IIS X-Forward-For ISAPI Filter
A recent customer issue came up where they were load balancing servers but we unable to get the true client address logged in their IIS logs. They had their servers fronted by a BIG-IP and when clients would make requests the address passed to the server was the internal address and not that of the client. This is a common issue with proxies and fortunately there is a standard for forwarding client information. It is the HTTP X-Forwarded-For header which is handled by most proxies. So, I set out to find an existing ISAPI filter to replace the c-ip (client ip) log value in IIS with the contents of the X-Forwarded-For header (if it exists). I was amazed to find that I couldn't find a single instance of any open source (or even commercial) filter that would do this. So, I dug out Visual Studio and whipped up a filter that does just that. It's very basic and contains no user configuration so all you need to do is plug it into your Web Applications list of ISAPI Filters within the IIS Administration and you're set to go. We've released the source under the iControl End User License Agreement (available in any iControl SDK download). You can download it here. If you find a way to optimize this filter, please let me know and I'll update the sources here. After 24-hours of posting, a customer already returned some performance testing on the filter indicating that it only effected the traffic by less than 1 percent. I'm sure there are ways to optimize the memory allocation in the filter to speed this up a bit more, but I'll leave that for the community to work on. Oh, and it should be noted that the X-Forwarded-For header isn't supported the same way across all proxy products so you'll want to make sure you test this out before using it. It is expecting the header to only contain an IP Address as it does a straight substitution on the value in the c-ip section of the log entry. Enjoy! -JoeThree Things I've Learned Recently While Deploying F5 CIS on OpenShift
Not every CIS deployment issue is actually a CIS issue. This article walks through three lessons learned from recent OpenShift deployments where the real culprit turned out to be NetworkPolicies, image selection, and version management rather than the controller itself.API Discovery and Enforcement with API Security Local Edition
API Security Local Edition is a self-hosted platform that discovers APIs from BIG-IP traffic insights, builds and maintains an inventory with risk scoring, and pushes enforcement back to BIG-IP. This article covers the architecture, the data flows between components, and the operator workflow from discovery to enforcement.
Introducing the F5 Application Study Tool (AST)
In the ever-evolving world of application delivery and security, gaining actionable insights into your infrastructure and applications has become more critical than ever. The Application Study Tool (AST) is designed to help technical teams and administrators leverage the power of open-source telemetry and visualization tools to enhance their monitoring, diagnostics, and analysis workflows.MinIO AIStor and F5 BIG-IP DNS – Globally steer and replicate your S3 object storage
A set of two complementary technologies were set out to be assessed, the first being MinIO’s active-active replication, which serves to keep buckets in sync across wide areas. This is more than object copying. It fully includes replication of delete operations, delete markers, existing objects, and replica metadata changes. As discussed in this blog, the ability exists to configure this across two or more sites, in interesting approaches like two data centers in one metro market all the way to a larger set spanning a continent; all are in play. As the blog indicates, the deliverable solution can be for multi-primary topologies, fast hot-hot failover, and multi-geo resiliency. The second technology, F5 BIG-IP DNS and LTM modules, can impose control over the path to these active-active scenarios. The ambitious requirements surrounding global server load balancing (or GSLB, for short) is directly in BIG-IP’s wheelhouse. The fully qualified domain names (FQDN) of vast sets of S3 buckets can now be put under the purview of BIG-IP. S3 users might be delivered to data centers filled with MinIO AIStor clusters using a simple round-robin approach, or perhaps a strategy where one data center is considered live, while another is ready for a hot standby switchover, in the event that network impairments arise. This is only the start of the possibilities. What about a strategy where topological knowledge is unleashed, say American users in the Atlanta region are steered to an east coast MinIO data center, say New York City, while all bucket data is immediately then synchronized to a west coast data center, perhaps in Los Angeles? A lab setup for learning All of the geographic traffic steering capabilities can become a rabbit hole, the only limiting factor is often the imagination of the solution architect. Take one final suggested and sequenced approach, first topology is used based upon the source addresses of incoming DNS queries. The idea could be to steer user traffic to “pools” of data centers on a continental -basis: traffic from users in North America is first filtered to a North American picklist of sites, Europeans to EMEA locations, perhaps Asian users to Asia Pac