Cybersecurity Awareness Month at Immersive
Welcome to Cybersecurity Awareness Month! October is your annual opportunity to pause, benchmark your security skills, and level up your defenses against an increasingly dynamic threat landscape. This year, our primary focus is on improving AI security & resilience across your workforce. As artificial intelligence transitions from an emerging technology into core business infrastructure, it introduces both incredible efficiency and novel security risks. Whether you are defending against frontier-model-enabled threats, securing automated enterprise pipelines, or guarding against human-centric identity attacks, true resilience requires hands-on training—not just passive learning. Join us throughout October in the Immersive platform to jump into hands-on challenges, test your skills in live crisis simulations, and tune in to interactive expert events designed to help you tackle modern cyber threats head-on. Labs & Challenges 🧪 The Hex Files (Earn a digital community badge if completed by Nov 9th!) Track down and stop the Pointy Hat Society—a coven of witches-turned-hackers—through a brand new program combining both hands-on labs and single-player Crisis Sim scenarios that reinforce safer security hygiene. Then test what you’ve learned in two of our most popular challenge labs. Lab Collections: The Hex Files: Coven Up The Hex Files: Witchy Waters Crisis Sims: The Hex Files: A Brew-tal Breach The Hex Files: The Nightshade Incident AI Security & Resilience Pathways 🤖 To help organizations build workforce resilience at scale, managers can assign any of the following 5 structured programs directly to their teams: Foundational AI Literacy and Safe AI Adoption — Understand what AI tools are, how they are driving business value, and how to safely adopt them in your daily workflows. Protecting Against Frontier-Model-Enabled Threats — Learn how frontier models like Mythos accelerate threat actor workflows and sharpen your defensive capabilities against AI-driven risks. AI Purple Teaming (Blue Team) — Bridge traditional incident response into AI-specific risks, specialized telemetry, and adversary behaviors in collaborative exercises. AI Purple Teaming (Red Team) — Build practical confidence targeting AI-specific attack surfaces, finding model vulnerabilities, and translating findings into better defenses. Scattered Spider Threat Actor Defense — Master techniques to detect social engineering, identity compromise, and lateral movement typical of Scattered Spider operations. Events & Webinars 📅 Mark your calendars for our live events and interactive sessions throughout the month: [Roadmap Webinar] The Evolution of Immersive One: Proving AI Readiness Across Your Enterprise Date & Time: Thursday, 8 October 2026 | 10:00 am EST / 3:00 pm BST Overview: Discover how to turn AI adoption into proven enterprise readiness with insights on our closed-loop resilience engine, the agentic frontier, and an exclusive sneak peek at our Q4 product roadmap. [Webinar] (Ghost) Busting Cybersecurity Myths: Separating Fact from Fiction Date & Time: Monday, 19 October 2026 | 10:00 am ET / 3:00 pm BST Overview: Join Container 7 experts as they debunk common cybersecurity myths, evaluate AI risks, and uncover practical strategies to build true organizational resilience. [Virtual Crisis Sim LIVE] The Return of the Puppetmaster: No Strings Attached Date & Time: Friday, 30 October 2026 | 10:00 am EST / 3:00 pm BST Overview: Put your crisis management team through a high-pressure live simulation tackling leaked enterprise credentials, insider threats, deepfakes, and rogue AI defense agents. Cyber Resilience Awards 2026 🏆 Keep your eyes peeled right here in the community and across our social channels during the final week of Cybersecurity Awareness Month! We will be announcing the winners of the Cyber Resilience Awards 2026, celebrating the individuals and organizations operating at the absolute cutting edge of cyber and AI resilience. Stay tuned to see who takes home top honors!Iris C2Agent: hunting malware that hands control to an AI model
A look at a new LLM-operated Windows implant, what it leaves behind, and how to investigate it. By the Container 7 Team Most implants wait for an operator to send commands. Iris, also tracked as C2Agent, waits for a goal. The operator types something like "get the administrator's password" and an AI model takes it from there, choosing each command, reading the output and deciding what to do next. If you defend Windows endpoints in an organization that uses AI tools, this one's worth your time. What's happening Iris is a Windows implant written in Go. It was first seen on September 10, 2026, and Palo Alto Networks' Unit 42 documented it in October 2026. Its design is unusual. The operator doesn't send a script or a list of commands. They send a plain-language objective, and the implant hands interactive