Behind the Scenes of Immersive One: How Lab Builder is a Game-Changer for Cyber Readiness
“The best customer feedback we got was, we don’t need you to do everything for us.” Rebecca: Wait, seriously? Matt: Never more so. We built Lab Builder, a powerful Immersive One platform feature, for a reason: Organizations need a way to create, maintain, and publish their own labs—fast. Historically, our cyber team built every lab in Lab Forge. That worked for us, but it locked out partners and customers who wanted to address their unique needs—tools, environments, policies, and threats. They asked, “Can we do more by ourselves?” Lab Builder answers that. Rebecca: Absolutely. Since its debut last fall, Lab Builder has evolved so much. What are the biggest benefits for users today? Matt: There are two big ones. The first is the ability to curate content relevant to your organization, its threats, and its technologies—quickly. Second, realism. Teams can safely train with real-world code, internal apps, or even live malware—all in a secure, disposable environment. It’s as close to production as it gets, giving them an authentic experience with the exact tools and systems they use every day. The result? Faster upskilling, stronger readiness, and measurable gains in resilience. | “With Lab Builder, your organization can turn any piece of code or policy into hands-on training—instantly.” Rebecca: That almost sounds too easy—until you see the demo in action for yourself! Matt: Yes! The team is super proud of how streamlined it is. The core workflow is just five steps. Create a new lab. Then configure it, adding basics like the lab title, intro, learning outcomes, and gamification aspects like points users will get for completing the lab. From there, the focus is on building the briefing and tasks—drag-and-drop questions, code-review challenges, “find the flaw”, there’s lots to choose from. Then you’re ready to publish. Just assign your SME as Lab Creator, and they’re live on the same Immersive One platform they use every day. Rebecca: To me, the genius of that kind of design is in its accessibility. You’ve also invested heavily in the learner experience. Matt: Definitely. Engagement is a top priority, so we’re always building for the learners who need to know cyber—not only to support their organization’s cyber resilience strategy, but to grow professionally. We aim to remove every barrier to learning we can. Rebecca: I know you’re not one to boast, but how has the team modernized the UI? Matt: We’ve done a lot of work here, starting with completely rebuilding the theory labs interface, giving it a clean, responsive design that works seamlessly across desktop, tablet, or phone. We’ve been building toward that for our practical labs experience too. There are now intuitive panels, seamless task navigation, and instant feedback to guide learners every step of the way. We also turned Lab Builder into a true WYSIWYG (“what you see is what you get”) editor so Lab Creators see exactly what their teams will experience. We also baked in full WCAG compliance, with keyboard navigation and screen-reader support on every screen. Ultimately, we hope to bring every Immersive-built and custom lab into a single, unified front end in Immersive One—so no matter where you are, one clear interface delivers the same training experience. Rebecca: It’s this kind of due diligence that really makes a difference! Let’s talk about VMs. How do they fit in? Matt: Theory can only take you so far, so we released practical labs in the spring. Organizations spin up their own virtual machines in AWS (Amazon Web Services), share the AMI with our account, and import it straight into Lab Builder. Why does that matter? Because learners train in their exact production environments—internal tools, real vulnerable code, compliance setups. We support typical instance sizes—enough CPUs and RAM for most use cases. While we may roll out more environments in the future, AWS supports 99% of our existing customer base. That means the process feels native for Immersive One users; they can truly learn by doing. | “Build your VM. Import it. Assign it. All without leaving Immersive One.” Rebecca: Initially, only Org Admins could build custom labs. How did you expand access so that more team members could create labs, without being given full admin rights? Matt: We introduced the Lab Creator role this summer. Now you pick who builds labs—no full admin rights required. Rebecca: So smart. Now, in-house experts can build content without getting the keys to the kingdom. Matt: 100%. The team is laser-focused on leveling up Immersive One with every Lab Builder release because it’s so instrumental to the customer experience. In June, we added video support right in the briefing panels because some individuals learn best through short, engaging clips. We also rolled out a Machine Library stocked with pre-configured VMs—basically templates, from Kali Linux to reverse-engineering rigs—so you can drop assets in and start training immediately with zero VM building. You just need t And internal publishing functionality lets Lab Creators bundle custom labs into collections and share them across Organizations—think a self-service marketplace for specialized, high-value content. Rebecca: Honestly, Matt, what you’ve done with Lab Builder functionality is incredible. And I know you’re not done yet. Matt: Yeah, right—not at all. The team is already heads-down on an AI agent, but I won’t spoil it! Rebecca: Love that! Well, thanks again for meeting with me today, Matt. Maybe next time we can discuss ways customers or partners are already using Lab Builder to meet their unique needs—you know, deep-dive into specific use cases. Matt: Oh, absolutely—happy to share what customers are using Lab Builder for. Maybe we even host a webinar for that one? Would be fun to entertain some Q&A. Rebecca: Nice, let’s plan on it! Final Thought Lab Builder not only powers a customer-first UI and UX, it can help you transform every new threat, policy change, or internal tool into an interactive lab—on your timeline, with your exact requirements. Want to explore the possibilities? Contact your Account Manager for a personalized introduction to this powerful feature. In the meantime, preview how easy it is to customize learning by watching this quick demo: Meet Lab BuilderBuilding Your First Practical Lab (Part 2)
