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!Decoding Coding: Picking a Language
These days, more and more jobs can benefit from being able to write simple scripts and programs, especially in cybersecurity. For example, pulling data from an API, scraping web pages, or processing large data files to extract information – the list of uses is virtually endless! Tempting as it is to dive right in, there are several things worth thinking about before you begin. This article will discuss one of the most important choices – selecting a language. What to consider when choosing a language A basic understanding of programming languages can make your life easier, increasing your adaptability and finesse in different environments. But with tons of languages like Python, Java, JavaScript, Go, Rust, and more, which one should you choose? Here are the crucial factors to consider: What's available Can you install whatever language you like to run your code, or are there limitations? If you have an enterprise-managed computer, you might not be able to install new software or languages, and you may need to use the default options. For Windows, this is PowerShell. Bash Script is the equivalent for Mac and Linux devices, and Python is often available too. Your personal experience and interest This one might sound obvious, but it does matter. We learn better and faster when we're invested in the subject. Look at your previous experiences. Have you worked with any programming languages before? Did you enjoy them? For example, if you had a good experience working with Python, let that guide your decision! That said, don't shy away from learning something new if there's a good reason or you’re curious to do so. What's trending in your organization Does your organization or team predominantly use a specific language? Not only would learning that one help you communicate better with your colleagues, but it could also give you an edge while working with systems developed in that language. Plus, there’ll be plenty of people to talk to if you get stuck! The language's capabilities and nature Like people, different languages have different strengths. Some are fantastic for web development (like JavaScript), while others are better suited for system-level programming (like C). Python is often an excellent choice. It's considered easy to learn, incredibly flexible, and powerful due to the huge catalog of packages available. While it isn't as fast as many other languages, for most purposes, it's usually more than fast enough. Java is a very widely used object-oriented programming language and can be extremely fast. The learning curve is steeper than Python, but there are loads of learning resources available. JavaScript (not to be confused with Java!) isn’t as useful for quick standalone scripts or applications, but it's the dominant language for websites and browsers, so understanding it is practically a superpower for testing and manipulating websites and applications. C and C++ allow low-level access to memory and offer a lot of flexibility – incredibly helpful when evaluating systems with these languages at their core. Available tools and training Great tools can make tough jobs easier. Certain programming languages have robust toolsets that can help automate your tasks. For instance, Python has a wide array of libraries and frameworks that make handling big projects a cinch while saving you time and effort – why reinvent the wheel when you can just import it? Take a look at what training is available for the language you’re interested in. Older and more popular languages are likely to have more to choose from, but there’s loads out there and a lot of it is free! Also, consider what tools you might already have access to within your organization. Community and support If a programming language has a large active community, it means help is readily available when you get stuck. Languages like Python, JavaScript, and Java have strong communities and plenty of online resources available. Scope for growth If you're planning to learn a language, why not pick one that's in demand? Check job boards, look at industry trends, and see if learning a particular language can give your professional growth a boost! Summary Remember, no language is “the best". The best is the one that suits your needs and circumstances. You might even find mastering multiple programming languages useful over time. Just like speaking multiple languages, the more you know, the better you can communicate in different environments! Once you understand some of the basic programming concepts, like variables and loops, it’s easier to learn a second or third language. Learning a programming language may initially seem like climbing a steep mountain. But once you get the hang of it, you'll realize that the view from the top was well worth the hike! Want to take the next step? Here are some lab collections that may help you learn a bit more about PowerShell and Python: PowerShell Basics Offensive PowerShell Introduction to Python Scripting Share your thoughts If you’re new to coding, tell us what language you’re trying out! Why did you pick it, and would you make the same choice again? Are there any specific challenges you found or any relevant experiences you’d like to share?Microsoft Sentinel: SOAR Demonstrate your skills
Hello all, I am struggling on question 6 where I feel like I have the Condition portion setup correctly but it won't say that it is completed. The condition is supposed to be within the For Each loop correct? The Condition parameters is: AND - Body Risk = High
Microsoft Sentinel Deployment & Log Ingestion: Ingesting Platform Logs via Diagnostic Settings
Hello Immersive Labs community, I’ve been working through the lab tasks and successfully completed tasks 1 through 6. However, I’m stuck on task 7, which asks: "A storage account has been deleted. What would be the data type of the generated log?" The task seems oddly described, and I can’t find any clear hints in the lab briefing or online resources. I’ve tried querying various data types like AzureActivity, AuditLogs, StorageBlobLogs, StorageFileLogs, and others, but none seem to fit correctly. Could anyone provide guidance or confirm which data type is actually relevant for this scenario? Are there any specific tips or resources I might be missing? Thanks in advance for your help!