control of the host to a large language model (LLM). The model works in a loop. It runs a command, reads the result and picks the next step. The family has moved fast. Unit 42 tracked 32 samples across eight major versions in 29 days. Newer versions can hijack AI developer tools already installed on the victim's machine and abuse legitimate commercial AI services to carry out their work. Unit 42 also describes it as the first documented implant to act as a Model Context Protocol (MCP) client, which lets it add new tool connections at runtime. Why it matters Iris changes two things defenders rely on. First, there's no fixed playbook. A traditional implant repeats the same tasks in the same order, which gives you a pattern to detect. With a model in control, the sequence of commands can shift from one run to the next, even when the goal stays the same. Second, it hides in traffic you've probably already approved. Most organizations allow AI services out of the network because their developers and analysts use them every day. When the malware calls those same services, or drives an AI tool that's already installed, malicious requests sit next to legitimate ones. Your AI egress policy now affects how much you can see. There's also an upside for defenders. A model that reasons step by step can leave a record of that reasoning. If you can find it, you can see what the attacker asked for and what the model tried to do. What to look for No single signature covers every version, so hunt for behavior. Start with these questions: Process lineage: Are AI developer tools or command-line assistants being launched by unexpected parent processes, or running commands nobody on the team asked for? AI egress: Which hosts talk to commercial AI services, and do those hosts have a business reason to? Look for AI traffic from servers or accounts that don't normally use it. Command cadence: Look for bursts of reconnaissance and credential-access commands with short, regular gaps between them, as if each one waited for the last result. On-disk artifacts: Watch for new configuration, prompt or "skill" files appearing alongside unfamiliar binaries in user-writable folders. Recorded reasoning: Where you can find the model's output, compare what it planned with what the host logs say actually ran. Get hands-on The Iris C2Agent Log Analysis lab puts you in the defender's seat. Working in Elastic, you'll investigate a Windows host where Iris was executed and tasked by a remote operator. You'll recover the artifacts it left on disk and on the network, then read the model's own recorded reasoning to separate what the attacker intended from what actually happened on the host. By the end, you'll know the indicators of compromise (IOCs) this family leaves behind. The lab is built for security analysts, incident responders, threat hunters, and detection engineers who monitor Windows estates. It also suits anyone working out how allowing AI traffic changes their attack surface. It's the first of our releases on Iris. A Cyber Range Exercise is in the works, so watch this space. Start the lab: http://immersivelabs.online/labs/hunting-the-tools-iris-c2agent-log-analysisIt's Software, Not Magic: Navigating the Vulnerability Speed Wave and Shadow AI
🎧 Listen now to The Resilience Room What does a 31-year career at one company look like in cybersecurity? For Lee Stephens, it looks like research, marketing, sales, operations, and consultancy — and never a dull moment. In this episode of The Resilience Room, Lee sat down with host Sam Dickison to cut through the noise on ransomware, the collapsing patch window, Shadow AI, and the looming quantum reckoning. The Retail Ransomware Wake-Up Call Last year's wave of attacks on the UK retail sector made cybersecurity front-page news. For Lee, the most striking thing wasn't the sophistication of the attacks — it was the opposite. "What came home was actually the simplicity of the attacks. Those really fundamental, boring basics are absolutely critical — and involved in so many of the incidents we deal with." While the media spotlight has moved on to AI, the threat landscape hasn't changed nearly as much as the news cycle suggests. The fundamentals remain the same: strong unique passwords, kept systems, tested backups, and a plan for a bad day. As Lee puts it, you don't need to outrun the bear — just don't be the slowest runner. The Vulnerability Speed Wave: Your 90-Day Patch Window Is Gone Here's the stat that should reframe every conversation about patch management: 2021: Average time from CVE disclosure to active exploit — one year 2025: Shrunk to one month 2026: Now at one week and one day 2027 projection: Potentially an hour — or a minute This geometric progression is being driven by AI-assisted vulnerability research on both sides. The traditional 90-day patch window is functionally dead, and some vendors are already moving to fortnightly