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.
Automating ACMEv2 Certificate Management on BIG-IP
While we often associate and confuse Let's Encrypt with ACMEv2, the former is ultimately a consumer of the latter. The "Automated Certificate Management Environment" (ACME) protocol describes a system for automating the renewal of PKI certificates. The ACME protocol can be used with public services like Let's Encrypt, but also with internal certificate management services. In this article we explore the more generic support of ACME (version 2) on the F5 BIG-IP.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.
Modern Applications-Demystifying Ingress solutions flavors
In this article, we explore the different ingress services provided by F5 and how those solutions fit within our environment. With different ingress services flavors, you gain the ability to interact with your microservices at different points, allowing for flexible, secure deployment. The ingress services tools can be summarized into two main categories, Management plane: NGINX One BIG-IP CIS Traffic plane: NGINX Ingress Controller / Plus / App Protect / Service Mesh BIG-IP Next for Kubernetes Cloud Native Functions (CNFs) F5 Distributed Cloud kubernetes deployment mode Ingress solutions definitions In this section we go quickly through the Ingress services to understand the concept for each service, and then later move to the use cases’ comparison. BIG-IP Next for Kubernetes Kubernetes' native networking architecture does not inherently support multi-network integration or non-HTTP/HTTPS protocols, creating operational and security challenges for complex deployments. BIG-IP Next for Kubernetes addresses these limitations by centralizing ingress and egress traffic control, aligning with Kubernetes design principles to integrate with existing security frameworks and broader network infrastructure. This reduces operational overhead by consolidating cross-network traffic management into a unified ingress/egress point, eliminating the need for multiple external firewalls that traditionally require isolated configuration. The solution enables zero-trust security models through granular policy enforcement and provides robust threat mitigation, including DDoS protection, by replacing fragmented security measures with a centralized architecture. Additionally, BIG-IP Next supports 5G Core deployments by managing North/South traffic flows in containerized environments, facilitating use cases such as network slicing and multi-access edge computing (MEC). These capabilities enable dynamic resource allocation aligned with application-specific or customer-driven requirements, ensuring scalable, secure connectivity for next-generation 5G consumer and enterprise solutions while maintaining compatibility with existing network and security ecosystems. Cloud Native Functions (CNFs) BIG-IP Next for Kubernetes enables the advanced networking, traffic management and security functionalities; CNFs enables additional advanced services. VNFs and CNFs can be consolidated in the S/Gi-LAN or the N6 LAN in 5G networks. A consolidated approach results in simpler management and operation, reduced operational costs up to reduced TCO by 60% and more opportunities to monetize functions and services. Functions can include DNS, Edge Firewall, DDoS, Policy Enforcer, and more. BIG-IP Next CNFs provide scalable, automated, resilient, manageable, and observable cloud-native functions and applications. Support dynamic elasticity, occupy a smaller footprint with fast restart, and use continuous deployment and automation principles. NGINX for Kubernetes / NGINX One NGINX for Kubernetes is a versatile and cloud-native application delivery platform that aligns closely with DevOps and microservices principles. It is built around two primary models: NGINX Ingress Controller (OSS and Plus): Deployed directly inside Kubernetes clusters, it acts as the primary ingress gateway for HTTP/S, TCP, and UDP traffic. It supports Kubernetes-native CRDs, and integrates easily with GitOps pipelines, service meshes (e.g., Istio, Linkerd), and modern observability stacks like Prometheus and OpenTelemetry. NGINX One/NGINXaaS: This SaaS-delivered, managed service extends the NGINX experience by offloading the operational overhead, providing scalability, resilience, and simplified security configurations for Kubernetes environments across hybrid and multi-cloud platforms. NGINX solutions prioritize lightweight deployment, fast performance, and API-driven automation. NGINX Plus variants offer extended features like advanced WAF (NGINX App Protect), JWT