This is the second blog in a 2 part series that will walk you through the entire process of building your first custom practical lab. You’ll learn how to do everything from launching and configuring an EC2 instance in your AWS account to imaging it and seamlessly integrating it into our platform. In part 1 we showed you how to create and import your own machine. You can read part 1 here. In this blog, we’ll walk through building a simple Linux privilege escalation scenario as a working example. Our goal is to give you the foundational steps so you can confidently design scenarios tailored to your own creativity, environment, and organizational needs. The lab objective Ensure you are connected to the machine via the Ubuntu user for the steps below and not our lab user (lab-user). The objective of this lab is to read a token file. To do so, the user will need to escalate privileges via a misconfiguration. We will create a flag.txt file inside /root/ that contains a string that the user must read in the lab. sudo nano /root/flag.txt Add some content inside the file. This will act as a flag that can be used later to complete the lab. w3ll_don3_h4ck3r Save the file The lab challenge Now let’s set up the challenge! The goal is for lab-user to find a way to read the /root/flag.txt which is owned by root and not accessible to the lab-user by default. They will do this by exploiting a world-writable script that is executed as the root user in cron job. Create a directory to hold the script that lab-user can exploit. For this example, it's going to be a simple script that outputs the current time to a file (not very creative). sudo mkdir /opt/date_printer This script will be executed by root, but lab-user will have write permissions to it. The initial content will be benign, but the purpose of this lab is for the lab-user to identify the misconfiguration that allows them to modify it to read the /root/flag.txt file to retrieve the flag. Create a file for the script: nano /opt/date_printer/printer.sh Add the following content: #!/bin/bash echo "Running date_printer: $(date '+%Y-%m-%d %H:%M:%S')" >> /var/log/date.log Save the file. Next, set the misconfigured permissions that allow lab-user to write to the script, enabling privilege escalation. sudo chmod +x /opt/date_printer/printer.sh sudo chown root:root /opt/date_printer/printer.sh sudo chmod 666 /opt/date_printer/printer.sh Additionally, we want to configure the folder to ensure root owns it, but other users on the machine have access to it. sudo chown root:root /opt/date_printer sudo chmod 777 /opt/date_printer Now, let’s add a cron job to run the script we just created. For this scenario, we are going to edit the /etc/crontab file. Cron jobs in this file are generally used for system-wide cron tasks and are readable by anyone. This is good as it adds some breadcrumbs to our lab! If the user reads this file (a common check when looking for privilege escalation on Linux), they will see a script gets run every minute, and it will point them to investigate that script file. Edit the file nano /etc/crontab Add the following line at the end of the file. This line tells cron to execute /opt/date_printer/printer.sh every minute, as the root user * * * * * root /opt/date_printer/printer.sh Save the file. At this point, we have a configured image with a low-privilege lab-user account, which we will use to connect to the lab machine. We also have a cronjob vulnerability that our users attempting the lab have to exploit as the lab-user! For this lab, all the user has to do is find the script that is run by the cronjob and edit it to print the token in the file we added at /root/flag.txt. They could do this by easily updating the /opt/date_printer/printer.sh script to replace the contents with #!/bin/bash cat /root/flag.txt >> /var/log/date.log This one-liner will cat the contents of the /root/flag.txt file to the /var/log/date.log file, which the user can then read to get the token (there are other things we could do here as well, but for the purposes of this lab, let's keep it simple). Imaging and sharing the lab AMI Go back to the EC2 dashboard and find the running instance you just configured. Right-click on the EC2 machine, select Image and templates, and then Create image. Image name: Provide a descriptive name, e.g., “MyFirstCyberLab-AMI” or “Linux-PrivEsc-Lab-AMI”. Image description: Add a brief description, e.g., “Custom lab with lab-user password SSH and cron job privesc scenario.” Leave other settings at their default values. Click Create image. This will now create an AMI from the configured EC2 lab machine. Adding your custom AMI to your lab Navigate to Lab Builder and go to your custom lab via Manage > Create Lab. If you haven’t created one yet, go ahead and do so by selecting Create a new custom lab. On the Lab details page, we can give our lab a name and configure various other settings. For the purposes of this example, we’ll call it Linux CTF Challenge, and we’ll fill out the rest of the information to ensure our users know what the lab is all about. Lab description: This is a Linux CTF machine designed to test your ability in privilege escalation! Estimated Time Required: 30 Minutes Difficulty: 3 Learning outcomes: Understand how to exploit a common Linux misconfiguration What’s involved: Investigate the machine and find the misconfiguration that allows for privilege escalation. Next, we want to fill in the briefing panel. The briefing panel is the learning material that lets our lab users understand a bit about the topic and anything else they need to know to answer the questions. Since this is a CTF, we’ll give them limited information: Linux CTF This is a CTF lab scenario designed to test your ability to exploit a common misconfiguration in Linux that could result in privilege escalation. Your task in this lab is to read a flag located at /root/flag.txt. Good luck! Next, we want to add a Task. Tasks are what the user has to solve to complete the lab. For this example lab, we want to add a question to verify that they’ve read the flag in the /root/flag.txt file. Select Add task, which will bring up a library of task types. From the library, select Question. This will add a question to the lab task list, which we can then edit by selecting Edit. Update the question settings to the following: Question text: What is the flag found in the /root/flag.txt file? Answer: w3ll_don3_h4ck3r The next stage is to import our custom image. Select Systems and then click Add under the Virtual machine—EC2 type. This will add a new machine to your lab. Once the machine has been added, we want to configure it. Selecting Edit at the top right will open the machine's configuration editor. In the blue information box, we provide which region and, most importantly, which AWS account to share your image with so that our platform can use it in a lab. Within your own AWS account where you created your AMI for the lab image, click on the AMI, and at the bottom of the screen, you will see Permissions. Select Edit AMI permissions and Add account ID. This will open a box where you enter the Account ID that is displayed in Lab Builder. Click Share AMI. Now, copy the AMI ID of the machine you just shared and add it to the Lab Builder machine AMI ID section: Set the following configuration for the other sections in this editor: System Name: Your chosen name for the system you’re configuring. For this example, let's call it “Linux Machine”. Instance Type: t3.medium Connection Type: SSH Username: lab-user (or the username you set) Password: lab-user (or the password you set) Once you’ve configured your system, you can easily use it in Lab Builder by selecting Preview System on the system view. Assuming you’ve built everything correctly, you’ll get a shiny preview of your newly configured machine! This is a good time to run through your lab scenario to ensure it's working correctly. And that’s it—congratulations on building your first practical lab! At this point, you can spruce up your lab by adding additional questions or details to the briefing panel and publish your lab to your organization for them to enjoy. This powerful new feature puts the control directly in your hands, allowing you to create incredibly specific and challenging learning environments. These range from simple privilege escalation scenarios like this one to complex, multi-machine attack simulations. We can’t wait to see the innovative labs you'll create. In the meantime, if you need more ideas or support, use our Help Centre docs for Lab Builder.Building Your First Practical Lab (Part 1)
