat OpenShift AI (RHOAI). Red Hat OpenShift AI (RHOAI) is a flexible and scalable AI/ML platform builds on the capabilities of Red Hat OpenShift that facilitates collaboration among data scientists, engineers, and app developers. This platform allows them to serve, build, train, deploy, test, and monitor AI/ML models and applications either on-premise or in the cloud, fostering efficient innovation within organizations. In our example, we use Red Hat OpenShift AI (RHOAI) Model Serving on ROSA for our inference service: Once inference service is deployed on ROSA, we can utilize F5 Distributed Cloud to secure our inference endpoint by steering the inference traffic through F5 Distributed Cloud's security layers, which offers an extensive suite of features designed specifically for the security of modern AI/ML inference endpoints. This setup would allow us to scrutinize requests, implement policies for detected threats, and protect sensitive datasets before they reach the inferencing service hosted within ROSA. In our example, we setup a F5 Distributed Cloud HTTP Load Balancer (rhoai-llm-serving.f5-demo.com), and we advertise it to the CE in the datacenter in California only: We now reach our Red Hat OpenShift AI (RHOAI) inference endpoint through F5 Distributed Cloud: F5 Distributed Cloud Web App & API Protection F5 Distributed Cloud Web App & API Protection provides comprehensive sets of security features, and uniform observability and policy enforcement to protect apps and APIs across hybrid and multicloud environments. We utilize F5 Distributed Cloud App Connect to steer the inference traffic through F5 Distributed Cloud to secure our inference endpoint. In our example, we protect our Red Hat OpenShift AI (RHOAI) inference endpoint by rate-limiting the access, so that we can ensure no single client would exhaust the inference service: A "Too Many Requests" is received in the response when a single client repeatedly requests access to the inference service at a rate higher than the configured threshold: This is just one of the many security features to protect our inference service. Click here to find out more on Securing Model Serving in Red Hat OpenShift AI (on ROSA) with F5 Distributed Cloud API Security. Demonstration In a real-world scenario, the front-end application could be hosted on the cloud, or hosted at the edge, or served through F5 Distributed Cloud, offering flexible alternatives for efficient application delivery based on user preferences and specific needs. To illustrate how all the discussed components work seamlessly together, we simplify our example by deploying Open WebUI as the front-end application on the Red Hat OpenShift cluster in the data center in California, which includes RAG services. While a DPU or GPU could be used for improved performance, our setup utilizes a CPU for inferencing tasks. We connect our app to our enterprise data stores deployed on NetApp ONTAP in the data center in Seattle using F5 Distributed Cloud Network Connect, where we have a copy of "Chapter 1. About the Migration Toolkit for Virtualization" from Red Hat. These documents are processed and saved to the Vector DB: Our embedding Large Language Model (LLM) is Sentence-Transformers/all-MiniLM-L6-v2, and here is our RAG template: Instead of connecting to the inference endpoint on Red Hat OpenShift AI (RHOAI) on ROSA directly, we connect to the F5 Distributed Cloud HTTP Load Balancer (rhoai-llm-serving.f5-demo.com) from F5 Distributed Cloud App Connect: Previously, we asked, "What is MTV?“ and we never received a response related to Red Hat Migration Toolkit for Virtualization: Now, let's try asking the same question again with RAG services enabled: We finally received the response we had anticipated. Next, we use F5 Distributed Cloud Web App & API Protection to safeguard our Red Hat OpenShift AI (RHOAI) inference endpoint on ROSA by rate-limiting the access, thus preventing a single client from exhausting the inference service: As expected, we received "Too Many Requests" in the response on our app upon requesting the inference service at a rate greater than the set threshold: With F5 Distributed Cloud's real-time observability and security analytics from the F5 Distributed Console, we can proactively monitor for potential threats. For example, if necessary, we can block a client from accessing the inference service by adding it to the Blocked Clients List: As expected, this specific client is now unable to access the inference service: Summary Deploying and securing RAG for enterprise RAG-enabled AI applications in a multi-vendor, hybrid, and multi-cloud environment can present complex challenges. In collaboration with Red Hat OpenShift AI (RHOAI) and NetApp ONTAP, F5 Distributed Cloud provides an effortless solution that secures RAG components seamlessly for enterprise RAG-enabled AI applications.