cycles. Speed is now a security control in its own right. AI in the SOC: Real Benefits, Realistic Limits SOC analyst burnout is real. The volume of alerts, the repetitive triage work — AI-assisted automation has genuine potential to help. But fully autonomous SOCs? Lee remains cautious. "At times AI feels like magic. It's not magic, it's software. And from thirty years ago when I did my computer science degree: garbage in, garbage out." His recommendation: don't chase the ten-times transformation. Chase the ten percent improvement and compound it. Organisations that have tried to automate everything often find they're spending more time maintaining automations than the work would have taken. Shadow AI: The New Shadow IT Problem Most people using unofficial AI tools aren't being malicious — they're trying to get their job done. But the risk is real, especially around feeding confidential data into models without proper data handling agreements. Some of Lee's clients require all data to remain in the UK at all times, which effectively rules out certain major tools that can't guarantee that under peak load. His cautionary scenario: a team uses AI to diagnose an infrastructure problem. The output gets translated for customers, then condensed for a senior exec. Each iteration drifts further from reality — and when the fix doesn't work, multiple conflicting versions of "the truth" are already in circulation. "AI has no conscience, it doesn't care. It's a prediction engine. It's software. Not magic — software." The solution is culture and training as much as technical controls, combined with IT teams designing better corporate solutions so people don't need to go off-piste in the first place. What's Next: Quantum's Y2K Moment Quantum computing has been "just five years away" for decades — but the trend lines are genuinely moving. More qubits, better coherence times, major advances every few months. The security implication is stark: the maths underpinning asymmetric encryption is trivially easy for a quantum computer to solve, which means every piece of encryption on the planet eventually needs replacing. NIST has published post-quantum cryptography standards, and vendors are beginning to implement them. But the migration is a massive undertaking. "It's akin to a Y2K moment where everybody needs to upgrade — for really no benefit at all, just to stay standard. The only disadvantage is we don't know when the moment is." The organisations best placed will be the ones who've already done their cryptographic inventory and know what they'd need to upgrade and in what order. Whether the topic is ransomware, patch windows, AI, or quantum — Lee's message is consistent: there's no silver bullet, no magic. Just fundamentals, applied consistently, with humans staying in the loop. After 31 years, it's the message that keeps working. The Resilience Room is hosted by Sam Dickison. New episodes explore the human and technical realities of cybersecurity with guests from across the industry.The Black Belt of Cybersecurity: What a Former Military Pilot Turned SecOps Leader Knows About Surviving the Chaos
🎧 Listen now to The Resilience Room In 2012, a young Air Force officer named Michael Vetri walked out of his commanding officer's office having just been told his career was about to change forever. He'd trained as a pilot. Now he was being reclassified into a field almost nobody in the room understood. "I'm so sorry you were reclassified into cyber," his flight instructor told him. "If you need someone to talk to, let me know." That's not a joke. That's how misunderstood cybersecurity was fourteen years ago — obscure enough that getting assigned to it sounded like a consolation prize, or worse, a diagnosis. Fast forward to today, and that reluctant recruit is Senior Director of SecOps at a SaaS company serving the biopharma industry, a black belt Krav Maga instructor, and — going by the enthusiasm with which he brought up Assassin's Creed unprompted — a certified nerd in the best sense of the word. Mike sat down on The Resilience Room and covered a lot of ground: emotional intelligence as a revenue driver, the four things that keep security teams from burning out, why AI is currently winning the fight, and how disarming a training knife in a Krav Maga studio taught him more about incident response than most corporate leadership seminars ever did. If you work in this field, several of these are going to land uncomfortably close to home. Good. That's the point. Empathy isn't a soft skill. It's a spreadsheet line item. Mike leads with rank, but not with authority — a distinction the military drilled into him early. "In the civilian world, you can't just say, 'I'm a captain, you're an NCO, you'll do what I say.' No — it's a lot more influential leadership, invoking those empathetic muscles more." He points to a Harvard Business Review study of 189 companies that