This is the first blog in a 2 part series that will walk you through the entire process of building your first custom practical lab. You’ll learn how to do everything from launching and configuring an EC2 instance in your AWS account to imaging it and seamlessly integrating it into our platform. In this blog, we’ll walk through the process of creating and importing your own machines. In part 2 we’ll walk you through building a simple Linux privilege escalation scenario as a working example. Our goal is to give you the foundational steps so you can confidently design scenarios tailored to your own creativity, environment, and organizational needs. Building your machine in AWS The first step is to log into your AWS account and provision your first EC2 machine to be configured. Access to AWS will depend on your organization and its access rules (e.g., logging in via console.aws.amazon.com). Provision an EC2 Once logged in, the first step is to launch an EC2 instance. Search for “EC2” in the top search bar and click “EC2” under Services. In the EC2 Dashboard, click on “Launch instance”. The lab will use a standard Linux distribution. It’s recommended to start with a clean slate. Select Ubuntu Server 22.04 LTS (HVM), SSD Volume Type, or a similar recent Ubuntu LTS version. This is a widely used and well-documented distribution. It’s also in the free tier, which means we can keep costs as low as possible. The next step is to select the instance type. Lab Builder supports t3.micro, t3.medium, and t3.large. You can select whichever CPU type and associated resources your machine needs in the lab that best suits your needs. Since this is a reasonably straightforward Linux privilege escalation lab, we are going to use t3.micro. Now, we’ll need to configure the instance details. You can generally leave most of these settings as the default for the current purpose. Network: Ensure your default VPC and a subnet are selected. Auto-assign Public IP: Check this is enabled so you can SSH into the instance from the internet and configure it. Security Groups control the machine's inbound and outbound access. Since you need to configure the machine, make sure you have SSH open to SSH into it (alternatively, you can use AWS Systems Manager to control access without the need for SSH or direct network access). When using Security Groups, it's a good practice only to allow sources from trusted IPs. Select “Create Security Group.” Select Allow SSH traffic from For the source, it is recommended to select a trusted IP range or your own IP. The storage you’ll need will depend on the machine you’re building. However, the default storage (8 GiB General Purpose SSD gp2) is usually sufficient unless you're installing and configuring large applications. Note: We do not currently support encrypted EBS volumes. Now you’re ready to launch your machine! Review all your settings carefully and click Launch. You’ll be prompted to add an SSH key to the machine. You’ll use this key to connect to the instance and configure it. To create a new key pair, choose a name like my-lab-key and click Download Key Pair. Keep the downloaded .pem file secure—you’ll need it to SSH into your instance. If you’re using an existing key pair, select it. Once you have your key sorted, select Launch Instance. Your EC2 instance will now start to launch. It might take a few minutes for it to be ready. Configuring the instance for use as a lab Once your EC2 machine is launched and ready, it’s time to connect to and configure it, ready to be used in a custom lab! Connect to the running instance via SSH, using the key you downloaded previously when you provisioned the EC2. ssh -i <key>.pem ubuntu@<public_ip></public_ip></key> Note: You may need to change the permissions on the key first by running chmod 400 .pem Next, update the system packages. sudo apt update sudo apt upgrade -y Note: Depending on the future labs you create, you may not want to update system packages if you need specific versions of software or libraries installed. Setting up SSH for the lab user With Lab Builder, labs can be configured to have a session open when the lab loads. This session can be SSH, RDP, or HTTP. For this lab, we want the lab to load in the platform with the lab user already SSH’d into the machine, so they can get to work on the scenario straight away without needing to worry about connecting to a machine themselves. Many default Ubuntu AMIs disable SSH password authentication and only allow key-based authentication by default, but this can be changed. Password authentication needs to be enabled to allow our lab user to connect with a password (Lab Builder does not support key-based authentication). To do this, we can update the configuration for SSH. In the examples below, we use nano, but you can use whichever file editing tool you are comfortable with (Vi/Vim, etc.) sudo nano /etc/ssh/sshd_config Find the line that says: #PasswordAuthentication no or PasswordAuthentication no. We want to change this to allow our users to connect via a password: PasswordAuthentication yes Make sure ChallengeResponseAuthentication no is set to no (it usually is). Save the file. While this will work for most distributions, certain images provided by AWS can have additional config for SSH that needs to be changed. For our image that we are using in this example (Ubuntu Server 22.04 LTS (HVM), SSD Volume Type), we also need to change the file at the location below: /etc/ssh/sshd_config/60-cloudimg-settings.conf Open this file and make the same change we did for the other sshd_config file PasswordAuthentication yes For the changes to SSH to take effect, restart the SSH service: sudo systemctl restart ssh Next, let's create a dedicated user account for our lab participants. sudo adduser lab-user When prompted: Enter a password that will be used for lab-user (make sure you remember this, as we will need it later!) Re-enter the password to confirm. You can press Enter for all the full name, room number, etc., prompts. Confirm with Y when asked if the information is correct. Note: We recommend changing the password above to something unique to you and this machine. At this point, we recommend logging out of the existing SSH session as the Ubuntu user and logging in as the newly created lab-user to verify that the SSH configuration has been updated and that you can successfully connect to the machine via password-based SSH authentication. ssh lab-user@<ec2-ip></ec2-ip> You should be prompted to enter the user's password, which will connect you to the machine. ssh lab-user@52.18.126.144 lab-user@52.18.126.144's password: >Welcome to Ubuntu 22.04.5 LTS (GNU/Linux 6.8.0-1029-aws x86_64) >lab-user@ip-172-31-17-115:~$ Ready to create the lab challenge? Read part 2 here.Making the Most of the Custom Lab Builder: Tone of Voice