Jennifer_Yeung
Feb 25, 2025 Place Technical Articles
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Securing Model Serving in Red Hat OpenShift AI (on ROSA) with F5 Distributed Cloud API Security
Learn how Red Hat OpenShift AI on ROSA and F5 Distributed Cloud API Security work together to protect generative AI model inference endpoints. This integration ensures robust API discovery, schema enforcement, LLM-aware threat detection, bot mitigation, sensitive data redaction, and continuous observability—enabling secure, compliant, and high-performance AI-driven experiences at scale.
Eric_Ji
Jan 03, 2025 Place Technical Articles
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AI RAG using F5 Distributed Cloud in Red Hat OpenShift AI and NetApp ONTAP Environment","conversation":{"__ref":"Conversation:conversation:339884"},"id":"message:339884","revisionNum":25,"uid":339884,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:421692"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":"","introduction":"","metrics":{"__typename":"MessageMetrics","views":308},"postTime":"2025-02-24T19:51:18.469-08:00","lastPublishTime":"2025-02-24T19:51:18.469-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction \n Retrieval Augmented Generation (RAG) is a powerful technique that allows Large Language Models (LLMs) to access information beyond their training data. The “R” in RAG refers to the data retrieval process, where the system retrieves relevant information from an external knowledge base based on the input query. Next, the “A” in RAG represents the augmentation of context enrichment, as the system combines the retrieved relevant information and the input query to create a more comprehensive prompt for the LLM. Lastly, the “G” in RAG stands for response generation, where the LLM generates a response with a more contextually accurate output based on the augmented prompt as a result. \n RAG is becoming increasingly popular in enterprise AI applications due to its ability to provide more accurate and contextually relevant responses to a wide range of queries. However, deploying RAG can introduce complexity due to its components being located in different environments. For instance, the datastore or corpus, which is a collection of data, is typically on-premise for enhanced control over data access and management due to data security, governance, and compliance with regulations within the enterprise. Meanwhile, inference services are often deployed in the cloud for their scalability and cost-effectiveness. \n In this article, we will discuss how F5 Distributed Cloud can simplify the complexity and securely connect all RAG components seamlessly for enterprise RAG-enabled AI applications deployments. Specifically, we will focus on Network Connect, App Connect, and Web App & API Protection. We will demonstrate how these F5 Distributed Cloud features can be leveraged to secure RAG in collaboration with Red Hat OpenShift AI and NetApp ONTAP. \n Example Topology \n \n \n F5 Distributed Cloud Network Connect \n F5 Distributed Cloud Network Connect enables seamless and secure network connectivity across hybrid and multicloud environments. By deploying F5 Distributed Cloud Customer Edge (CE) at site, it allows us to easily establish encrypted site-to-site connectivity across on-premises, multi-cloud, and edge environment. \n Jensen Huang, CEO of NVIDIA, has said that \"Nearly half of the files in the world are stored on-prem on NetApp.”. In our example, enterprise data stores are deployed on NetApp ONTAP in a data center in Seattle managed by organization B (Segment-B: s-gorman-production-segment), while RAG services, including embedding Large Language Model (LLM) and vector database, is deployed on-premise on a Red Hat OpenShift cluster in a data center in California managed by Organization A (Segment-A: jy-ocp). By leveraging F5 Distributed Cloud Network Connect, we can quickly and easily establish a secure connection for seamless and efficient data transfer from the enterprise data stores to RAG services between these two segments only: \n \n F5 Distributed Cloud CE can be deployed as a virtual machine (VM) or as a pod on a Red Hat OpenShift cluster. In California, we deploy the CE as a VM using Red Hat OpenShift Virtualization — click here to find out more on Deploying F5 Distributed Cloud Customer Edge in Red Hat OpenShift Virtualization: \n \n Segment-A: jy-ocp on CE in California and Segment-B: s-gorman-production-segment on CE in Seattle: \n \n Simply and securely connect Segment-A: jy-ocp and Segment-B: s-gorman-production-segment only, using Segment Connector: \n \n NetApp ONTAP in Seattle has a LUN named “tbd-RAG”, which serves as the enterprise data store in our demo setup and contains a collection of data. After these two data centers are connected using F5 XC Network Connect, a secure encrypted end-to-end connection is established between them. In our example, “test-ai-tbd” is in the data center in California where it hosts the RAG services, including embedding Large Language Model (LLM) and vector database, and it can now successfully connect to the enterprise data stores on NetApp ONTAP in the data center in Seattle: \n \n F5 Distributed Cloud App Connect \n F5 Distributed Cloud App Connect securely connects and delivers distributed applications and services across hybrid and multicloud environments. By utilizing F5 Distributed Cloud App Connect, we can direct the inference traffic through F5 Distributed Cloud's security layers to safeguard our inference endpoints. \n \n Red Hat OpenShift on Amazon Web Services (ROSA) is a fully managed service that allows users to develop, run, and scale applications in a native AWS environment. We can host our inference service on ROSA so that we can leverage the scalability, cost-effectiveness, and numerous benefits of AWS’s managed infrastructure services. For instance, we can host our inference service on ROSA by deploying Ollama with multiple AI/ML models: \n \n Or, we can enable Model Serving on Red Hat OpenShift AI (RHOAI). Red Hat OpenShift AI (RHOAI) is a flexible and scalable AI/ML platform builds on the capabilities of Red Hat OpenShift that facilitates collaboration among data scientists, engineers, and app developers. This platform allows them to serve, build, train, deploy, test, and monitor AI/ML models and applications either on-premise or in the cloud, fostering efficient innovation within organizations. In our example, we use Red Hat OpenShift AI (RHOAI) Model Serving on ROSA for our inference service: \n \n Once inference service is deployed on ROSA, we can utilize F5 Distributed Cloud to secure our inference endpoint by steering the inference traffic through F5 Distributed Cloud's security layers, which offers an extensive suite of features designed specifically for the security of modern AI/ML inference endpoints. This setup would allow us to scrutinize requests, implement policies for detected threats, and protect sensitive datasets before they reach the inferencing service hosted within ROSA. In our example, we setup a F5 Distributed Cloud HTTP Load Balancer (rhoai-llm-serving.f5-demo.com), and we advertise it to the CE in the datacenter in California only: \n \n We now reach our Red Hat OpenShift AI (RHOAI) inference endpoint through F5 Distributed Cloud: \n \n F5 Distributed Cloud Web App & API Protection \n F5 Distributed Cloud Web App & API Protection provides comprehensive sets of security features, and uniform observability and policy enforcement to protect apps and APIs across hybrid and multicloud environments. We utilize F5 Distributed Cloud App Connect to steer the inference traffic through F5 Distributed Cloud to secure our inference endpoint. In our example, we protect our Red Hat OpenShift AI (RHOAI) inference endpoint by rate-limiting the access, so that we can ensure no single client would exhaust the inference service: \n \n A \"Too Many Requests\" is received in the response when a single client repeatedly requests access to the inference service at a rate higher than the configured threshold: \n \n This is just one of the many security features to protect our inference service. Click here to find out more on Securing Model Serving in Red Hat OpenShift AI (on ROSA) with F5 Distributed Cloud API Security. \n Demonstration \n In a real-world scenario, the front-end application could be hosted on the cloud, or hosted at the edge, or served through F5 Distributed Cloud, offering flexible alternatives for efficient application delivery based on user preferences and specific needs. To illustrate how all the discussed components work seamlessly together, we simplify our example by deploying Open WebUI as the front-end application on the Red Hat OpenShift cluster in the data center in California, which includes RAG services. While a DPU or GPU could be used for improved performance, our setup utilizes a CPU for inferencing tasks. \n We connect our app to our enterprise data stores deployed on NetApp ONTAP in the data center in Seattle using F5 Distributed Cloud Network Connect, where we have a copy of \"Chapter 1. About the Migration Toolkit for Virtualization\" from Red Hat. These documents are processed and saved to the Vector DB: \n \n Our embedding Large Language Model (LLM) is Sentence-Transformers/all-MiniLM-L6-v2, and here is our RAG template: \n \n Instead of connecting to the inference endpoint on Red Hat OpenShift AI (RHOAI) on ROSA directly, we connect to the F5 Distributed Cloud HTTP Load Balancer (rhoai-llm-serving.f5-demo.com) from F5 Distributed Cloud App Connect: \n \n Previously, we asked, \"What is MTV?