authentication, mTLS, session persistence, and detailed application-layer observability. Some under the hood differences, BIG-IP Next for Kubernetes/CNF make use of F5 own TMM to perform application delivery and security, NGINX rely on Kernel to perform some network level functions like NAT, IP tables and routing. So it’s a matter of the architecture of your environment to go with one or both options to enhance your application delivery and security experience. BIG-IP Container Ingress Services (CIS) BIG-IP CIS works on management flow. The CIS service is deployed at Kubernetes cluster, sending information on created Pods to an integrated BIG-IP external to Kubernetes environment. This allows to automatically create LTM pools and forwarding traffic based on pool members health. This service allows for application teams to focus on microservice development and automatically update BIG-IP, allowing for easier configuration management. Use cases categorization Let’s talk in use cases terms to make it more related to the field and our day-to-day work, NGINX One Access to NGINX commercial products, support for open-source, and the option to add WAF. Unified dashboard and APIs to discover and manage your NGINX instances. Identify and fix configuration errors quickly and easily with the NGINX One configuration recommendation engine. Quickly diagnose bottlenecks and act immediately with real-time performance monitoring across all NGINX instances. Enforce global security polices across diverse environments. Real-time vulnerability management identifies and addresses CVEs in NGINX instances. Visibility into compliance issues across diverse app ecosystems. Update groups of NGINX systems simultaneously with a single configuration file change. Unified view of your NGINX fleet for collaboration, performance tuning, and troubleshooting. NGINX One to automate manual configuration and updating tasks for security and platform teams. BIG-IP CIS Enable self-service Ingress HTTP routing and app services selection by subscribing to events to automatically configure performance, routing, and security services on BIG-IP. Integrate with the BIG-IP platform to scale apps for availability and enable app services insertion. In addition, integrate with the BIG-IP system and NGINX for Ingress load balancing. BIG-IP Next for Kubernetes Supports ingress and egress traffic management and routing for seamless integration to multiple networks. Enables support for 4G and 5G protocols that are not supported by Kubernetes—such as Diameter, SIP, GTP, SCTP, and more. BIG-IP Next for Kubernetes enables security services applied at ingress and egress, such as firewalling and DDoS. Topology hiding at ingress obscures the internal structure within the cluster. As a central point of control, per-subscriber traffic visibility at ingress and egress allows traceability for compliance tracking and billing. Support for multi-tenancy and network isolation for AI applications, enabling efficient deployment of multiple users and workloads on a single AI infrastructure. Optimize AI factories implementations with BIG-IP Next for Kubernetes on Nvidia DPU. F5 Cloud Native Functions (CNFs) Add containerized services for example Firewall, DDoS, and Intrusion Prevention System (IPS) technology Based on F5 BIG-IP AFM. Ease IPv6 migration and improve network scalability and security with IPv4 address management. Deploy as part of a security strategy. Support DNS Caching, DNS over HTTPS (DoH). Supports advanced policy and traffic management use cases. Improve QoE and ARPU with tools like traffic classification, video management and subscriber awareness. NGINX Ingress Controller Provide L4-L7 NGINX services within Kubernetes cluster. Manage user and service identities and authorize access and actions with HTTP Basic authentication, JSON Web Tokens (JWTs), OpenID Connect (OIDC), and role-based access control (RBAC). Secure incoming and outgoing communications through end-to-end encryption (SSL/TLS passthrough, TLS termination). Collect, monitor, and analyze data through prebuilt integrations with leading ecosystem tools, including OpenTelemetry, Grafana, Prometheus, and Jaeger. Easy integration with Kubernetes Ingress API, Gateway API (experimental support), and Red Hat OpenShift Routes F5 Distributed Cloud Kubernetes deployment mode The F5 XC k8s deployment is supported only for Sites running Managed Kubernetes, also known as Physical K8s (PK8s). Deployment of the ingress controller is supported only using Helm. The Ingress Controller manages external access to HTTP services in a Kubernetes cluster using the F5 Distributed