data centers. Things then get really interesting at the next layer, although again topology can be leaned on, BIG-IP DNS can also be instructed to slowly poll users’ local DNS resolvers over time, such that future requests for service from, say, Atlanta as an example, again, would receive a solution which knows that the round-trip response times from New York are actually and demonstrably quicker than Los Angeles and result in that being the first criteria used to steer S3 to its optimal data center cluster. The following was the objective of the lab’s setup, a two cluster AIStor solution, multiple 4-disk AIStors in each data center’s cluster. Although replication can be synchronous or asynchronous, the latter is a better fit in cases where distance between participating data centers is significant. To introduce latency reflective of North American coast-to-coast normal values, WAN latency was emulated in the lab and an asynchronous replication between buckets was selected. A key take away from the diagram is the administration component, the so called “Corporate Headquarters” and the fact that it is not collocated with storage. It does however, have authoritative control over DNS domains in use. Also, note the sample S3 consumers may be located anywhere, and latency to each data center will be unique. The MinIO AIStor active-active setup in a nutshell The MinIO blog post referenced earlier takes a user through an easy-to-follow GUI-based approach to setting up the clusters for replication, however the command-line mcli approach is also valid. The MinIO documentation site can be found here and covers the replication topic in general. The key takeaways for anyone standing up an environment like that described in this article: The bare minimum for erasure coding, a foundational part of MinIO’s data resiliency story, is 4 drives. I have used 2 servers with 4 drives, for 8 drives, per site. Ample bandwidth between sites, in my lab I have 100 Mbps between emulated sites. Buckets to be included in an active-active replication approach must have both versioning and object-locking enabled when creating them, matching identical buckets and permissions should be set up at all other participating data centers. In this lab setup, the fictious organization is byteboutique.io, a distributed organization with MinIO storage in multiple locations, allowing B2B partners to access via S3 buckets line of business material such as “datasheets”, “product-videos” and “sales-orders-inventory”. When creating the buckets with the AIStor GUI, such as for a new bucket “sales-reports”, simply ensure versioning and object-lock are requested. Versioning allows objects touched at one location to be kept in sync with the versions accessible at all locations, even deleted objects are simply versioned and retained under the hood for future usage. Once this is performed at the clusters in each participating site, the next step is quite straightforward. Simply group select the buckets in question and feed AIStor the information about the desired replication. The last step, after pressing the button above, is to set up the replication parameters to initiate communications with the other AIStor site in the lab. At this point, objects delivered into either site’s buckets, using perhaps graphical tools like S3browser, will be replicated to the same bucket in other data centers. The next requirement is how can we use the F5 technology to provide a universal naming convention, as both humans and business automated routines prefer DNS names over static IP addresses. We want an S3 application to write to byteboutique.io’s buckets with knowledge that the content will go to one MinIO site, any site. It could even be done in a round-robin manner. The beauty of the active-active angle is that the automated backend replication work is shielded from the user. Beyond this, we can take things one step further and have the BIG-IP use context such as source IP awareness or on-going network response measurements to guide that S3 traffic to the best possible landing site. Global load balancing S3 traffic with BIG-IP DNS and LTM – infrastructure setup We can adjust our diagram to introduce the two F5 components required to meet the lab objectives. Each emulated data center will have one BIG-IP, as a minimum these appliances will have the local traffic manager (LTM) module licensed. LTM allows incoming S3 transactions to be load balanced per selected algorithm to AIStor nodes local to that site. The “least connections” algorithm is a popular choice for heavy S3 traffic flow. The received S3 traffic will be both new, user-initiated requests as well as traffic generated by AIStor clusters themselves to achieve a perpetually replicated state amongst sites. The other component to be licensed is the DNS module. This will allow global traffic steering and need not be on all BIG-IP appliances. It can co-habitate nicely with the LTM module, so perhaps some data center-housed BIG-IPs will use it, as well as BIG-IP appliances that might already exist in other areas, such as in a corporate headquarters. The minimum number of BIG-IP DNS appliances is two, but for production more would be recommended. In our lab setup, the headquarters Windows server is the authoritative DNS server for our fictious byteboutique.io domain. What we can quickly do is delegate control