measured how deeply organizations embedded emotional intelligence principles — self-awareness, self-regulation, empathy, motivation — into their culture. The companies that did it well beat their revenue targets by roughly 20% year over year. The companies that didn't, missed by about the same margin. For an industry that still occasionally treats "soft skills" as a nice-to-have bolted onto technical competence, that's a hard number worth sitting with — especially if your last performance review mentioned "communication" in a slightly ominous tone. The four pillars that keep security teams from quitting Burnout in cybersecurity isn't news to anyone reading this. But Mike's framework for fighting it is refreshingly concrete. He tells every team he leads that people will stay if you give them four things — not in excess, just in moderation: Time. If someone's been grinding through late nights, notice. Let them come in later. Build in recovery. Money. Pay fairly for what they contribute — full stop. Recognition. Not just for wins. How you handle underperformance, professionally and without ambush, matters just as much. Development. Give people room to grow into who they're becoming, not just who they were hired to be. One detail worth stealing: when an underperformance conversation is looming, Mike doesn't open with the problem. He opens with "how are you doing? Everything okay outside of work?" — asked more than once if needed. People under pressure, especially newer employees still trying to prove themselves, often won't volunteer that something's wrong. They'll white-knuckle through it instead. A little space to be asked, rather than told, changes what people are willing to say. During one incident that dragged on for months, Mike built a shift schedule with staggered four-hour windows so his team could claw back rest without falling behind. He also borrowed a page from Air Force operational risk models: a short questionnaire — how much sleep did you get, how many consecutive night shifts, how long since you've touched this system — that produces a risk score. Score too high, and you talk to a duty officer before you're cleared to keep going. It's the kind of instrumented self-check most SOCs could use and almost none have. Be the eye of the hurricane Ask any incident responder what makes a bad night worse, and it's rarely the incident itself. It's the Slack DMs. The "hey, what's going on?" pings stacking up while you're still trying to pull logs. Mike's answer is to act as an umbrella for his team — absorbing pressure from above so the people doing the actual work can think. That starts with managing expectations upward immediately: this is fresh, I'm still learning it, I'll update you in an hour. One sentence, enormous operational space. It tells leadership "I've got this" while telling the incident team "go dark and go deep." Underneath that is a military framework Mike leans on constantly: the OODA loop — Observe, Orient, Decide, Act. In a live incident, the goal is to move through that loop faster than whatever you're up against. The moment you get stuck in "orient," unable to commit to a decision, you've lost the initiative. Everything else — the calm voice, the reassurance, the "breathe, I've got your cover" — exists to keep the team moving through the loop instead of freezing in it. And underneath that is the least glamorous ingredient of all: reps. "You wouldn't want a surgeon who hasn't performed one in three or four months operating on you," Mike says. Tabletop exercises, drilled relentlessly, are what makes calm possible when the real thing hits. AI is winning the speed war — for now This is where the conversation gets genuinely unsettling, in the way only a good SOC-lead story can. Mike cited threat intel research his own team pulled showing that traditional, manually written phishing emails land at about a 12% click rate. AI-generated phishing is landing at 54%. That's not an incremental shift — it's a different category of threat. His framing borrows from military history: "Just like the English were doing great with their longbowmen until they were met with cannons — we're doing great against traditional malware, traditional phishing, until we're met with AI-based malware, AI-based phishing." He pointed to Phantom Raven as a case study in the new normal — a highly polymorphic strain that reshuffles its own hash and callback domains the moment defenders try to contain it, sometimes riding in through a malformed open-source package nobody scrutinized closely enough. Mike's answer isn't to out-human the machines — it's to fight AI with AI, using it to accelerate investigation and triage so humans can make faster calls. But he draws a firm line at execution: "I still think it's a bad idea to let AI execute actions on your security stack." Not because it can't make good calls, but because when it's wrong, it's wrong at machine