“ and we never received a response related to Red Hat Migration Toolkit for Virtualization: \n \n Now, let's try asking the same question again with RAG services enabled: \n \n We finally received the response we had anticipated. \n Next, we use F5 Distributed Cloud Web App & API Protection to safeguard our Red Hat OpenShift AI (RHOAI) inference endpoint on ROSA by rate-limiting the access, thus preventing a single client from exhausting the inference service: \n \n As expected, we received \"Too Many Requests\" in the response on our app upon requesting the inference service at a rate greater than the set threshold: \n \n With F5 Distributed Cloud's real-time observability and security analytics from the F5 Distributed Console, we can proactively monitor for potential threats. For example, if necessary, we can block a client from accessing the inference service by adding it to the Blocked Clients List: \n \n As expected, this specific client is now unable to access the inference service: \n \n Summary \n Deploying and securing RAG for enterprise RAG-enabled AI applications in a multi-vendor, hybrid, and multi-cloud environment can present complex challenges. In collaboration with Red Hat OpenShift AI (RHOAI) and NetApp ONTAP, F5 Distributed Cloud provides an effortless solution that secures RAG components seamlessly for enterprise RAG-enabled AI applications. 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Model Serving in Red Hat OpenShift AI (on ROSA) with F5 Distributed Cloud API Security","conversation":{"__ref":"Conversation:conversation:338353"},"id":"message:338353","revisionNum":60,"uid":338353,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:326558"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Learn how Red Hat OpenShift AI on ROSA and F5 Distributed Cloud API Security work together to protect generative AI model inference endpoints. This integration ensures robust API discovery, schema enforcement, LLM-aware threat detection, bot mitigation, sensitive data redaction, and continuous observability—enabling secure, compliant, and high-performance AI-driven experiences at scale. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":781},"postTime":"2024-12-18T12:01:43.571-08:00","lastPublishTime":"2024-12-18T12:01:43.571-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" As enterprises embrace Generative AI—particularly deploying large language models (LLMs) and other foundational AI models—production environments become increasingly complex. Organizations need an end-to-end MLOps platform that streamlines the entire lifecycle: developing, training, fine-tuning, and especially serving models at scale. \n Red Hat OpenShift AI (OpenShift AI) meets this need by providing a comprehensive, hybrid MLOps environment. When deployed on Red Hat OpenShift Service on AWS (ROSA), OpenShift AI takes advantage of AWS’s managed infrastructure services and cloud-native elasticity, enabling organizations to scale AI/ML workflows efficiently and cost-effectively. \n Yet, as models are served to end-users or integrated into downstream applications, security considerations become paramount. Inference endpoints may be targeted for unauthorized access, data exfiltration, or prompt manipulation. F5 Distributed Cloud addresses these challenges by offering robust capabilities—API discovery, schema enforcement, LLM-aware threat detection, bot mitigation, sensitive data redaction, and continuous observability—to ensure that inference endpoints remain secure, compliant, and high-performing. \n In this post, we will: \n \n Introduce OpenShift AI and its capabilities for AI/ML workloads with an emphasis on model serving. \n Discuss how running OpenShift AI on ROSA leverages AWS services for scalable, cost-effective AI/ML operations. \n Show how F5 Distributed Cloud API Security enhances the security posture of generative AI model inference endpoints—covering automated API discovery, schema enforcement, threat detection, rate limiting, and compliance measures. \n Demonstrate how to integrate these capabilities end-to-end, using Ollama Mistral-7B as an example model. \n \n What is Red Hat OpenShift AI for Generative AI Applications? \n Red Hat OpenShift AI is a hybrid MLOps platform designed to simplify the entire AI lifecycle. It empowers teams to train, fine-tune, deploy, serve, monitor, and continuously improve generative