Now you can build your own labs in the Custom Lab Builder, we thought we’d provide some guidance on writing with a strong tone of voice to ensure your labs are as engaging as possible. This blog is the third in a series on making the most of the Lab Builder, looking at what we call the Four Cs. Ensuring your writing is… Conversational Concise Conscious Consistent The previous two posts looked at accessibility and inclusivity. This post focuses on tone of voice and how to write authentically to ensure your audience engages with the lab and remembers the message you’re trying to teach them. Writing well For most of your life, you’ve probably been told to write properly. Avoid contractions at all costs. Use complex sentences with plenty of fancy connecting words like “furthermore” and “moreover”. And never start a sentence with “and”. This formal style works really well for some industries. Academia is traditionally an incredibly formal area when it comes to the written word, as is the broadsheet newspaper realm. This is often to reflect the work’s sincerity, to avoid weakening a writer’s reputation, and to present ideas consistently and objectively. But Immersive Labs believes writing can be sincere and objective without being so... dull! Be conversational Copywriting is increasingly conversational, appearing everywhere from LinkedIn posts to the back of your milk carton. This style engages readers by feeling personal and authentic, aligning with Richard Mayer’s Personalization Principle, that people learn more deeply when words are conversational rather than formal. A human-to-human copywriting style makes sense for Immersive Labs, as we’re all about focusing on the humans behind the screens. When using the Lab Builder, we recommend writing your labs in an engaging, approachable style to create a modern, user-friendly learning environment. But conversational doesn’t mean sloppy. It’s about presenting ideas clearly and confidently, helping users feel at ease while they learn. Use everyday, concrete language Using fancy, complex words doesn’t make content better – it can actually distract readers and undermine clarity. Instead, prioritize clear, straightforward language to ensure your message is easy to understand, especially by users with cognitive disabilities. Avoid overly poetic phrases, figures of speech, idioms, or ambiguous language, which can confuse or overwhelm readers, including those with autism spectrum conditions. Strive for clarity to help users grasp your message the first time, keeping their needs front and centre. Address the reader Authenticity is all about gaining your reader’s trust. We recommend speaking directly to them in your custom labs by using “you” throughout your copy. This handy trick also avoids any ambiguity when it comes to practical tasks. Take the following example. “In this lab, the machine must be analyzed and IoCs must be extracted.” Instead of being vague and passive, we recommend talking directly to the reader and telling them exactly what they need to do. “In this lab, you need to analyze the machine and extract IoCs.” Or better yet, you can be even more direct by cutting that down even further: “In this lab, analyze the machine and extract IoCs.” Our labs and scenarios frequently talk directly to the reader. Users are more likely to stay engaged when they’re spoken to, not at. Use contractions Contractions instantly make your writing more conversational by mimicking natural speech. Combining words like "it is" to "it’s" or "you are" to "you’re" adds a touch of informality that feels approachable and inclusive. While once discouraged in formal writing, contractions are ideal for a modern learning environment, making text easier to read, understand, and remember. Be concise Writing in plain language is good for all users, but can make a massive difference for neurodivergent users, those who struggle to focus, those who hyperfocus, or maybe those who find reading difficult. We follow recommendations from the Advonet Group, the British Dyslexia Association, and Clark and Mayer’s Coherence Principle to ensure accessibility for a diverse audience – and you should too! Writing simply and clearly doesn’t mean trivializing content or sacrificing accuracy; it just makes your message easier to understand. After all, no one's ever complained that something's too easy to read! The difficulty comes when balancing this with technical content. How can you make advanced, complex cybersecurity topics clear and concise? Keep it short and sweet Sentences longer than 20 words become difficult to understand and can detract from the point being made. It’s easy for people’s minds to wander, so get to your point in as few words as possible. The same goes for paragraphs. Try and avoid long, dense walls of text. Nobody wants to read that, and it’s no good when thinking about accessibility. Keep your paragraphs to four or five lines, maximum. Get to the point Avoid adding unnecessary side notes to your labs, as they can distract from the main message and make learning harder. Unnecessary content distracts the learner’s attention from the main message, making them less likely to remember the core topic. It disrupts the connections between key messages and diverts the learner’s focus, making it harder to piece together the bigger picture. This is all down to cognitive load theory, which says that in general, humans can handle around four pieces of new information at any one time. To help users focus, stick to the lab's core topic and avoid overloading them with unrelated details. TL;DR When writing your labs with the Custom Lab Builder, ensure all your text is conversational to engage your users with the topic. And also make all your copy as concise as possible. Getting your message across in as few words as possible will reduce cognitive overload, boredom, and frustration. By focusing on being conversational, as well as being consistent and conscious (as we covered in the previous blog posts in this series), your readers will engage with your content better, remember the topic, and be able to put it into practice more easily – improving their cybersecurity knowledge and driving their cyber resilience. Share your thoughts! What do you think about these tone of voice tips when writing your custom labs? Have you tried to write your labs in a conversational yet concise way, and how did this go down with your users? Do you have any other suggestions for the community on how to write conversationally? We’d love to hear from you!Making the Most of Custom Lab Builder: A Guide to Writing Inclusively for All