over the sub-domain corp.byteboutique.io to our BIG-IP DNS appliances. In other words, we will create DNS Name Server (NS) resource records for the “corp” sub-domain which point to the BIG-IPs. This is the critical cog in the wheel. All S3 accessible buckets will use DNS names below corp and are thus fully under the control of the BIG-IP administrator. Other approaches that can retain existing domain names and put them under the control of BIG-IP DNS would include using CNAME DNS resource records. In the following image, we see that the delegated corp domain has NS resource records added, and that looking at the main byteboutique.io resource records, there are A resource records pointing to IP addresses dedicated to DNS on both the HQ and East data center BIG-IPs (30.0.0.12 and 40.0.0.12). We are halfway home. Now we just need to see the key parameters of a BIG-IP DNS configuration. There is both regular F5 documentation on the DNS solution here and also a very handy lab guide here that graphically provides every step towards a sample classroom set up. To simply hit on the main tasks, the BIG-IP DNS appliances must be set to join a common DNS “group”, the name “F5DEMO_group” is used in the following lab setup screenshot. This means BIG-IPs in the DNS group can share content like zone files and collectively control where S3 traffic lands. The impactful part to you? After joining the BIG-IP DNS appliances with the “gtm_add” command, you will only ever need to create new FQDN values (such as an S3 service at name storage.corp.byteboutique.io) on just one BIG-IP DNS and all others in the group will be adjusted accordingly behind the scenes. Phew. So, with DNS administration, just set it and forget it on any BIG-IP member of your choice. Beneath the surface, F5’s iQuery protocol is in play to keep DNS members coordinated automatically. The only other command-line task in this whole endeavor is to issue “bigip_add” at each appliance, LTM, DNS, or LTM/DNS. This will let the device’s certificates be trusted by other appliance peers and allow secure communications between each. The next task is to create logical holding entities for our locations, simply and intuitively called “Data Centers”. As such, we will need entries for our diagram’s east site, west site, and headquarters. The last step of this one-time infrastructure setup phase is to add “servers” at each site. These correspond to all BIG-IP appliances, including LTM only appliances, which serve to load balance to AIStor nodes. The nice part, you ask? A discovery feature includes all the currently configured virtual servers at each site, so the task is simply adding to the server list and choosing a health check to be run in the background. Here is our server list. Notice the virtual server count has been populated, including the hq site which being licensed only for the DNS module, understandably has no virtual servers. Tying it all together - modern traffic steering for MinIO S3 buckets with F5 BIG-IP Before answering the age-old question, what precisely is a “Wide IP” anyways, let us settle on some term clarity first. For anyone with a background in BIG-IP LTM, or any on-prem load balancer, a pool normally means a set of local origin servers “behind” the load balancer. These might be Linux appliances with Nginx webservers, Windows-based server applications based around IIS, or in our case, MinIO AIStor servers offering S3 API-compatible object storage services. In the world of GSLB there are actually two tiers of pools, the first tier of pool allows groups of data centers, not individual origin servers, to be selected by one of the many available algorithms. Consider this as an example, when first selecting a pool for a mock web application, named myapp.global.example.com. In the above example, created purely for illustration, incoming requests for the domain name myapp.global.example.com would be round robin directed to either data centers in the Americas, or Asia or Europe regions. In reality, a topology-based load balancing method, not round robin, would likely be invoked in the top “Load Balancing Method” pull down. The key point is to highlight that each region might have a half dozen data centers, or more, each equipped with BIG-IP virtual servers ready to handle application traffic delivered there. This is a very reasonable first pool-level approach you might use, and the FQDN in the example (myapp.global.example.com) is referred to as a “Wide IP”. A Wide IP, or WIP for short, is an F5 DNS construct that maps names to pools first, and then to individual sites (housing virtual servers) second. In our lab, our Wide IP to get S3 transactions to MinIO AIStor nodes, is “storage.corp.byteboutique.io”. We can see we have just one pool, as denoted by the arrow. Perhaps think of this as a North America-only scenario, but a solution that is ready to be rolled out internationally when byteboutique really takes off and expands to the world. Drilling down by clicking on our WIP, we see the one pool, and observe two “members”, meaning two virtual servers are associated to this pool. This is a quick shorthand count of sorts, we know we are looking at a solution where the WIP resolves to one of two possible MinIO-equipped data centers. Interestingly, since our lab is using one pool, the actual load balancing method at this layer is moot. Round robin is