AI models. By merging IT operations, data science workflows, and application development practices into one platform, OpenShift AI accelerates innovation, fosters governance, and encourages collaboration—essential ingredients for delivering enterprise-grade AI solutions. \n Key Features: \n \n Hybrid MLOps Platform: Unified environment for data scientists, developers, and operators, whether you run on-premises, in the cloud, or in a hybrid setup. \n Distributed Workloads & Fine-Tuning: Scale model training and fine-tuning across distributed compute frameworks, adapting large language models to your domain’s requirements. \n Model Serving & Monitoring: Deploy and serve models at scale using technologies like KServe, ModelMesh, and a variety of specialized runtimes (OpenVINO™ Model Server, Caikit/TGIS, NVIDIA Triton Inference Server, etc.). Continuously monitor model performance, detect drift, and ensure ongoing model quality. \n Lifecycle Management & DevOps Integration: Seamlessly integrate data science pipelines with CI/CD workflows. Automate model deployment, rollout new versions safely, and achieve consistent delivery of AI-driven features. \n Enhanced Collaboration: Enable data scientists, developers, and IT Ops to work together using notebooks (JupyterLab), popular frameworks (TensorFlow, PyTorch), and unified governance, speeding up innovation and time-to-value. \n \n High-Level Architecture: OpenShift AI integrates workbenches, distributed workloads, data science pipelines, serving engines, and monitoring tools atop Kubernetes and OpenShift operators, leveraging GitOps, pipelines, service mesh, and serverless technologies. \n \n Figure 1: OpenShift AI High-Level Architecture \n Running OpenShift AI on ROSA for Scalable AI/ML Solutions \n Red Hat OpenShift Service on AWS (ROSA) brings a fully managed OpenShift environment to AWS. This allows teams to focus on building and serving AI models rather than managing infrastructure. \n Key advantages: \n \n Scalability: Seamlessly scale GPU-accelerated compute, storage, and networking resources as model serving workloads grow. \n Cost Efficiency & On-Demand Resources: Leverage Amazon EC2 instances, Amazon S3, and other AWS services only as needed, paying as you go. \n Unified Management: Offload cluster operations and lifecycle management to ROSA, ensuring reliable operations and freeing your team to concentrate on AI innovation. \n \n Security Challenges in Model Serving for Generative AI \n While OpenShift AI and ROSA simplify operations, serving AI models still raises critical security concerns: \n \n Unauthorized Access & Data Leakage: External requests to inference endpoints may attempt to extract proprietary knowledge or sensitive data from the model. \n Prompt Injection & Malicious Content: LLMs can be tricked into producing harmful or confidential outputs if the prompts are manipulated. \n Bot Attacks & Performance Risks: Automated scripts can overwhelm inference endpoints, degrade performance, or escalate costs. \n Compliance & Sensitive Data Handling: AI outputs can contain PII or regulated data, necessitating encryption, redaction, and audit trails to meet compliance demands. \n Evolving Threat Landscape: The complexity and dynamism of AI models and APIs call for continuous posture management and adaptive threat detection. \n \n Enhancing Security with F5 Distributed Cloud API Security \n F5 Distributed Cloud provides a comprehensive set of capabilities tailored for securing modern AI inference endpoints. By integrating these capabilities with OpenShift AI deployments on ROSA, organizations gain: \n \n Automated API Discovery & Posture Management:\n \n Identify all inference endpoints automatically, eliminating hidden APIs. \n Enforce schemas based on observed traffic to ensure requests and responses follow expected patterns. \n Integrate “shift-left” security checks into CI/CD pipelines, catching misconfigurations before production. \n \n \n LLM-Aware Threat Detection & Request Validation:\n \n Detect attempts to manipulate prompts or break compliance rules, ensuring suspicious requests are blocked before reaching the model. \n \n \n Bot Mitigation & Adaptive Rate Controls:\n \n Differentiate between legitimate users and bots or scrapers, blocking automated attacks. \n Dynamically adjust rate limits and policies based on usage history and real-time conditions, maintaining performance and reliability. \n \n \n Sensitive Data Redaction & Compliance:\n \n Identify and mask PII or sensitive tokens in requests and responses. \n Adhere to data protection regulations and maintain detailed logs for auditing, monitoring, and compliance reporting. \n \n \n Seamless Integration & Observability:\n \n Deploy F5 Distributed Cloud seamlessly alongside OpenShift AI on ROSA without reshuffling existing architecture. \n Use centralized dashboards and analytics to monitor real-time metrics—latency, error rates, compliance indicators—to continuously refine policies and adapt to emerging threats. \n \n \n \n Example: Working with Multiple OLLAMA Models and Programmatic Inference \n In this scenario, multiple OLLAMA models have been deployed on the OpenShift cluster. For instance: \n sh-5.1$ ollama ls \nNAME ID SIZE MODIFIED \nllama2:7b 78e26419b446 3.8 GB 3 days ago \nllama3.2:1b baf6a787fdff 1.3 GB 2 weeks ago \nmario:latest 7434c42677ab 3.8 GB 12 seconds ago \nmistral:latest f974a74358d6 4.1 GB 11 days ago \norca-mini:latest 2dbd9f439647 2.0 GB About a minute ago\nphi3:latest 4f2222927938 2.2 GB 2 weeks ago \nphi3:mini 4f2222927938 2.2 GB 2 weeks ago \ntinyllama:latest 2644915ede35 637 MB 2 weeks ago \n \n We have a variety of models—ranging from Mistral-7B to tinyllama—that can be served simultaneously. While the environment is currently using CPUs for hosting these models, you could leverage GPUs for better performance in a production scenario. \n Unlike the model training and fine-tuning phase—where we worked directly within OpenShift AI Workbenches (to be covered in future blogs)— in this scenario we’re querying the LLM endpoint from a local application, where Python libraries and LangChain are installed. Instead of hitting the cluster directly, we route traffic through an F5 Distributed Cloud-managed URL (e.g., http://llm01.volt.thebizdevops.net). In a real-world deployment, the frontend could also be hosted on OpenShift or served through F5 Distributed Cloud Regional Edges (RE), providing flexible options for scaling and delivering the application. \n This ensures that all requests pass through F5 Distributed Cloud’s security layers, applying policies, detecting threats, and protecting sensitive data before they reach the LLM endpoint hosted in OpenShift AI on ROSA. \n \n Figure 2: Integrated Architecture with F5 Distributed Cloud and OpenShift AI on AWS \n F5 Distributed Cloud Capabilities in Action \n To illustrate the key F5 Distributed Cloud features, we’ve divided them into distinct capabilities and included screen captures in the F5 Distributed Cloud console. These captures provide a visual reference to understand how each capability enhances the security and compliance posture of your LLM inference endpoints. \n 1. API Discovery & Schema Enforcement \n What It Does: F5 Distributed Cloud automatically identifies all exposed inference endpoints for your AI/LLM models. It then derives schemas from real traffic, enforcing expected request and response formats. By blocking malformed inputs early, your model stays protected, ensuring consistent, reliable, and trustworthy inferences. \n \n Figure 3: API Discovery & Schema Enforcement (Refer to the annotated image above showing multiple discovered endpoints, shadow APIs, and downloadable OpenAPI specifications derived from actual traffic patterns.) \n 2. LLM-Aware Threat Detection & Request Validation \n What It Does: This feature identifies potential threats to your LLM endpoints by enforcing strict OpenAPI-based validation on incoming requests. By catching invalid or suspicious inputs early, you can adjust policies to block them in the future, ensuring that malicious attempts—whether aiming to exploit the LLM’s behavior or break compliance rules—never reach the inference logic. \n \n Figure 4: Security Analysis for a Non-Compliant Request (Here, the request triggered an OpenAPI validation failure. Although currently “allowed,” policies can be easily configured to “block” these events going forward, preventing non-compliant or potentially harmful requests from impacting your LLM models.) \n \n \n Figure 5: API Inventory Validation Configuration (This image illustrates the corresponding configuration settings for OpenAPI Validation. By validating both requests and responses at multiple layers—headers, body, and content-type—F5 Distributed Cloud ensures that LLM prompts remain safe, compliant, and free from injection attacks.) \n 3. Bot Mitigation & Rate Limiting \n What It Does: Differentiates legitimate user traffic from automated bots or scrapers bot mitigation, and ensures fair usage of resources (rate limiting). By differentiating legitimate requests from bot-driven abuse and enforcing request thresholds, F5 Distributed Cloud protects inference endpoints from performance degradation while maintaining a positive user experience. \n \n Figure 6: Bot Defense in Action (This image shows how F5 Distributed