Language shapes how people perceive and engage with content, so it’s crucial to consider the kind of words you use. Using outdated terminology can offend and disengage learners, as well as hurt a company’s reputation. This blog is the second in a series on making the most of the Lab Builder, looking at what we call the Four Cs. Ensuring your writing is… Conscious Consistent Conversational Concise The previous post in this series looked at accessibility. In this post, we’ll explore what it means to write consciously and inclusively, share practical tips, and show how our platform supports this critical effort. Why is inclusive language important? Inclusive language avoids bias, respects diversity, and ensures accessibility for all. In cybersecurity, it means using terms that foster collaboration and trust, avoiding outdated or harmful phrases, and creating welcoming and empowering content The Quality Team at Immersive Labs is committed to staying up to date with how language changes in the cyber industry. We regularly undertake research and speak to other industry professionals to ensure that our language is appropriate. Words to avoid We recommend avoiding specific terms that some people may find offensive, and some socially charged language that may have negative connotations. Non-inclusive language to avoid Preferred inclusive versions Whitelist/Blacklist Allowlist/Denylist White hat/Black hat hackers Ethical/Unethical hackers Master/Slave Leader/Follower, Primary/Replica, Primary/Standby Grandfathered Legacy status Gendered pronouns (e.g. assuming “he/him/his”) They, them, their Gendered pronouns (e.g. “guys”) Folks, people, you all, y’all Man hours, man power Hours, engineer hours, workforce, staffing Man-in-the-middle attack Machine-in-the-middle attack Sanity check Quick check, confidence check, coherence check Dummy value Placeholder value, sample value Crazy, insane Amazing, incredible, or any other appropriate adjective Socially charged words Preferred inclusive versions Native Built-in, default, pre-installed, integrated, core Abort Stop, cancel, end, force quit Cripple Disable, impair, damage, destroy, ruin Kill Stop, force quit, close, shut down Trigger Activate, initiate, cause, launch Unsure if a phrase you’ve used could be seen as offensive? Ask yourself: is this the most accurate and appropriate choice? Often, you can find a more descriptive word and avoid using these examples. Top tips for inclusive language Use writing tools Tools like Grammarly can help identify problematic words or phrases. You can create customized lists in Grammarly, which will then flag when a word has been used in your writing. Additionally, there are many inclusive language guides available online. Keep it short and sweet Use short sentences and paragraphs. Shorter sentences are easier to read, scan, and understand – especially for those with cognitive disabilities. Aim for sentences around 10–15 words, with variation for a natural flow. Avoid sentences longer than 20 words, as they can be harder to follow. Read aloud Proofread your work aloud to catch awkward phrasing, overly complex sentences, or insensitive terms. Hearing the words can help identify spots where clarity or tone might need improvement. Get a second opinion Ask a colleague to review your final version. A fresh set of eyes can spot language that might be unclear, inappropriate, or overly complicated. Share your thoughts Now that Lab Builder is here and you’ve had a chance to create your own content, how have you made your content more inclusive? We’re always looking to stay up to date, so if you have any further suggestions to add to our list of words to avoid or any other tips, let us know! We’d love to learn from you and grow the collective community knowledge.Making the Most of the Custom Lab Builder: Writing With Accessibility in Mind
What if someone tried to access your content who was visually impaired? Or who had cognitive difficulties? Or who was hard of hearing? Would they be able to understand the information you’ve provided and improve their cyber resilience? Our in-house copyediting team has created a series of articles to help you craft high-quality labs, aligned to the rigorous processes we follow. We embrace what we call the Four Cs to ensure all labs are: Consistent Conscious Conversational Concise These articles delve into each of these principles, showing how to implement them in your labs to create content that resonates with readers, enhances learning, and boosts cyber resilience. This post highlights how being conscious of your formatting can enhance accessibility for assistive technology users and how consistent formatting improves navigation for everyone. Rich text formatting Rich text formatting tools like subheadings, bullet points, lists, and tables in the Custom Lab Builder help organise information for easier scanning, better retention, and improved comprehension. Using these will ensure your content is consistent, accessible, and reader-friendly for everyone! Rich text formatting elements carry specific meaning, which assistive technologies rely on to convey information to specific users. Headings Visually, headings represent hierarchy through different font styling and allow users to quickly scan content. Programmatically, they allow users who can’t see or perceive the visual styling to access the same structural ability to scan. Heading elements should reflect the structure of the content. So your title should go in ‘Heading 1’ formatting, your next subheading will go in ‘Heading 2’ formatting, and so on. To ensure your content reads correctly to screen reader users, don’t use HTML heading styling to represent emphasis, and don’t use bold to make text appear like a heading. Lists (bullets/numbering) Always use bullets or numbered lists using the provided formatting to convey a list. A screen reader will announce that the following information is a list. Links How a link is formed significantly impacts usability. Consider the following sentence: “To find out more about this topic, complete our Intro to Code Injection lab here.” Links are interactive elements, which means you can navigate to them using the tab key. A user who relies on screen magnification to consume content may choose to tab through content to see what's available. The example above would be communicated as just “here”, which provides no context. They’d need to manually scroll back to understand the link’s purpose. Always use descriptive link text that clearly indicates its destination. Avoid ambiguous phrases like “here”. If that’s not possible, ensure the surrounding text provides clear context. “To find out more about this topic, complete our Intro to Code Injection lab.” Bold Only use bold for emphasis! Avoid italics, capital letters, or underlining (reserved for hyperlinks) to prevent confusion. Consistency in formatting reduces cognitive load, making your text more accessible. Bold stands out, provides better contrast, and helps readers quickly identify key information. Avoid italics With 15–20% of the population having dyslexia, italics are worth avoiding because research shows it’s harder for this user group to read italic text. Italics can sometimes bunch up into the next non-italic word, which can be difficult to comprehend or distracting to read. Media If you’re adding media to your labs, such as videos and images, it’s especially important to consider those who use assistive technologies. These users need to have the same chance of understanding the content as everyone else. They shouldn’t miss out on crucial learning. What is alternative text? Alt text describes the appearance and function of an image. It’s the written copy that appears if the image fails to load, but also helps screen reading tools describe images to visually impaired people. Imagine you’re reading aloud over the phone to someone who needs to understand the content. Think about the purpose of the image. Does it inform users about something specific, or is it just decoration? This should help you decide what (if any) information or function the images have, and what to write as your alternative text. Videos Any videos you add to your lab