seen in the above screenshot however any other mechanism of selecting from a pool set of one will, of course, not be impactful. However, by clicking onto the pool itself, we get to the heart of the actual decision-making logic in our lab setup. The following screen will dictate what IP address, meaning what site’s S3 virtual server IP address, will be delivered to a client’s local DNS resolver upon request (double-click to enlarge). We see that the two sites of our lab, east and west, are both represented in the virtual server “Member” list of our pool. The status is green, as both virtual servers are, within the two respective data centers, evaluating perpetually that the AIStor servers for in good (green) health. This is a subtle but powerful feature of BIG-IP GSLB versus normal DNS, we can see “behind” the load balancer in our two sites and ensure traffic will never be sent to a site that is having issues communicating with a sufficient number of backend S3 nodes. Perhaps you will want to think of this as "intelligent" DNS. The other major takeaway is the load balancing logic. This is a simple, perhaps “fast start” approach. We are using a static round-robin algorithm, when DNS A resource record (RR) requests are delivered to either BIG-IP DNS appliance, since they are authoritative for *.corp.byteboutique.io, the IPs of the two virtual servers will be utilized in responses, in a round robin manner. DNS has time to live (TTL) values in responses, so any local DNS resolver is sure to ask again over time, and generally, unless we choose persistence, our solution will serve each virtual server equally over time. Tiered traffic steering logic - dynamic load balancing A common design approach is to have a dynamic load balancing approach as “Preferred” and a more foolproof static approach as the alternate. You can see the tiered load balancing strategies of preferred, alternate and fallback in the previous screenshot. A good example is the idea of Round Trip Time, a dynamic attempt to measure latency from both potential data centers to the local DNS resolver. Generally, this favors the outcome that the DNS A resource record response for storage.corp.byteboutique.io will be the “closer” data center. Perhaps if network conditions and network media are alike, a user in Atlanta will be steered to AIStor clusters in New York, as opposed to Los Angeles, due to terrestrial propagation delays of crossing a continent. The “Alternate” option is best served by a static approach. In case the polling of a local DNS server is not yet in place or not properly working due to firewalls, a static choice like Round Robin can be used as the alternate. A common static approach is to use “Topology”, examine the source IP of an S3 client’s local DNS resolver and use IP network connectivity knowledge to deduce which of the two data centers is likely fewer IP network hops away. A couple of last notes on this past screenshot, what exactly is the Fallback IP for? In our scenario, it is possible that both active round trip measurements and static source IP analysis fail to come to a best data center choice. This is where Fallback comes into play. In my example, I have the IP address of the “East” data center S3 virtual server hardcoded as the Fallback (40.0.0.100). This gives our solution the assurance that a completely valid answer, even if not the optimal answer, will always be available from DNS. Also, you may note that we talk in terms of the client’s local DNS resolver source IP address, why not the source address of the user itself? This is the nature of DNS. Clients do not normally recursively engage with the global DNS infrastructure, that role is deferred to a configured DNS resolver. There is often no issue with this, if the S3 consumer is an office-bound application server itself, reading and writing to MinIO S3 storage. The local DNS resolver is very likely co-located. There are scenarios where a DNS resolver is not collocated. Think of a split tunnel VPN connection and you are using a vendor's global S3 services; your laptop’s corporate (VPN) DNS service may engage the world from another state or country, but the resulting S3 traffic may flow directly from the S3 cluster to your actual location with split tunneling. In such cases, workarounds exist with BIG-IP DNS, such as the use of the EDNS0 option, which strives to carry actual client source IP information into the DNS realm. A quick test of our AIStor and BIG-IP lab To see if our solution works, we will just use basic round robin global load balancing. For completeness, let’s look at the actual last leg of load balancing, when one of the virtual servers in either data center receives S3 transactions from clients. Our lab setup looks like the following, highlighting the west location, immediately followed by a glimpse of the “west” BIG-IP’s virtual server setup and its local pool of AIStor nodes. There are numerous GUI and command line approaches to generate S3-API compliant traffic, ideas are FileZillaPro, CyberDuck and Curl commands to name but a few. In this example I have used the S3Browser utility which even on the free account tier has many useful features. To evaluate the lab setup, we will instruct S3Browser to connect to the FQDN of storage.corp.byteboutique.io on TCP port 9000. It is recommended in production to use user level, not admin level as I