speed — locking down a developer's test script as if it were a live threat and taking a production deploy down with it. Assessment and evaluation, yes. Pulling the trigger, not yet — not until AI has earned the trust that comes from track record, the same way a junior analyst earns it. He's also thinking about a subtler insider-risk problem taking shape as advanced AI systems move into more sensitive testing and evaluation programs. His concern isn't the technology itself — it's what happens when knowledge of where that technology lives becomes valuable enough to bribe someone for. New capability, same old human vulnerability. Train at black belt level, or don't bother training at all Mike's been doing Krav Maga since a college roommate walked in mid-Assassin's Creed session and told him the game's weapon disarms were based on a real martial art taught nearby. He put the controller down, went and checked it out, and got hooked on his very first class — which happened to be weapon disarming. He made it to black belt in 2014, taught for years, and — genuinely one of the better anecdotes on the episode — met his wife through a self-defense class he was running as a side gig at New Mexico State. Her kicks, he reports, had "so much pepper behind them." The parallel he draws to cybersecurity is the episode's sharpest idea: you're only prepared to handle the level you've actually trained for. A yellow belt can handle a yellow belt. Theoretically, maybe an orange. Train your team only against script kiddies and spray-and-pray attackers, and script kiddies and spray-and-pray attackers are all you'll ever be ready for. The nation-state actor doesn't care what belt is hanging in your dojo. It shows up in the small stuff too — like treating every incident, however minor, as if you might have to defend your handling of it in court one day. Heavily documented, no matter how low-level it looks. "Don't give the opposing attorneys any reason to pierce your veil," as Mike puts it. Complacency doesn't announce itself. It just quietly lowers the belt you're actually prepared to fight at. The throughline of this whole conversation is one Mike states almost in passing but clearly means as a core operating principle: technical skill gets you in the room, but judgment, empathy, and relentless preparation are what keep your team — and your organization — standing when things go sideways. AI is changing the speed of the fight. It isn't changing who has to decide what to do about it. Catch the full conversation with Mike Vetri on The Resilience Room – available on most podcast platforms.New CTI Lab: CVE-2026-23744 (MCPJam RCE): Offensive
On January 16, 2026, advisories were released covering a critical vulnerability in MCPJam Inspector, the local-first development platform for MCP servers. The Latest version, 1.4.2 and earlier, is vulnerable to a remote code execution (RCE) vulnerability, a trivial yet highly impactful vulnerability that allows an attacker to send a crafted HTTP request that triggers the installation of an MCP server, leading to RCE. What is this about? Model context protocol (MCP) has become more popular as a way to connect applications and services together that use AI, such as connecting tools to your OpenAI account, so the AI can help you work with the tool, perform tasks on your behalf, or work as webhooks between tools. MCPJam is an example of a tool that makes these processes easier and more convenient. Why is this critical for you and your team? As AI adoption across industries and sectors rises, products and services have been released to help people interact with AI pipelines. With MCPJam and tools like it, you can test and develop MCP (model context protocol) servers, emulate deployments, and debug your workflow, making your entire MCP development pipeline much smoother.If you're using any tools like this, where you share you API keys and other sensitive data with the tool, you need to be cognisant of the risks that these tools carry, as many others are vulnerable to basic misconfigurations that can lead to serious impacts. Who is the content for? Penetration Testers Security Analysts Incident Responders Here is a link to the lab: CVE-2026-23744 - MCPJam: Offensive This application has no logging available at all, so no Defensive Variant of this labIt’s Not Magic, It’s Mechanics: Demystifying the OWASP Top 10 for AI