should have a transcript or subtitles for those who can’t hear it. Being consistent Consistency is a major thinking point for accessibility. We recommend adhering to a style guide so all of your labs look and feel consistent. We recommend thinking about the structure of your labs and keeping them consistent for easy navigation. In our labs, users expect an introduction, main content, and a concluding “In This Lab” section outlining the task. This helps users recognize certain elements of the product. It reduces distraction and allows easier navigation on the page. For example, some users prefer diving into practical tasks and referring back to the content if they need it. By using the same structure across your lab collections, your users will know exactly where to find the instructions as soon as they start. TL;DR It’s crucial to focus on accessibility when writing your custom labs. Utilise the built-in rich text formatting options in the Custom Lab Builder (and stay consistent with how you use them!) to ensure your labs are easy to navigate for every single user. By being conscious and consistent with your formatting, every user will engage with your content better, remember the topic, and be able to put it into practice more easily, improving their cybersecurity knowledge and driving their cyber resilience. No matter how they consume content. Keep your eyes peeled for the next blog post in this series, which will look at inclusive language. Share your thoughts! There’s so much information out there on creating accessible content. This blog post just focused on the language, structure, and current formatting options available in the Custom Lab Builder. Have you tried to make your labs or upskilling more accessible, and how did this go down with your users? Do you have any other suggestions for the community on how to write content with accessibility in mind? Share them in the comments below!From Feng Shui to Surveys: How User Feedback Shapes Immersive Labs
We’ve all been asked to give product feedback in one way or another – a pop-up message after completing a purchase, an email asking how your visit went, or a poll appearing on your social media feed. They all have one thing in common: a real person behind them, looking for valuable insights. I’m one of those people! My role as Senior UX Researcher involves speaking to Immersive users and gathering their feedback to help the company make tangible improvements. UX, or user experience, is at the heart of what I do. And it’s been around for longer than you might think. What is UX? It’s believed that the origins of UX began in 4000 BC with the ancient Chinese philosophy of Feng Shui, the spatial arrangement of objects in relation to the flow of energy. In essence, designing the most user-friendly spaces possible. A short skip to 500 BC, and you can see UX at play with the Ancient Greeks' use of ergonomic principles (also known as human factors), defined as “an applied science concerned with designing and arranging things people use so that the people and things interact most efficiently and safely.” In short, people have been concerned about creating great user experiences for thousands of years. How does Immersive get feedback? Bringing you back to the present day, let me walk you through a recent research study undertaken with Immersive Labs users and what their experiences and feedback led to. In May this year, we sent out a survey to our users asking them about their needs for customised content. The feedback was given directly to the team working on the feature, helping to inform their design choices and confirm or question any assumptions they had about user needs. In July, we invited users, including Training Manager and community member mworkman to take part in a pilot study for the Custom Lab Builder, giving them exclusive access to the first iteration of the feature. They could use the builder in their own time, creating real examples of custom labs using their own content and resources. This gave them a realistic experience and highlighted issues along the way. What does Immersive do with that feedback? In August, those users joined a call with us to provide their feedback and suggestions. From these calls, we gained insights and statistics that were presented to the entire Product Team, voicing our customers’ needs. We then used this to shape the direction of the lab builder feature before its release. Customers told us that they wanted to create labs based on their own internal policies and procedures, which would require more flexible question-and-answer formats for tasks. They also wanted more formatting options and the ability to add media to labs. In response to this feedback, we increased the number of task format types from three to five, and we’ll continue to add to this. We also added the ability to include multiple task formats in the same lab. Users also now have the option to upload images and include rich text within their custom labs, enhancing the layout and customisation experience. The Custom Lab Builder was released in October 2024 with an update pushed in December, and we’re still working on improving it! Throughout this first quarter of 2025, we’ve released more new features, including drag and drop, free text questions, and instructional tasks in the Lab Builder. How can you get involved? Once again, we’ll be calling on our users to give feedback on their experiences with these features, continuing to involve you in our design process to ensure that our products and experiences reflect what users are looking for. Throughout 2025, Immersive Labs will be providing opportunities for our users to come along to feedback sessions, have their opinions heard through surveys, and many more exciting chances to talk to the people behind the product. Follow our Community Forum for hot-off-the-press opportunities! For more guidance on Lab Builder, visit our Help Center.From Concept to Content: A Deep Dive into Building and Critically Analyzing Labs
Putting it all together The main bulk of the development work is building the labs. This usually comprises two parts that require different skill sets; one is putting together the written portion of the lab (such as the briefing, tasks, and outcomes), and the other is implementing any technical needs for the practical side of the lab. While some labs may focus more on one component than the other, this general overview of lab development will demonstrate each step of the process. Developing written content Regardless of the lab, the written content forms the backbone of the educational material. Even with prior knowledge and planning, additional research is essential to ensure clear explanations. Once research is complete, an outline is drafted to focus on the flow, ensuring the information is presented logically and coherently. This step helps enhance the final product. The final step is turning the outline into the final written content. Everyone approaches this differently, but personally, I like to note all the points I want to cover in a bullet list before expanding on each one. This method ensures all necessary information is covered, remains concise and clear, and aligns with learning outcomes and objectives. Technical implementation For practical labs, technical setup is key. Practical tasks