Cloud Services Platform. The ingress controller is a K8s deployment that configures the HTTP Load Balancer using the K8s ingress manifest file. The Ingress Controller automates the creation of load balancer and other required objects such as VIP, Layer 7 routes (path-based routing), advertise policy, certificate creation (k8s secrets or automatic custom certificate) Conclusion As you can see, the diverse Ingress controllers tools give you more flexibility, tailoring your architecture based on organization requirements and maintain application delivery and security practices across your applications ecosystem. Related Content and Technical demos BIG-IP Next SPK: a Kubernetes native ingress and egress gateway for Telco workloads F5 BIG-IP Next CNF solutions suite of Kubernetes native 5G Network Functions Deploy WAF on any Edge with F5 Distributed Cloud Announcing F5 NGINX Ingress Controller v4.0.0 | DevCentral JWT authorization with NGINX Ingress Controller My first CRD deployment with CIS | DevCentral BIG-IP Next for Kubernetes BIG-IP Next for Kubernetes (LA) BIG-IP Next Cloud-Native Network Functions (CNFs) CNF Home F5 NGINX Ingress Controller Overview of F5 BIG-IP Container Ingress Services NGINX OneF5 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
Mitigation of OWASP API Security Risk: BOPLA using F5 XC Platform
Introduction: OWASP API Security Top 10 - 2019 has two categories “Mass Assignment” and “Excessive Data Exposure” which focus on vulnerabilities that stem from manipulation of, or unauthorized access to an object's properties. For ex: let’s say there is a user information in json format {“UserName”: ”apisec”, “IsAdmin”: “False”, “role”: ”testing”, “Email”: “apisec@f5.com”}. In this object payload, each detail is considered as a property, and so vulnerabilities around modifying/showing these sensitive properties like email/role/IsAdmin will fall under these categories. These risks shed light on the hidden vulnerabilities that might appear when modifying the object properties and highlighted the essence of having a security solution to validate user access to functions/objects while also ensuring access control for specific properties within objects. As per them, role-based access, sanitizing the user input, and schema-based validation play a crucial role in safeguarding your data from unauthorized access and modifications. Since these two risks are similar, the OWASP community felt they could be brought under one radar and were merged as “Broken Object Property Level Authorization” (BOPLA) in the newer version of OWASP API Security Top 10 – 2023. Mass Assignment: Mass Assignment vulnerability occurs when client requests are not restricted to modifying immutable internal object properties. Attackers can take advantage of this vulnerability by manually parsing requests to escalate user privileges, bypass security mechanisms or other approaches to exploit the API Endpoints in an illegal/invalid way. For more details on F5 Distributed Cloud mitigation solution, check this link: Mitigation of OWASP API6: 2019 Mass Assignment vulnerability using F5 XC Excessive Data Exposure: Application Programming Interfaces (APIs) don’t have restrictions in place and sometimes expose sensitive data such as Personally Identifiable Information (PII), Credit Card Numbers (CCN) and Social Security Numbers (SSN), etc. Because of these issues, they are the most exploited blocks to gain access to customer information, and identifying the sensitive information in these huge chunks of API response data is crucial in data safety. For more details on this risk and F5 Distributed Cloud mitigation solution, check this link: Mitigating OWASP API Security Risk: Excessive Data Exposure using F5 XC Conclusion: Wrapping up, this article covers the overview of the newly added category of BOPLA in OWASP Top 10 – 2023 edition. Finally, we have also provided minutiae on each section in this risk and reference articles to dig deeper into F5 Distributed Cloud mitigation solutions. Reference links or to get started: F5 Distributed Cloud Services F5 Distributed Cloud WAAP Introduction to OWASP API Security Top 10 2023Post-Quantum Cryptography: Building Resilience Against Tomorrow’s Threats