Welcome back to our series, “Behind the Scenes of Immersive One”! The following is a conversation with Sabrina Kayaci, Cybersecurity Engineer for Immersive One, and Rebecca Schimmoeller, Lead Product Marketing Manager. Today, we’re continuing the discussion on our Secure AI capability. “When developers hear ‘AI Security,’ they either start to sweat or eye-roll. It either feels like a black box where the old rules don’t apply, or it feels like inflated marketing hype. The truth is, AI vulnerabilities aren't magic; they are mostly just new manifestations of the classic flaws we’ve been fighting for decades. Once you map the new threats to the old patterns, the mystique fades. You realize it’s not magic to fear or hype to ignore—it’s just an engineering problem to solve.” Rebecca: Awesome frame, Sabrina. No matter where you sit on the spectrum—whether you’re anxious about the risks or skeptical of the buzz—AI security doesn't mean starting from zero. Developers should already have the muscle memory for this. Sabrina: Exactly. We aren't asking them to learn a new language; we're asking them to apply their existing fluency to a new dialect. That’s the core philosophy behind our new OWASP Top 10 for LLMs and GenAI collection. We tackle the problem that AI is often treated as a "new and daunting" field. By framing threats like Supply Chain Vulnerabilities or Excessive Agency as variations of known issues, we accelerate the learning curve. We strip away the "AI mysticism" to reveal the underlying mechanical flaw. Rebecca: I love "stripping away the mysticism." Let’s talk about how that works, starting with the big one everyone is concerned about—Prompt Injection. How do you take that from "scary AI jailbreak" to something a grounded engineer can fix? Sabrina: In the media, Prompt Injection is portrayed as this sentient ghost in the machine. In our lab, we treat it as an Input Validation failure. We show that the system is simply confusing "user input" with "system instructions." When a developer sees it through that lens, the eye-roll stops. It’s no longer hype; it’s just mixed context. And they know how to fix mixed context. We show them how to apply that architectural fix to an LLM. Rebecca: That maps perfectly. But looking at the curriculum, I see we go much deeper than just a standard "Top 10" checklist. Why was it important to go beyond the simple definitions? Sabrina: Because a definition tells you what something is, but it doesn't tell you how it impacts you. In the new OWASP LLM collection, we focus on Core Mechanics and Attack Vectors. We deconstruct threats like Data and Model Poisoning or Supply Chain vulnerabilities to show you exactly how they infiltrate a system. It’s the difference between knowing what an engine looks like and knowing how to take it apart. You need to understand the mechanics of the vulnerability to understand the potential impact—otherwise, you're just guessing at the fix. Rebecca: It sounds like we're upgrading their threat modeling software, not just their syntax. Sabrina: Yes, 100%. Look at Excessive Agency. That sounds like a sci-fi plot about a robot takeover. But when you do the lab, you realize it’s just "Broken Access Control" on steroids. It’s about what happens when you give an automated component too much permission to act on your behalf. Once a developer maps "Excessive Agency" to "Least Privilege," they stop worrying about the robot and start locking down the permissions. Rebecca: Is the goal to get them through all ten modules to earn a Badge? Sabrina: The OWASP Top 10 for LLMs Badge is the end state. It proves you have moved past the "sweat or eye-roll" reactive phase. To your manager, it signals you have a proactive, structured understanding of the AI risk landscape and can speak the language of secure AI. There’s no hype in that. Only value-add to you and your team. Final Thought Our OWASP Top 10 for LLMs collection is the antidote to AI security angst. For the developer, it demystifies the threat landscape, proving that their existing security instincts are the key to solving new problems. For the organization, it ensures that your AI strategy is built on a bedrock of engineering reality, rather than a shaky foundation of fear. [Access Collection]Architecting at Speed: Mastering Secure Development with OpenAI Codex
Welcome back to our series, “Behind the Scenes of Immersive One”! The following is a conversation with BenMcCarthy, Lead Cybersecurity Engineer for Immersive One, and RebeccaSchimmoeller, Lead Product Marketing Manager. Today, we’re continuing the discussion on our Secure AI capability. There is a misconception that security is the enemy of development speed. But with AI, the opposite is true. If you don't have security engineered into your AI workflow, you can't actually go fast—because you’re constantly stopping to fix 'trash code' or patch vulnerabilities. The developers who win in this era aren't just the ones coding faster; they are the ones architecting systems that are secure by design, even at AI speeds.” Rebecca: That’s a crucial distinction, Ben. We often hear that AI is a "firehose" of productivity, but without control, that firehose just creates a mess. It seems like the role of the developer is shifting from "writing lines" to managing this high-velocity output. How does the new Building with AI: Codex CLI collection help them make that shift? Ben: By giving them the controls they need to