should reinforce the theoretical concepts covered in the written portion, helping users understand the practical application of what they’ve learned. Before implementing anything, the author decides what to include in the practical section. For a CTI lab on a vulnerability, the vulnerable software must be included, which involves finding and configuring it. For general topics, a custom script or program may be needed, especially for niche subjects. The key is ensuring the technical exercise is highly relevant to the subject matter. Balancing the difficulty of practical exercises is crucial. Too easy, and users won’t engage. Too hard, and they’ll get frustrated. Tasks should challenge users to think critically and apply their knowledge without discouraging them. This requires iterative testing and feedback to fine-tune the complexity. The goal is to bridge the gap between theoretical knowledge and real-world application, making learning effective and enjoyable. Quality assurance and finishing touches The development process is complete, but there’s still work to do before releasing the content. We take pride in polishing our content, so the final steps are crucial. Checking against expectations Before the official QA process, we review the original plan to spot any discrepancies, such as unmet learning objectives or missing topics. While deviations don’t always require changes, they must be justified. Assuring quality A thorough QA process is vital for catching grammatical errors, technical bugs, and general improvements before release. Each lab undergoes three rounds of QA, each performed by a different person – two rounds of technical QA, and one for presentation. Some of the steps taken during technical QA include: Verifying written content accuracy, flow, and completeness. Ensuring all learning objectives are covered. Identifying any critical bugs or vulnerabilities that would allow users to bypass the intended solution. Providing small tweaks or changes to tasks for clarity. Assigning relevant mappings (NICE K-numbers, MITRE tags, CWEs). After technical QA, the lab is reviewed by our quality team to ensure it meets our presentation standards. Once all labs in a collection pass rigorous QA, they are released for users. The final step occurs post-release on the platform. Gathering and implementing user feedback Users are at the heart of everything we do, and we strive to ensure our content provides real value. While our cyber experts share valuable knowledge, user feedback prevents echo chambers and highlights areas for improvement. After new releases, we conduct an evaluation stage to analyze what went well and where we can improve. User feedback We gather quantitative and qualitative feedback to help us identify root issues and solutions. Quantitative feedback involves analyzing metrics like completion rates and time taken. We also examine specific changes, such as frequently missed questions or labs where users drop out. These are important things to note, but we avoid drawing conclusions solely from this data. This is where qualitative feedback comes in. Qualitative feedback includes user opinions and experiences gathered from feedback text boxes, customer support queries, and direct conversations. These responses are stored and read by the team and provide context beyond raw numbers. Channels such as customer support queries and follow-ups with customers also help us improve our content. Post-release reviews We conduct post-release reviews at set intervals after content release to analyze quantitative and qualitative data. This review helps us assess the entire process and identify areas for improvement. These reviews allow us to update content with new features, like adding auto-completable tasks for CyberPro. The reviews ensure our content remains current and enhances user experience. Wrapping up Hopefully, this blog post has provided insight into all the care we put into building and tailoring our content for users. This process has come a long way since we started making labs in 2017! Don't forget — with our new Lab Builder feature, you can now have a go at creating your own custom labs. If there's a topic that interests you and you want to share that knowledge with your team, making your own lab is a great way to do it! If there’s any part of the process you’d like to know more about, ask in the comments. Are there any collections that made you think, “Wow, I wonder how this was made”? Let us know!From Concept to Content: Laying the Foundations of a Lab Collection
Technical planning At this stage, we address niche technical details not covered in initial planning but crucial for polished content. Below is an example of the question-and-answer process used for the “Web Scraping with Python” collection. Should the practical sections of the content be created using Docker, for optimal speed and modularity, or does the subject matter require the use of a full EC2 instance? As there are no unusual requirements for the technical portion of the collection (such as needing kernel-level access, network modifications, or third-party software that doesn’t run in containers), the labs can run on Docker. This is a benefit not only for the overall user experience, but also allows for image inheritance during development, which will be demonstrated a bit later on. Are there any tools, custom scripts, or system modifications that should be present across the whole piece of content? The collection is based around writing Python scripts, so ensuring that Python is installed on the containers as well as any required web scraping libraries is a must. In addition, some considerations for user experience can be made, such as installing an IDE like Visual Studio Code on the containers. How can task verification be implemented to make sure it’s both robust and non-intrusive? In the case of this collection, implementing auto-completable tasks may be difficult due to the variety of ways users can create solutions, as well as the lack of obvious traces left by web scraping. Instead, it may be more appropriate to insert task solutions into a mock website that needs to be scraped, which the user can retrieve by completing the task and providing the solution in an answer box. Understanding the technical requirements for a piece of content helps to bridge the gap between planning and development, making it a crucial step. With some of the key questions answered, it’s time to move on to implementation. Creating base images It’s finally time to put fingertips to keyboards and start programming! The first part of implementation creates what all labs in a collection will be built on – a base image. This is a skeleton that provides all the necessary tools and configuration needed for the whole collection, using a concept called image inheritance. If you're new to containerization software like Docker, don't worry – image inheritance is straightforward. Docker containers use images as blueprints to create consistent, mini-computers (containers). This is useful for labs because it allows you to quickly create a pre-configured container without the overhead of setting up a virtual machine, saving time and system resources. This is where image inheritance comes in. Docker images can inherit traits from parent images, similar to how you inherit