have S3 access credentials. As noted, TLS is set to “off” but can easily be supported by both BIG-IP and AIStor. A potential performance-focused move would be to utilize TLS as far as the BIG-IP and then offload S3 TLS to pure HTTP within the boundaries of the datacenter. This may not be an option for security-first advocates who put a premium on end-to-end encryption over storage solution scalability. Once we connect, we see a list of buckets that have been entered into the active-active replication arrangement. Within the “sales-orders-inventory” bucket we see three files, the user is not bothered with what precise data center provided the object list displayed. The user now uploads a file, a simple file upload button is present, and loads a new file into the bucket. Within seconds, looking at sample AIStor nodes in the east and west, we can confirm that the bucket instances have all been updated in both clusters. To validate the BIG-IP GSLB solution is operating as intended, beyond the net effect on the storage experience which we see is working, multiple interesting views are available within the BIG-IPs themselves. The first expectation would be, as per our two Name Server (NS) DNS resource records, we would expect both the headquarters and east office appliances to be consulted relatively equally over time. As we see in the following screenshot, with east on top and headquarters below, that is the case (double-click to enlarge). Now, to double click on the east BIG-IP, we have seen 82 queries for storage.corp.byteboutique.io but have the two virtual servers in the pool been offered up as destinations in near equal amounts? The answer is yes; the static round robin algorithm seems to have worked. Since the preferred load balancing algorithm for the defined pool is a static, faultless one, round robin, as expected there are no instances of an alternate or fallback approach being required. A next step for closer approximation to a production environment, would be to introduce a dynamic algorithm, perhaps round trip time, to demonstrate our lab S3 user who experiences lower latency to the east data center can leverage this fact and be served by the replicated cluster in the closer east data center. Summary MinIO is a thought leader in S3-API compatible storage, both for single site applications and for distributed clusters. There is an appetite in this space for active-active replicated solutions, where different S3 users can interact with any given instance and know that the totality of the storage offering is being kept in sync. BIG-IP plays two key supportive roles in this equation, the first of which is the LTM module. A local S3 load balancing function, which distributes transactions, whether reads or writes, and can optimally distribute load against all AIStor nodes in the cluster. Hot spot avoidance is paramount at this level. The second role BIG-IP offers is through the DNS module, where global traffic steering can connect any S3 user to any one of a set of AIStor sites. Job one here is resiliency of the solution, where any data center being offline temporarily can be circumvented by control of DNS. Other aspects were touched upon in this article. S3 traffic steering based upon topological information. The knowledge accrued by studying the source of DNS requests and the expected network hop count to the closest data center is one frequently used approach. Basic Geo-IP information is another route, simply direct traffic from, say, EU nations to an EU pool of data centers and other worldwide traffic to the closest site based upon IP maps. Dynamic methods were also touched upon in the discussion. A logical use case would be a round-trip latency approach, where repetitive queries from a given local DNS resolver allow this source to be polled from various BIG-IP equipped MinIO data centers over time. Thus, future requests can be directed at the expected fastest target. Finally, it was mentioned that a cascade of load balancing algorithms could be used, dynamic decision making first, followed second by an alternate static approach, like a topology database. A final fallback IP address provided to a BIG-IP virtual server on the largest site to catch corner cases is a logical approach.You Don't Have to Have Played to Understand the Game
Andy Reid barely played football. He was a community college tackle who transferred to BYU and then rode the bench for most of his time in Provo. Teammates remember him as the guy in the film room, not the guy on the field. He spent his Saturdays watching, taking notes, and pestering head coach LaVell Edwards with so many questions about strategy that Edwards eventually told him: Kid, you should coach. That’s the origin story of three Super Bowl wins for my local Kansas City Chiefs, six appearances across the Chiefs and Eagles, and one of the winningest coaches in NFL history. Not a stud player who worked his way down to the sideline, but a guy who asked a lot of questions, kept asking them, and turned that into a career. Richard Williams had never picked up a tennis racket in his life when he saw Virginia Ruzici win a tournament on TV in 1978 and decided his daughters were going to be world champions. He taught himself the sport from books and instructional videos and then wrote and implemented a 78-page plan for coaching Venus and Serena on the public courts in