Modern cryptographic systems such as RSA, ECC (Elliptic Curve Cryptography), and DH (Diffie-Hellman) rely heavily on the mathematical difficulty of certain problems, like factoring large integers or computing discrete logarithms. However, with the rise of quantum computing, algorithms like Shor's and Grover's threaten to break these systems, rendering them insecure. Quantum computers are not yet at the scale required to break these encryption methods in practice, but their rapid development has pushed the cryptographic community to act now. This is where Post-Quantum Cryptography (PQC) comes in — a new wave of algorithms designed to remain secure against both classical and quantum attacks. Why PQC Matters Quantum computers exploit quantum mechanics principles like superposition and entanglement to perform calculations that would take classical computers millennia2. This threatens: Public-key cryptography: Algorithms like RSA rely on factoring large primes or solving discrete logarithms-problems quantum computers could crack using Shor’s algorithm. Long-term data security: Attackers may already be harvesting encrypted data to decrypt later ("harvest now, decrypt later") once quantum computers mature. Figure1: Cryptography evolution How PQC Works The National Institute of Standards and Technology (NIST) has led a multi-year standardization effort. Here are the main algorithm families and notable examples. Lattice-Based Cryptography. Lattice problems are believed to be hard for quantum computers. Most of the leading candidates come from this category. CRYSTALS-Kyber (Key Encapsulation Mechanism) CRYSTALS-Dilithium (Digital Signatures) Uses complex geometric structures (lattices) where finding the shortest vector is computationally hard, even for quantum computers Example: ML-KEM (formerly Kyber) establishes encryption keys using lattices but requires more data transfer (2,272 bytes vs. 64 bytes for elliptic curves) The below figure shows an illustration of how Lattice-based cryptography works. Imagine solving a maze with two maps-one public (twisted paths) and one private (shortest route). Only the private map holder can navigate efficiently Code-Based Cryptography Based on the difficulty of decoding random linear codes. Classic McEliece: Resistant to quantum attacks for decades. Pros: Very well-studied and conservative. Cons: Very large public key sizes. Relies on error-correcting codes. The Classic McEliece scheme hides messages by adding intentional errors only the recipient can fix. How it works: Key generation: Create a parity-check matrix (public key) and a secret decoder (private key). Encryption: Encode a message with random errors. Decryption: Use the private key to correct errors and recover the message Figure3: Code-Based Cryptography Illustration Multivariate & Hash-Based Quadratic Equations Multivariate These are based on solving systems of multivariate quadratic equations over finite fields and relies on solving systems of multivariate equations, a problem believed to be quantum-resistant. Hash-Based Use hash functions to construct secure digital signatures. SPHINCS+: Stateless and hash-based, good for long-term digital signature security. Challenges and Adoption Integration: PQC must work within existing TLS, VPN, and hardware stacks. Key sizes: PQC algorithms often require larger keys. For example, Classic McEliece public keys can exceed 1MB. Hybrid Schemes: Combining classical and post-quantum methods for gradual adoption. Performance: Lattice-based methods are fast but increase bandwidth usage. Standardization: NIST has finalized three PQC standards (e.g., ML-KEM) and is testing others. Organizations must start migrating now, as transitions can take decades. Adopting PQC with BIG-IP As of F5 BIG-IP 17.5, the BIG-IP now supports the widely implemented X25519Kyber768Draft00 cipher group for client-side TLS negotiations (BIG-IP as a TLS server). Other cipher groups and capabilities will become available in subsequent releases. Cipher walkthrough Let's take the supported cipher in v17.5.0 (Hybrid X25519_Kyber768) as an example and walk through it. X25519: A classical elliptic-curve Diffie-Hellman (ECDH) algorithm Kyber768: A post-quantum Key Encapsulation Mechanism (KEM) The goal is to securely establish a shared secret key between the two parties using both classical and quantum-resistant cryptography. Key Exchange X25519 Exchange: Alice and Bob exchange X25519 public keys. Each computes a shared secret using their own private key + the other’s public key: Kyber768 Exchange: Alice uses Bob’s Kyber768 public key to encapsulate a secret: Produces a ciphertext and a shared secret Bob uses his Kyber768 private key to decapsulate the ciphertext and recover the same shared secret: Both parties now have: A classical shared secret A post-quantum shared secret They combine them using a KDF (Key Derivation Function): Why the hybrid approach is being followed: If quantum computers are not practical yet, X25519 provides strong classical security. If