eye color from your parents. Instead of one central image for all purposes, you create a parent image with shared requirements and then customize descendant images for specific needs. Let’s use the “Python for web scraping” collection as an example again. Think about what kind of things would need to be present in each lab: An installation of Python so the user can run scripts. A code editor to write the scripts in. A mock website for the user to test their scripts on. The first two of these requirements are essentially the same in every lab; there’s no real need to change the installation of Python or the code editor, and in fact, it would be better to have them all be identical, which would result in a smoother user experience. The third, however, does need to be changed — the specific task requirements are going to be different from lab to lab, and the website files will need to change to accommodate this. Taking into account the requirements, an inheritance structure like this can be used: Base image – Python installation and code editor present Lab 1 – Custom website files Lab 2 – Custom website files Lab 3 – Custom website files … Structuring images this way saves time, disk space, and development work by reusing shared configurations. Next time… In part three of this mini-series, you'll learn about the final stages of content development: creating labs, quality assurance, and release. To be notified when part three is released, follow The Human Connection Blog using the bell icon. Meanwhile, feel free to ask questions about the content creation process or specific collections in the replies. Have you used the Lab Builder feature to make any custom labs yet?From Concept to Content: A Deep Dive into Theorizing and Planning a Lab Collection
The decision process When creating new content, the first step is deciding what to commit to. We consider: User demand: Are users frequently requesting a specific topic? Evolving landscapes: Is there new technology or industry trends we should cover? Internal analysis: Do our cyber experts have unique insights not found elsewhere? Overarching goals: Is the content part of a larger initiative like AI security? Regulations and standards: Can we teach important regulations or standards? Cyber competency frameworks: Are we missing content from frameworks like NICE or MITRE? After considering these points, we prioritize one idea for creation and refinement. Lower-priority ideas are added to a backlog for future use. Feasibility and outcomes Having a concrete idea is just the beginning. Over the years, we’ve learned that understanding the desired outcomes is crucial in planning. Our core mission is education. We ensure that each lab provides a valuable learning experience by setting clear learning objectives and outcomes. We ask ourselves, “What should users learn from this content?” This ranges from specific outcomes, like “A user should be able to identify an SQL Injection vulnerability”, to broader skills, like “A user should be able to critically analyze a full web application”. Listing these outcomes ensures accountability and fulfillment in the final product. Setting clear learning objectives involves defining what users will learn and aligning these goals with educational frameworks like Bloom’s Taxonomy. This taxonomy categorizes learning into cognitive levels, from basic knowledge and comprehension to advanced analysis and creation. This ensures our content meets users at their level and helps them advance. Turning big topics into bite-sized chunks Once a topic is selected, we must figure out how to break down huge subject areas into digestible chunks. This is a fine balance; trying to cram too much information into one lab can be overwhelming, while breaking the subject down too much can make it feel disjointed. One good approach is to examine the learning objectives and outcomes set out in the first step, map them out to specific subtopics, and finally map those to labs or tasks. For example, consider this theoretical set of learning outcomes for a Web scraping with Python lab collection. A user should understand what web scraping is and when it’s useful. A user should be able to make web requests using Python. A user should be able to parse HTML using Python. A user should understand what headless browsers are and when to use them. A user should be able to use a headless browser to parse dynamic content on a webpage. These outcomes can be mapped into two categories: theory outcomes (“A user should understand”) and practical outcomes (“A user should be able to”). Understanding the difference between these two is useful, as a few things can be derived from it – for example, whether to teach a concept in a theory (heavy on theoretical knowledge without providing a practical task) or practical (teaching a concept and exercising it in a practical environment) lab. Using this, the outline for a lab collection can start to take shape, as seen in the table below. Learning outcome Knowledge Type Suggested Lab Title Suggested Lab Content A user should understand what web scraping is and when it is useful. Theory Web scraping with Python – Introduction A theory lab showing the basics of web scraping, how it works, and when it is useful. A user should be able to make web requests using Python. Practical Web scraping with Python – Making web requests A practical lab where the user will write a Python script that makes a web request using the “requests” library. A user should be able to parse HTML using Python. Practical Web scraping with Python – Parsing HTML A practical lab where the user will write a Python script that parses HTML using the “beautifulsoup” library. A user should understand what headless browsers are and when they should be used. Theory Web scraping with Python – Understanding headless browsers A theory lab explaining why dynamic content can’t be scraped using previous methods, and how headless browsers can solve the issue. A user should be able to use a headless browser to parse dynamic content on a webpage. Practical Web scraping with Python – Using headless browsers A practical lab where the user will write a Python script that scrapes dynamic content from a website using the “puppeteer” library. All Demonstrate Web scraping with Python – Demonstrate your skills A demonstrate lab where the user will complete a challenge that requires knowledge from the rest of the collection. Each learning objective is assigned to a lab to ensure thorough and user-friendly coverage. Often, multiple objectives are combined into one lab based on subtopic similarity and the total number of labs in a collection. The above example illustrates the process, but extensive fine-tuning and discussion are needed before finalizing content for development. Next time… In part two of this mini-series, you’ll read about the next stage of the content development process, which involves laying the technical foundations for a lab collection. Don't miss the Series… You can opt to receive an alert when part two of this series is released, by “following” activity in The Human Connection Blog using the bell at the top of this page. In the meantime, feel free to drop any questions about the content creation process in the replies. Are there any parts of the planning process you want to know more about?