Compton when they were very young. Thirty Grand Slam singles titles between them later and the “you have to have played at the highest level to coach at the highest level” theory was looking pretty thin. Nobody looks at Reid's three Super Bowl rings and says “yeah, but did you really understand it without playing in the league?” Nobody tells Richard Williams his daughters’ Grand Slam titles have an asterisk because he learned the game from a VHS tape. We accept, in sports, that there’s more than one way to know a thing. Somehow that grace evaporates the second AI enters the conversation. Doing isn't understanding There’s a flavor of pushback on AI use that goes something like: “you have to do it manually first to really understand it.” Sometimes that’s gatekeeping in a wise-elder’s costume. Sometimes it’s a genuine concern. An experienced person who built their intuition the hard way, watching newer folks skip the grind, and worrying (not unreasonably) that the intuition won’t form. But “doing it manually” and “understanding it” aren’t the same thing. They overlap, but they’re not the same thing. You can grind through a problem manually for years and still not understand the system around it. And you can understand a system deeply without having implemented every piece of it yourself, if you’re willing to ask enough questions. The questioning is the work Here’s the part I think people miss when they’re worried about AI making us dumber: A lot of what an expert does for you, when you’re lucky enough to have one, is answer questions patiently. Over and over. Sometimes the same question is phrased three different ways because you didn’t quite get it the first time. Sometimes a dumb question that you’d be embarrassed to ask on a Slack channel. Good mentors don’t get tired of this. But there are very few good mentors. They’re busy, and you only get so many of them in a career. I’ve been at this for thirty years now and I can count the great mentors I’ve had on one hand. LLMs don’t get tired. They don’t sigh. They don’t make you feel stupid for asking why something works the way it does for the fourth time. And the act of formulating the question, asking "what exactly am I confused about?" and "what do I need to know to clear the fog?” That’s a huge chunk of where understanding actually comes from. The model is a sparring partner for your own thinking, if you let it be one. Use it as a vending machine and you’ll get exactly that: answers, not understanding. The tragic version of LLM use is the one where someone pastes the problem, takes the answer, ships it, and walks away no smarter than they started. Then does it again the next day. And the next. Building a career out of outputs they couldn’t reproduce or defend if you took the tool away. That's the version the skeptics are right about. It just isn’t the only version. Andy Reid didn’t need to have been a pro-bowl tackle to understand offensive football. He needed to watch carefully, ask the right questions, and think rigorously about what he was seeing. Richard Williams didn’t need to have been on tour. He needed books, tapes, and the willingness to do the homework. Playing at the highest level is one path to understanding. But for systemic thinking, tactical thinking, architectural thinking, it might not always be the best one. Two things I learned this week First: I’m working on a side project where the FastAPI Cloud backend runs as a two-instance replica deployment. I started on SQLite, which worked fine until I realized writes were landing in whichever instance happened to handle the request, leaving me with two file-based databases with immediate data drift. I moved to a serverless Postgres database (Neon) to give both instances a single source of truth, and once I was there, realized I could just point dev and prod at the same data. Yes, in a real production system this is an anti-pattern and I’d never recommend it. But for a small project where I’m iterating fast and the bottleneck is my own understanding of the problem, not having to migrate data back and forth every time I want to test a frontend change or hunt down a bug? Game changer. I got there by talking the tradeoffs through with my good friend Claude. What breaks, when it breaks, what the actual risk surface looks like at my scale. Nobody handed me a "here's when to break the rule" tutorial. I asked questions until I understood the rule well enough to break it on purpose. Second: I'm building an on-box tool for BIG-IP (article coming soon), and I hit the HA problem. How do I keep state synced across boxes? My first instinct was file-based storage on the host, which, it turns out, is exactly where AS3 and SSL Orchestrator started. SSLO went a step further and built a dedicated sync layer called gossip to keep those files coordinated across the cluster. Over time, both products converged on a different approach: data-groups for metadata and iFiles for larger payloads, both of which ride along with standard config sync. That's a much smaller surface area to maintain, and it leans on infrastructure the platform already guarantees. So I'm following the same path: metadata in data-groups, data blobs in iFiles. I figured this out by interrogating Claude about how those products were architected, why they made the choices they did, and what the failure modes were. I could have read the