a quantum computer arrives, Kyber768 keeps communications secure. Helps organizations migrate gradually from classical to post-quantum systems. Implementation guide F5 published article Enabling Post-Quantum Cryptography in F5 BIG-IP TMOS to implement PQC on BIG-IP v17.5 Create a new Cipher Rule To create a new Cipher Rule, log in to the BIG-IP Configuration Utility, go to Local Traffic > Ciphers > Rules. Select Create. In the Name box, provide a name for the Cipher Rule. For Cipher Suits, select any of the suites from the provided Cipher Suites list. Use ALL or DEFAULT to list all of the available suites. For DH Groups, enter X25519KYBER768 to restrict to only this PQC cipher For Example: X25519KYBER768 For Signature Algorithms, select an algorithm. For example: DEFAULT Select Finished. Create a new Cipher Group In the BIG-IP Configuration Utility, go to Local Traffic > Ciphers > Groups Select Create. In the Name box, provide a name for the Cipher Group. Add the newly created Cipher Rule to Allow the following box or Restrict the Allowed list to the following in Group Details. All of the other details, including DH Group, Signature Algorithms, and Cipher Suites, will be reflected in the Group Audit as per the selected rule. Select Finished. Configure a Client SSL Profile In the BIG-IP Configuration Utility, go to Local Traffic > Profiles > SSL > Client. Create a new client SSL profile or edit an existing. For Ciphers, select the Cipher Group radio button and select the created group to enable Post-Quantum cryptography for this client’s SSL profile. NGINX Support for PQC We are pleased to announce support for Post Quantum Cryptography (PQC) starting NGINX Plus R33. NGINX provides PQC support using the Open Quantum Safe provider library for OpenSSL 3.x (oqs-provider). This library is available from the Open Quantum Safe (OQS) project. The oqs-provider library adds support for all post-quantum algorithms supported by the OQS project into network protocols like TLS in OpenSSL-3 reliant applications. All ciphers/algorithms provided by oqs-provider are supported by NGINX. To configure NGINX with PQC support using oqs-provider, follow these steps: Install the necessary dependencies sudo apt update sudo apt install -y build-essential git cmake ninja-build libssl-dev pkg-config Download and install liboqs git clone --branch main https://github.com/open-quantum-safe/liboqs.git cd liboqs mkdir build && cd build cmake -GNinja -DCMAKE_INSTALL_PREFIX=/usr/local -DOQS_DIST_BUILD=ON .. ninja sudo ninja install Download and install oqs-provider git clone --branch main https://github.com/open-quantum-safe/oqs-provider.git cd oqs-provider mkdir build && cd build cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local -DOPENSSL_ROOT_DIR=/usr/local/ssl .. make -j$(nproc) sudo make install Download and install OpenSSL with oqs-provider support git clone https://github.com/openssl/openssl.git cd openssl ./Configure --prefix=/usr/local/ssl --openssldir=/usr/local/ssl linux-x86_64 make -j$(nproc) sudo make install_sw Configure OpenSSL for oqs-provider /usr/local/ssl/openssl.cnf: openssl_conf = openssl_init [openssl_init] providers = provider_sect [provider_sect] default = default_sect oqsprovider = oqsprovider_sect [default_sect] activate = 1 [oqsprovider_sect] activate = 1 Generate post quantum certificates export OPENSSL_CONF=/usr/local/ssl/openssl.cnf # Generate CA key and certificate /usr/local/ssl/bin/openssl req -x509 -new -newkey dilithium3 -keyout ca.key -out ca.crt -nodes -subj "/CN=Post-Quantum CA" -days 365 # Generate server key and certificate signing request (CSR) /usr/local/ssl/bin/openssl req -new -newkey dilithium3 -keyout server.key -out server.csr -nodes -subj "/CN=your.domain.com" # Sign the server certificate with the CA /usr/local/ssl/bin/openssl x509 -req -in server.csr -out server.crt -CA ca.crt -CAkey ca.key -CAcreateserial -days 365 Download and install NGINX Plus Configure NGINX to use the post quantum certificates server { listen 0.0.0.0:443 ssl; ssl_certificate /path/to/server.crt; ssl_certificate_key /path/to/server.key; ssl_protocols TLSv1.3; ssl_ecdh_curve kyber768; location / { return 200 "$ssl_curve $ssl_curves"; } } Conclusion By adopting PQC, we can future-proof encryption against quantum threats while balancing security and practicality. While technical hurdles remain, collaborative efforts between researchers, engineers, and policymakers are accelerating the transition. Related Content New Features in BIG-IP Version 17.5.0 K000149577: Enabling Post-Quantum Cryptography in F5 BIG-IP TMOS F5 NGINX Plus R33 Release Now Available | DevCentral
