GenAI

4 Topics
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While it offers businesses a competitive edge by streamlining operations and improving customer experiences, it also introduces risks such as security vulnerabilities, data breaches, and cost challenges. Businesses must adopt robust cybersecurity measures and carefully manage AI investments to balance benefits with risks. F5 provides comprehensive controls to protect AI and IT infrastructures, ensuring sustainable growth in an AI-driven world. Welcome to F5 AI Gateway - a runtime security and traffic governance solution ","introduction":"","metrics":{"__typename":"MessageMetrics","views":783},"postTime":"2025-01-27T05:00:00.040-08:00","lastPublishTime":"2025-01-27T05:00:00.040-08:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction \n Artificial Intelligent (AI) has transformed the way we live and work, revolutionising industries and enhancing everyday life. By automating repetitive tasks and enabling data-driven decision-making, AI has unlocked unprecedented levels of innovation, efficiency, and productivity. For businesses, AI offers a significant competitive edge, streamlining operations, enhancing customer experiences, and driving growth. However, with great opportunities comes great risks. AI introduces security vulnerabilities, including potential misuse, data breaches, and expanded attack surfaces. It is essential for businesses to recognize these challenges and adopt robust cybersecurity measures to mitigate risks. Moreover, AI is an investment-intensive technology, and its return on investment remains difficult to quantify (at least as of this writing). Without careful oversight and optimization, AI budgets can quickly spiral out of control. Embracing AI responsibly—balancing its benefits with its risks—ensures sustainable growth and resilience in an increasingly AI-driven world. \n At F5, we offer a wide range of controls to safeguard your AI infrastructure, applicable not only to AI systems but to all IT environments.  \n \n In this article and video demo, we will focus on the AI Gateway (AI GW), which provides runtime security and traffic governance. It’s worth noting that other controls, such as securing APIs—the backbone of any AI service—are equally critical.  \n The F5 AI Gateway is designed to address specific challenges, such as those outlined in the OWASP Top 10 for LLM Applications. It features an extensible architecture with purpose-built processors and plugins, enhanced by a Software Development Kit (SDK) for custom use cases. \n Here are those demo videos on F5 AI Gateways. \n   \n Traffic Management and Optimisation of LLM \n AI Gateway function is more than just a security control tools. It play an important roles on cost optimization and traffic management. This video to demonstrate the value and capabilities of F5 AI Gateway on traffic management and optimization of LLM traffic to optimize resources, reduces costs, enhances user experience, and safeguards data privacy. \n \n   \n Securing LLM Applications \n Demonstrate F5 AI GW capabilities to address some of the key concern raised in OWASP TOP 10 for LLM apps by leveraging native in-build AI processor. \n \n   \n Specific individual Demo Video. \n Please do note that the following individual specific demo video is an excerpt of the overall Traffic management, Optimization and Security demo. \n   \n Identity Aware Model Routing \n Demonstrate the capability of F5 AI Gateway to dynamically apply and steer LLM traffic policy according to user identity based on JSON Web Token (JWT) or header. \n \n   \n Language and Code Detection Routing \n Demonstrate the capability of F5 AI Gateway to detect type of language input and steer according to the defined language model for better user experience and cost optimization. It also demonstrate code detection and steer the traffic to a local self-hosted model to prevent any unintentional code leakage to a SaaS-Managed model. \n \n   \n System Prompt Enforcement (Guardrail) \n Showcase the system prompt capabilities of the F5 AI Gateway (AI GW) to ensure conversations remain contextually relevant. The AI Gateway enforces strict guardrails to prevent users from deviating from the intended purpose of the chatbot. This helps maintain the integrity of the system while preventing the misuse of AI resources \n \n   \n OWASP Top 10 LLM01 - Prompt Injection \n Showcase Prompt Injection processor to address few key concern raised in OWASP TOP 10 for LLM apps especially on Prompt Injection concern. (LLM01) \n \n   \n OWASP Top 10 LLM02 - Sensitive Information Disclosure (PII) \n Showcase PII Redactor processor to address key concern raise on sensitive information disclosure raise in OWASP TOP 10 for LLM apps (LLM02) \n \n   \n OWASP Top 10 LLM07 - System Prompt Leakage \n Showcase System Prompt processor to address key concern raise on system prompt leakage raise in OWASP TOP 10 for LLM apps (LLM07) \n \n Please reach out to F5 representative for further deep-dive information. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"4286","kudosSumWeight":5,"repliesCount":1,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzkyMDctMlNBbnVx?revision=7\"}"}}],"totalCount":1,"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:335138":{"__typename":"Conversation","id":"conversation:335138","topic":{"__typename":"TkbTopicMessage","uid":335138},"lastPostingActivityTime":"2024-10-18T12:08:36.968-07:00","solved":false},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzUxMzgtY3J6WXdz?revision=12\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzUxMzgtY3J6WXdz?revision=12","title":"agentic-rag-pipeline.png","associationType":"BODY","width":3334,"height":1808,"altText":""},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzUxMzgtaldrUGh0?revision=12\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzUxMzgtaldrUGh0?revision=12","title":"ref-arch.png","associationType":"BODY","width":3410,"height":1734,"altText":""},"TkbTopicMessage:message:335138":{"__typename":"TkbTopicMessage","subject":"Enhance your GenAI chatbot with the power of Agentic RAG and F5 platform","conversation":{"__ref":"Conversation:conversation:335138"},"id":"message:335138","revisionNum":12,"uid":335138,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:171064"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" Agentic RAG (Retrieval-Augmented Generation) enhances the capabilities of a GenAI chatbot by integrating dynamic knowledge retrieval into its conversational abilities, making it more context-aware and accurate. \n In this demo, I will demonstrate an autonomous decision-making GenAI chatbot utilizing Agentic RAG. I will explore what Agentic RAG is and why it's crucial in today's AI landscape. I will also discuss how organizations can leverage GPUaaS (GPU as a Service) or AI Factory providers to accelerate their AI strategy. \n F5 platform provides robust security features that protect sensitive data while ensuring high availability and performance. They optimize the chatbot by streamlining traffic management and reducing latency, ensuring smooth interactions even during high demand. This integration ensures the GenAI chatbot is not only smart but also reliable and secure for enterprise use. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":508},"postTime":"2024-10-15T05:00:00.029-07:00","lastPublishTime":"2024-10-18T12:08:36.968-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction \n This is the GenAI demo series, where we focus on securing, delivering, and optimizing modern GenAI apps with F5. In my first demo video, I demonstrated how Retrieval-Augmented Generation (RAG) works and highlighted its value for an organization. This video will build upon that foundational understanding by focusing on enhancing and supercharging your GenAI chatbot with the power of Agentic RAG (Agent-based RAG). Learn how F5 platform makes it ridiculously easy to achieve your organization's AI journey. I will also touch on how you can leverage GPUaaS or AI Factory providers to accelerate your AI strategy. \n   \n What is an Agentic RAG? \n Agentic RAG is an advanced AI framework that extends traditional Retrieval-Augmented Generation (RAG) systems by integrating enhanced decision-making capabilities. Unlike standard RAG models, which focus primarily on retrieving context, Agentic RAG incorporates reasoning and autonomy. This allows the AI system to make decisions and take actions based on retrieved data to achieve predefined goals. By combining RAG with agent-based tools, it creates a more dynamic and intelligent system. \n Agentic RAG Pipeline \n \n   \n Architecture \n \n Both Agentic RAG pipeline will be discussed and explained in the demo video. \n   \n \n Agentic RAG Demo \n \n \n \n Security - Protecting your GenAI Chatbot (coming soon) \n Delivery & Optimization - Digital Resiliency and traffic delivery (coming soon) \n Behind the scene of Agentic RAG (coming soon) \n ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"1525","kudosSumWeight":2,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzUxMzgtY3J6WXdz?revision=12\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzUxMzgtaldrUGh0?revision=12\"}"}}],"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:334519":{"__typename":"Conversation","id":"conversation:334519","topic":{"__typename":"TkbTopicMessage","uid":334519},"lastPostingActivityTime":"2024-10-09T10:28:39.575-07:00","solved":false},"User:user:194786":{"__typename":"User","uid":194786,"login":"Janibasha","registrationData":{"__typename":"RegistrationData","status":null},"deleted":false,"avatar":{"__typename":"UserAvatar","url":"https://community.f5.com/t5/s/zihoc95639/images/dS0xOTQ3ODYtMjA5NDJpMEI1Q0JDRDNGRkQ2MUM0Mw"},"id":"user:194786"},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktOUVjdU9S?revision=7\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktOUVjdU9S?revision=7","title":"no-dataguard-result.png","associationType":"BODY","width":3122,"height":1054,"altText":""},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktZ0hwcUJs?revision=7\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktZ0hwcUJs?revision=7","title":"image.png","associationType":"BODY","width":1795,"height":1537,"altText":""},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktbU9SbVI1?revision=7\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktbU9SbVI1?revision=7","title":"owasp-llm-nap.jpeg","associationType":"BODY","width":1670,"height":660,"altText":""},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktRXcwUTBy?revision=7\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktRXcwUTBy?revision=7","title":"svc.JPG","associationType":"BODY","width":3213,"height":614,"altText":""},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktRXRHaUxX?revision=7\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktRXRHaUxX?revision=7","title":"llm-ssn-hidden.jpg","associationType":"BODY","width":3663,"height":1709,"altText":""},"TkbTopicMessage:message:334519":{"__typename":"TkbTopicMessage","subject":"Mitigate OWASP LLM Security Risk: Sensitive Information Disclosure Using F5 NGINX App Protect","conversation":{"__ref":"Conversation:conversation:334519"},"id":"message:334519","revisionNum":7,"uid":334519,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:194786"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" This short WAF security article covered the critical security gaps present in current generative AI applications, emphasizing the urgent need for robust protection measures in LLM design deployments. Finally we also demonstrated how F5 Nginx App Protect v5 offers an effective solution to mitigate the OWASP LLM Top 10 risks. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":343},"postTime":"2024-10-07T05:00:00.027-07:00","lastPublishTime":"2024-10-09T10:28:39.575-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction: \n This article covers the basics of the rise of AI (Gen AI) and how F5 products can protect these AI backends. The rise of Large Language Models (LLMs) has marked a transformative era in AI, enabling machines to produce and comprehend text with human-like proficiency. These sophisticated models are now integral to applications in customer support, content creation, and even scientific research. However, their advanced capabilities also cause big security worries, especially around accidentally sharing sensitive information. These models can sometimes share private data from their training data. This means we need strong protection systems to reduce these risks. Addressing these challenges, the OWASP LLM Top 10 project has been created to identify and prioritize the most critical security threats associated with LLMs. Out of this top 10, LLM-06 risk specifically focuses on sensitive information disclosure, emphasizing the importance of stringent data handling protocols and privacy safeguards to prevent unintended data leaks to ensure the secure and ethical use of LLM technology. In this article, we are going to see how F5 Nginx App Protect v5 can protect the LLM backends from LLM06: Sensitive Information Disclosure risk. \n   \n Use case: \n We are going to deploy a Gen AI application which takes URL hosting data and is passed on to a backend LLM application. Once data is analyzed by LLM, users can ask questions about this data and LLM will come back with the right answers. We have deployed this application inside AWS EKS cluster and 2 application services are running inside this cluster. Front-end services serve the UI, and the backend hosts the LLM model. How and what of this application with its internal tools and LLM model is not of importance and can find many free tools online. Since this article focusses on LLM06: Sensitive Information Disclosure risk, we will pass a website URL containing some dummy SSN of random users. Once this website data is loaded to LLM, we can ask for the SSN of a user and LLM will return the SSN from this data. SSNs are sensitive information and should always be protected as it will lead to personal data exploitation. In this case, LLM model does not have security rules to find and protect this data. So, it will be directly shown in the response as shown below. \n \n To protect this LLM backend service, we are going to deploy and configure NGINX App Protect Version 5 as a k8s workload in the data path. The latest release of NGINX App Protect v5 has made the WAF process more ridiculously easy, thereby making it more efficient and optimized. All data traffic will be validated by the NGINX App Protect before being exposed in the response. In this use case, since we want to mask the SSN, we are going to configure the data-guard feature with its appropriate configuration files onboarded to this container.  \n \n Above configuration file can be downloaded from NGINX App Protect WAF configuration guide. \n Data guard is a WAF feature which detects and masks Credit Card Number (CCN) and/or U.S. Social Security Number (SSN) and/or custom patterns in HTTP responses. Since data-guard feature is enabled, SSNs of users in the LLM backend response are detected and masked by the NGINX App Protect thereby protecting the personal data. For more info on NGINX App Protect data guard feature, check this link.   NOTE: Since this is just for demo and focuses only on LLM workload protection, we are using NGINX App Protect v5. But as per customer practices, users can configure NGINX Ingress Controller, Secure Mesh, etc. \n \n   \n Deployment Steps:  \n \n Check the service cluster IP of backend LLM service and update it in below yaml file upstream server to create nginx config file configmap apiVersion: v1\nkind: ConfigMap\nmetadata:\n name: nginx-conf-map-api\n namespace: default\ndata:\n nginx.conf: |\n user nginx;\n\n worker_processes auto;\n load_module modules/ngx_http_app_protect_module.so;\n\n error_log /var/log/nginx/error.log debug;\n\n events {\n worker_connections 10240;\n }\n\n http {\n include /etc/nginx/mime.types;\n default_type application/octet-stream;\n sendfile on;\n keepalive_timeout 65;\n \n # NGINX App Protect WAF\n app_protect_enforcer_address 127.0.0.1:50000;\n\n upstream main_DNS_name {\n server 172.20.41.242:8000;\n }\n server {\n listen 80;\n proxy_http_version 1.1;\n proxy_read_timeout 600;\n proxy_connect_timeout 600;\n proxy_send_timeout 600;\n\n app_protect_enable on;\n app_protect_policy_file \"/etc/app_protect/bundles/NAP_API_Policy.tgz\";\n app_protect_security_log_enable on;\n app_protect_security_log log_all /etc/app_protect/bundles/security.log;\n\n location / {\n client_max_body_size 0;\n default_type text/html;\n # set your backend here\n proxy_pass http://main_DNS_name;\n proxy_set_header Host $host;\n }\n }\n }\n   \n Build and push a NGINX Plus docker image to your private registry by following this link \n Copy your JWT token and run below command to create a k8s secret # kubectl create secret docker-registry regcred --docker-server=private-registry.nginx.com --docker-username=<JWT Token> --docker-password=none \n Check the below file, update API policy bundle URL in init container and docker image info in nginx container. Apply this file to install nginx deployment and pods. apiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: nap5-deployment\nspec:\n selector:\n matchLabels:\n app: nap5\n replicas: 1\n template:\n metadata:\n labels:\n app: nap5\n spec:\n imagePullSecrets:\n - name: regcred\n initContainers:\n - name: init-fetchbundle\n image: curlimages/curl:8.9.1\n command: \n - sh\n - -c\n - |\n echo \"Downloading file...\"\n curl -vvv -L https://github.com/f5devcentral/f5-xc-terraform-examples/raw/main/workflow-guides/NAP_API_Policy.tgz -o /etc/app_protect/bundles/NAP_API_Policy.tgz \n volumeMounts:\n - name: app-protect-bundles\n mountPath: /etc/app_protect/bundles \n containers:\n - name: nginx\n image: <registry-url>:tag-name\n imagePullPolicy: IfNotPresent\n volumeMounts:\n - name: app-protect-bd-config\n mountPath: /opt/app_protect/bd_config\n - name: app-protect-config\n mountPath: /opt/app_protect/config\n - name: nginx-conf-map-api-volume\n mountPath: /etc/nginx/nginx.conf\n subPath: nginx.conf\n - name: nap-api-policy-volume\n mountPath: /etc/nginx/NAP_API_Policy.json\n subPath: NAP_API_Policy.json \n \n - name: waf-enforcer\n image: private-registry.nginx.com/nap/waf-enforcer:5.2.0\n imagePullPolicy: IfNotPresent\n env:\n - name: ENFORCER_PORT\n value: \"50000\"\n volumeMounts:\n - name: app-protect-bd-config\n mountPath: /opt/app_protect/bd_config \n \n - name: waf-config-mgr\n image: private-registry.nginx.com/nap/waf-config-mgr:5.2.0\n imagePullPolicy: IfNotPresent\n securityContext:\n allowPrivilegeEscalation: false\n capabilities:\n drop:\n - all\n volumeMounts:\n - name: app-protect-bd-config\n mountPath: /opt/app_protect/bd_config\n - name: app-protect-config\n mountPath: /opt/app_protect/config\n - name: app-protect-bundles\n mountPath: /etc/app_protect/bundles\n \n volumes:\n - name: app-protect-bd-config\n emptyDir: {}\n - name: app-protect-config\n emptyDir: {}\n - name: app-protect-bundles\n emptyDir: {}\n - name: nginx-conf-map-api-volume\n configMap:\n name: nginx-conf-map-api\n - name: nap-api-policy-volume\n configMap:\n name: nap-api-policy \n Next deploy NGINX App Protect service using below file apiVersion: v1\nkind: Service\nmetadata:\n name: nap5\n labels:\n app: nap5\n service: nap5\nspec:\n ports:\n - protocol: TCP\n port: 80\n targetPort: 80\n selector:\n app: nap5\n type: ClusterIP \n   \n \n Check the cluster services and copy the NGINX App Protect service cluster ip \n Update App Protect cluster ip address in your Gen AI application frontend yaml file openAI address to create deployment and load balancer service \n \n Testing: \n \n Once setup is complete, check the cluster services command and open the Gen AI front end load balancer service URL in a browser \n Enter the web page input as https://dlptest.com/sample-data/namessndob/ and in query provide \"What is Robert Aragon's SSN?