source, and I could have tried to track down the developers and architects (and I should have over dinner to get the inside scoop). But the speed of “ask, get an answer, ask the next question, get an answer” let me sketch the whole design space in an afternoon. That's not skipping the understanding. That’s building it. Get off whose lawn? I get the resistance. Some of it is "get off my lawn." Some of it is genuine expertise feeling devalued. Some of it is real fear about what this technology means for the people who come up behind us. None of those concerns are stupid. The people who built their understanding the hard way, by tinkering, by breaking things, by reading source code under duress at 2am because there was no other way to get the system back online? They are not wrong about the value of that path. They earned something real in all that trial by fire. Some of them are the best engineers I know. The intuition that comes from years of manual struggle is a kind of literacy that doesn’t have a shortcut, and the people who have it are the ones I most want in the room when something goes sideways. But I’d push back on the specific claim that you must do every step manually to understand the thing. You don’t. Engage with it seriously. Ask real questions and chase the answers until they hold together. Be willing to be wrong. Notice when you’re wrong, and update accordingly. Used well, an LLM doesn’t dull that loop. It tightens it. The design decision, the tradeoff, the bet, I’m getting to that part of the problem sooner than I would have otherwise. Reid had Edwards. Williams had the library. The skeptics aren’t wrong that some understanding only comes from doing. They’re wrong that this is one of them.F5 BIG-IP Virtual Patching With Web App Scanning Results
F5 Distributed Cloud Web App Scanning offers automated penetration testing capabilities for web applications and APIs. The scanning methodology is designed to address vulnerabilities as defined by the OWASP Top 10 for web apps and LLMs, ensuring robust coverage against commonly exploited risks and emerging threats. Following each scan, the tool generates a detailed report, which serves as a valuable resource for defining and enhancing your F5 security policies. For more information about Web App Scanning, visit the official documentation. When paired with the BIG-IP Advanced WAF, F5 Distributed Cloud Web App Scanning allows you to protect applications from a wide range of attacks, including those that exploit known vulnerabilities. By integrating the two solutions, vulnerabilities identified during scans can be automatically exported to BIG-IP Advanced WAF to apply virtual patches, providing seamless security enhancements for your applications. This video demonstration walks you through the process of exporting vulnerabilities detected by F5 Distributed Cloud Web App Scanning to a service secured by BIG-IP Advanced WAF (AWAF). With this integration, you can apply targeted virtual patches to endpoints in your applications. The key steps demonstrated include: Using the Vulnerability Assessment Policy Template: Begin by creating a baseline security policy in BIG-IP Advanced WAF, leveraging its integration with F5 Distributed Cloud Web App Scanning. Integrating Vulnerability Details: The output from F5 Distributed Cloud Web App Scanning can be imported, providing suggested updates to your security policy that specifically address the vulnerabilities identified during the scan. Custom Vulnerability Handling: Select which vulnerabilities should be addressed by the security policy according to your application’s requirements. Retesting the Security Policy: Re-run the Web App Scan to validate that the enhanced security policy effectively protects against the previously identified vulnerabilities. For more information on exporting vulnerability scan results from F5 Distributed Cloud Web App Scanning to BIG-IP Advanced WAF, visit the official documentation.
F5 BIG-IP Zero Trust Access
Introduction F5 BIG-IP Zero Trust Access, a key component of the F5 Application Delivery and Security Platform (ADSP), helps teams secure apps that are spread across hybrid, multi-cloud and AI environments. In this article, I’ll highlight some of the key features and use cases addressed by BIG-IP Zero Trust Access. F5 BIG-IP Zero Trust Access improves security and the user experience while managing access to your portfolio of corporate applications. Demo Video What is Zero Trust? Key Zero Trust Concepts Zero Trust is a cybersecurity framework built on the following core concepts: Never Trust Similar to human concepts that trust is not given freely, it is earned Always Verify Authenticate and authorize based on all available data points Continuously Monitor Zero Trust is an ongoing security framework that requires monitoring F5 enables zero-trust architectures that optimize your investments and extend zero-trust security across your entire portfolio. Why is this important? Securing apps is complex because apps are spread across a hybrid, multi-cloud environment. Apps themselves have become hybrid in nature, too. This creates 2 problems: Legacy and custom applications can complicate access security. Apps residing anywhere increases the attack surface. F5 BIG-IP Zero Trust Access secures hybrid application access. Securely managing access to corporate applications is critical to