Cloud identifies and classifies automated traffic as “Bad Bots,” blocking them to preserve endpoint availability and prevent resource exhaustion.) \n \n \n Figure 7: Configuring Rate Limits (This image illustrates the setup of request thresholds for a specific endpoint, ensuring no single client overwhelms the inference service.) \n \n \n Figure 8: Enforced Rate Limit (429 Too Many Requests) (This image demonstrates a client exceeding the above-configured request limit and receiving a 429 response, confirming that F5 Distributed Cloud’s rate limiting is actively maintaining fair resource allocation.) \n 4. Sensitive Data Redaction & Compliance Logging \n What It Does: Identifies and masks personally identifiable information (PII) or other sensitive data—such as credit card numbers, emails, and phone numbers—within model responses. New Sensitive Data Exposure Rules allow you to customize and enforce policies to block or redact sensitive fields dynamically. This ensures compliance with frameworks like HIPAA, GDPR., and other regulatory mandates while capturing detailed logs for auditing. \n \n Figure 9: Adding Sensitive Data Exposure Rules (This image shows how you can add custom rules to detect and control exposure of sensitive fields, such as card-expiration dates, phone numbers, and credit-card details, ensuring model responses comply with organizational security policies.) \n \n \n Figure 10: Sensitive Data Detection Across APIs (Here, sensitive data types—like social-security numbers and phone numbers—are automatically detected across API responses. Built-in and custom rules flag potential exposures, empowering teams to enforce redaction and maintain compliance.) \n \n \n Figure 11: Service Policy for Model Validation (This image shows a service policy in action, blocking an inference request that doesn’t meet defined model validation criteria. Such policies can also be tied to compliance mandates, ensuring non-compliant responses are never returned to clients.) \n \n \n Figure 12: API Compliance & Sensitive Data Detection (Here, sensitive fields like credit-card or phone-number are automatically identified, and associated compliance frameworks (HIPAA, GDPR) are recognized. This empowers you to enforce data redaction, maintain regulatory compliance, and produce audit-ready logs without revealing sensitive details.) \n 5. Centralized Observability & Continuous Policy Updates \n What It Does: Offers dashboards and analytics tools to monitor request volumes, latency, errors, and compliance metrics across your AI inference endpoints. Security teams can leverage these observations to continuously refine their policies, enhance schema validations, and recalibrate rate limits as threats evolve or model usage grows. \n \n Figure 13: Endpoint-Level Metrics Dashboard (This example shows an LLM endpoint /api/generate with available metrics including error rate, latency, request rate, request size, response size, and throughput. By monitoring these trends, teams can quickly identify performance bottlenecks, detect anomalies, and apply targeted policy changes to maintain optimal efficiency and security.) \n The Outcome: Secure, Compliant, and Performant LLM Serving \n By combining Red Hat OpenShift AI on ROSA with F5 Distributed Cloud, organizations can: \n \n Confidently serve multiple LLMs at scale, handling diverse use cases and workloads. \n Securely expose inference endpoints, ensuring that requests from external clients are validated, sanitized, and protected against prompt injection, unauthorized access, or excessive traffic. \n Maintain compliance and privacy, redacting sensitive data and logging requests for auditing and reporting purposes. \n Continuously adapt to evolving threats, leveraging real-time observability and agile policy management for persistent security improvements. \n \n This powerful combination enables generative AI models to be woven into complex enterprise workflows—such as insurance claims processing—without sacrificing trust, governance, or user satisfaction. \n Conclusion \n Red Hat OpenShift AI on ROSA, bolstered by F5 Distributed Cloud API Security, provides a robust, scalable, and secure foundation for running generative AI workloads in production. Together, they address the nuanced security challenges of exposing LLM inference endpoints to external clients. \n Whether you are working with a single LLM or managing a portfolio of models like OLLAMA’s Mistral, Phi3, and TinyLlama, this combination ensures that your users—connecting from anywhere—can trust the quality, security, and compliance of the AI services they rely on. 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