\" \n After some time, validate that SSN number is masked in the response   \n \n Conclusion: \n This article highlights the critical security gaps present in current Gen AI applications, emphasizing the urgent need for robust protection measures in LLM design deployments. In the latter half, we demonstrated how F5 NGINX App Protect v5, with its advanced security features, offers an effective solution to mitigate the OWASP LLM Top 10 risks. By leveraging these capabilities, organizations can significantly enhance the security and resilience of their AI applications. \n   \n References: \n \n https://genai.owasp.org/llm-top-10/  \n https://genai.owasp.org/llmrisk/llm06-sensitive-information-disclosure/ \n https://docs.nginx.com/nginx-app-protect-waf/v5/admin-guide/deploy-on-kubernetes/ \n https://docs.nginx.com/nginx-app-protect-waf/v5/admin-guide/compiler/ \n \n   \n NOTE: This article covered only one risk and stay tuned for more exciting articles on remaining OWASP LLM Top 10 risks prevention using F5 products. ","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})@stringLength":"10168","kudosSumWeight":2,"repliesCount":0,"readOnly":false,"images":{"__typename":"AssociatedImageConnection","edges":[{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDE","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktOUVjdU9S?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDI","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktZ0hwcUJs?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDM","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktbU9SbVI1?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDQ","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktRXcwUTBy?revision=7\"}"}},{"__typename":"AssociatedImageEdge","cursor":"MjUuM3wyLjF8b3wyNXxfTlZffDU","node":{"__ref":"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzQ1MTktRXRHaUxX?revision=7\"}"}}],"totalCount":5,"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:332057":{"__typename":"Conversation","id":"conversation:332057","topic":{"__typename":"TkbTopicMessage","uid":332057},"lastPostingActivityTime":"2024-08-12T01:59:45.036-07:00","solved":false},"AssociatedImage:{\"url\":\"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzIwNTctR2VMWkE4?revision=6\"}":{"__typename":"AssociatedImage","url":"https://community.f5.com/t5/s/zihoc95639/images/bS0zMzIwNTctR2VMWkE4?revision=6","title":"rag-enable-chatbot.jpg","associationType":"BODY","width":3208,"height":1608,"altText":""},"TkbTopicMessage:message:332057":{"__typename":"TkbTopicMessage","subject":"Secure, Deliver and Optimize Your Modern Generative AI Apps with F5","conversation":{"__ref":"Conversation:conversation:332057"},"id":"message:332057","revisionNum":6,"uid":332057,"depth":0,"board":{"__ref":"Tkb:board:TechnicalArticles"},"author":{"__ref":"User:user:171064"},"teaser@stripHtml({\"removeProcessingText\":true,\"truncateLength\":-1})":" In this demo, Foo-Bang Chan explores how F5's solutions can help you implement, secure, and optimize your chatbots and other AI applications. This will ensure they perform at their best while protecting sensitive data. One of the AI frameworks showed is Enterprise Retrieval-Augmented Generation (RAG). This demo leverages F5 Distributed Cloud (XC) AppStack, Distributed Cloud WAAP, NGINX Plus as API Gateway, API-Discovery, API-Protection, LangChain, Vector databases, and Flowise AI. ","introduction":"","metrics":{"__typename":"MessageMetrics","views":529},"postTime":"2024-07-31T05:00:00.034-07:00","lastPublishTime":"2024-07-31T05:00:00.034-07:00","body@stripHtml({\"removeProcessingText\":true,\"removeSpoilerMarkup\":true,\"removeTocMarkup\":true,\"truncateLength\":-1})":" Introduction \n I will never forget once my master taught me, when you build your house, build it on solid ground. If you build your house on sand, when a storm comes, your house will be destroyed, whereas if you build on solid ground, it can withstand storms.  \n This principle applies broadly - to life, business, and especially to AI strategy. A robust AI initiative requires solid groundwork. This foundation ensures your AI efforts are resilient, adaptable, and sustainable over time. Remember, AI implementation is a long-term strategic journey, not a quick fix. \n Start by laying strong foundations. This begins with a clear vision and well-defined objectives. A solid architecture is crucial for success, as is high-quality data - one of the most critical elements in AI. Your AI architecture, workflows, and pipelines all contribute to this foundation. \n F5 offers a robust platform and tools that enables you to secure, deliver, and optimize your generative AI and modern applications. 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