preventing data breaches. Doing it well can also increase efficiencies in business processes and user productivity. A Zero Trust security model can deliver this business value by enabling users to seamlessly and securely access their applications from anywhere regardless of where the application resides. In today’s world of hybrid, multicloud and AI applications, Zero Trust is a must. Application access control is key to any Zero Trust architecture. How does F5 address Zero Trust? F5 Zero Trust Begins with Secure Access to All Apps. The F5 Application Delivery and Security Platform (ADSP) is the foundation for Zero Trust Architectures. F5 ADSP delivers visibility, enforcement, and intelligence where it matters most: the application layer. While there are many important components to Zero Trust, we will be focusing on Zero Trust Application Access: Identity-Aware Proxy - Secure access to apps with a fine-grained approach to user authentication and authorization that enables only per-request context- and identity-aware access. Single Sign-On (SSO) and Access Federation - Integrating with existing SSO and identity federation solutions, users can access all their business apps via a single login, regardless of if the app is SAML enabled or not. OAuth 2.0 and OIDC Support - Enable social login to simplify access authorization from trusted third-party identity providers like Google, LinkedIn, Okta, Azure AD, and others. Identity Aware Proxy (IAP) – A Key Component of Zero Trust Use the Guided Configuration to configure the Identity Aware Proxy. From the BIG-IP UI, go to Access > Guided Configuration > Zero Trust. Select the Identity Aware Proxy You will see a configuration example of Identity Aware Proxy Click Next at the bottom For the Config Properties, give it a name, “IAP_DEMO” in this example Set the below options to On Click Save & Next Enable the F5 Client Posture Check Select your CA Trust Certificate Click Add Give it a Name, “FW_Check” in this example Under Windows, select Firewall and Domain Managed Devices Enter your domain name, “f5lab.local” in this example Click Done Click Save & Next Configure the Virtual Server Properties Switch Advanced Settings to On Set the Destination Address, “10.1.10.100” in this example For the Client SSL Profile, select the Client SSL Certificate, Private Key and Trusted Certificate Authorities For the Server SSL Profile, select your Server SSL Certificate and Private Key Click Save & Next Click Add under Authentication Give it a Name, “AD” in this example Set the Authentication Type to “AAA” Set the Authentication Server Type to Active Directory Choose your Authentication Server, “ad-servers” in this example Check the box for Active Directory Query Properties Under Required Attributes, find “memberOf” and click the arrow to move it to Selected Click MFA Click Add Double click Radius Under Choose Radius Server, select Create New Give it a name, “radius_pool” in this example Enter the Server IP Address, “10.1.20.8” in this example Enter the Secret in the two fields Click Save Click Save & Next Click Add Give it a name, “basic_sso” in this example For the SSO Configuration Object, click Create New The Username Source and Password Source should be set like the following Click Save Click Save & Next Under Applications click Add Give it a name, “iap1.acme.com” in this example Under Application Properties, set the FQDN and Caption to “basic.acme.com” Set the Pool IP Address to 10.1.20.7, Port 443, HTTPS Click Save For the Auth Domain, enter “iap1.acme.com” Click Save & Next Set Primary Authentication to “AD” Click Save & Next Click Add under Contextual Access For the Contextual Access Properties, give it a name, “basic.acme.com” in this example Set the Resource to iap1.acme.com Set Device Posture to FW_CHECK Set Single Sign-On to basic_sso Find the Sales Engineering Group and click Add Select the box for Additional Checks Set the Match Action to Step Up Set Step Up Authentication to Custom Radius based Authentication Click Save & Next The Remediation Page must be changed to a real host where users can download and install the EPI updates In this example, it has been changed to “https://iap1.acme.com/epi/downloads” Click Save & Next Click Save & Next Click Deploy Click Finish when the deployment completes Test the functionality by going to a client computer and accessing https://iap1.acme.com Logon with valid credentials You should see a page like the following Click basic.acme.com Login with valid credentials & click Validate You should see the basic.acme.com web page and be already logged in Note: If you disable the Windows Firewall on the client, you should get a block page similar to the following: Conclusion BIG-IP introduces a powerful access experience. BIG-IP provides a variety of Authentication, Federation, SSO and MFA protocols allowing for modern to legacy protocol translation. BIG-IP integrates with 3 rd parties to enforce identity aware decisions. BIG-IP secures identities for any apps and users anywhere in legacy and modern environments, spanning on-prem, hybrid or cloud locations. The highly scalable and proven Access Security solution that F5 customers know and trust. Related Content Zero Trust Solution Overview Secure Corporate Apps with a Zero Trust Security Model BLOG: F5 BIG-IP Zero Trust Access Zero Trust Application Access for Federal Agencies