harness that speed safely. If you let OpenAI’s Codex run without guardrails or understanding, you get velocity, sure—but you also get risk. We designed this collection to empower developers to become their own Security Architects for their workflows. We are leveraging the Azure AI Foundry capability to give learners real, secure access to these models. The goal isn't to teach you how to hit "Tab" to autocomplete; it's to teach you how to rigorously evaluate, guide, and constrain what the AI produces using the command line tool like Codex so you can ship code that is both fast and bulletproof. Rebecca: So it’s about elevating the human’s role to "Architect." Let’s talk specifics given what the collection covers—how did you instill that mindset? Ben: We start by ensuring developers know the power of what you can do with Codex. How to get the best out of your models in this CLI tool. We go over effective prompt engineering, tool usage, and how AI can help with "Greenfield" projects (net-new builds) and "Brownfield" projects (legacy codebases). This is a critical skill for a lead engineer. AI is great at generating new code (greenfield), but it can be dangerous when it doesn't understand the hidden dependencies of a ten-year-old application (brownfield). We teach engineers how to spot those context gaps, key stuff that the AI might miss. Rebecca: I saw "specification-driven development" was a big part of your roadmap, too. How does that fit into the "speed" theme? Ben: This is the ultimate accelerator. Instead of writing the code line-by-line, you write the "spec"—the blueprint—and let Codex handle the implementation details. It’s not about doing less work; it’s about doing higher-leverage work. You define the logic and security constraints, and the AI handles the boilerplate. It shifts the developer’s brain from "how do I type this function?" to "what should this system actually do?" Rebecca: That sounds like a powerful approach, Ben. But what about the security risks? If developers are offloading implementation to Codex, how do they avoid leaking data or introducing bugs? Ben: That’s non-negotiable. In the Guardrails lab, we show learners how to build a safety net. We teach practical methods for stripping PII (Personally Identifiable Information) and using hooks to sanitize inputs before they reach the model. It gives developers the confidence to use these tools freely, knowing they have already engineered the safety mechanisms to protect their org. Rebecca: I saw a lab in the collection called "Tools and MCP" (Model Context Protocol). Is that where you get into advanced workflows? Ben: Exactly. This is where we give developers the keys to become a force multiplier. We show users how to connect Codex to other tools. This is the ideal definition of ROI for developers. You’re automating the tedious "check your work" phase, allowing you to ship secure code faster without burning out on manual review. Rebecca: It feels like that approach accepts today’s AI era realities for what they are and finds the strategic advantages… pushing developers towards productivity and security gains with real mastery. And just like the Claude collection, users have access to a Demonstrate Lab, to prove that mastery, am I right? Ben: Absolutely. The Demonstrate Lab challenges users to build a solution that’s efficient, functional, and secure. It proves that you aren't just an "AI user"—you are an AI Engineer who understands the capabilities the collection covers. Final Thought Our Building with AI: Codex collection is about upgrading the developer’s toolkit. For the organization, it ensures AI adoption is secure and scalable. For the engineer, it removes the drudgery of boilerplate, freeing you to focus on the creative, architectural challenges that drive real value. Ready to upgrade your workflow? [Access Collection]Beyond the Chat Window: How to Securely Vibe Code with Anthropic’s Claude
Welcome back to our series, “Behind the Scenes of Immersive One”! The following is a conversation with RobertKlentzeris, Application Security Content Engineer for Immersive One, and RebeccaSchimmoeller, Lead Product Marketing Manager. Today, we’re deep diving into one facet of our Secure AI capability. “We are seeing a shift from ‘chatting with AI’ to ‘inviting AI into the terminal.’ With the release of tools like Claude Code, developers aren't just copying and pasting snippets from a browser anymore. They are letting an agent live directly in their CLI, giving it permission to read file specs, run commands, and architect entire features. It’s a massive leap in capability—but also in trust.” Rebecca: That is the big shift we’re hearing about, Rob. The market is obsessed with the idea of "vibe coding" right now—just describing what you want and letting the AI handle the implementation details. But for a security leader, the idea of an AI agent having direct access to the CLI (Command Line Interface) sounds terrifying. It feels less like a helper and more like handing a stranger your SSH keys. Rob: That is exactly what makes Claude Code different from your standard autocomplete tools. You aren't just getting code suggestions; you are interacting with an agent that has tooling capabilities—like using MCP (Model Context Protocol) or running slash commands. If you don't know what you're doing, you might accidentally let the agent produce insecure code or mishandle PII in a way that’s harder to spot than a simple copy-paste error. This new collection is about bridging that gap: how do we embrace the speed of vibe coding without sacrificing the security of our platform? Rebecca: So it’s about safe integration. Let’s get into the weeds—what does the "safe" version of this look like in the actual Immersive One labs you created? Rob: We start by defining common patterns used in AI coding agents such as manual prompts and how you can write them so Claude generates secure code. We then go a little deeper and explore how you can let your agents start coding securely with more autonomy and less intervention while staying secure with spec-driven development. From there, we move to the components of Claude Code and show how to leverage these advanced features, such as custom slash commands and skills that can enhance the security of both large legacy and greenfield projects. Rebecca: I noticed your roadmap included a focus on "Guardrails" and "Claude Agents." Is this where we stop "trash code" from hitting production? Rob: Exactly. This is unique to the agentic workflow. In the Claude Agents lab, we teach users how to set up a "Reviewer Agent" that audits the code generated by the first agent. We also have a dedicated lab on Guardrails, focusing on stripping PII (Personally Identifiable Information) before Claude ever sees the data. It’s about ensuring that even if the AI is "vibing," the security protocols remain rigid. Rebecca: That sounds incredible for the security team, but what about the developer? If I’m used to just doing my thing, head down to deliver on time, won’t specification-driven development cramp my style? Rob: Fun fact: It actually makes you faster. Think of the 'spec' as the prompt that saves you ten revisions. At Immersive, we focus heavily on ROI and removing pain for users. In this case, we show developers how to use slash commands and hooks to automate the boring stuff. When you learn to use these tools properly, you stop wrestling with the AI and start conducting it. And because these labs are hands-on with real Claude Code access in a secure sandbox, you can experiment with these powerful agents without worrying about breaking your own local environment. Your manager will love that too. Rebecca: Ha! You’re right. It sounds like we’re giving users a safe place to crash-test the car before they drive it. And I see you wrap it all up with a "Demonstrate" lab? Rob: We do. We want to prove competence. The Demonstrate Lab is a capstone where you have to combine everything—usage, security, and productivity. You have to prove you know how to use Claude Code to build something functional and secure. It validates that you aren't just generating code; you're engineering with it. Final Thought Our Building with AI: Claude Code collection isn't just another coding tutorial. It is a blueprint for the agentic future of development. For you the developer, it turns Claude from a vibe code buddy into a fully integrated, secure pair programmer. For your organization, it transforms a potential security risk into a governed, high-speed workflow. Want to get started? [Access Collection]Boosting Cyber Readiness Together: Introducing The New AI In-Lab Assistant
We’re passionate about creating an empowering, collaborative learning environment. That’s why we’re excited to introduce the AI In-Lab Assistant – an intelligent chatbot right inside your lab environment. We want you to solve challenges independently. The new AI assistant is designed to provide hints and tips on a lab, offering guidance without giving away the answer. The aim is to enhance your learning experience, ensuring you're always ready for what’s next. 🔎 Finding your on-demand learning companion Getting started is simple. Open a lab, and you’ll see a sparkle icon in the top right corner of your screen. Clicking this sparkle icon opens the chatbot interface. If the window obstructs your view, you can reposition the chatbot by dragging its top bar. If you need to hide it, click the cross in the top right of the pop-up window or the sparkle icon again. 🔬 How can the assistant help me? We specifically designed this chatbot to help with lab-related queries and tasks inside the lab environment. You can ask it to summarize the lab briefing, help you understand key concepts (such as "what is a SIEM?"), or assist with specific questions (for instance, "I need help with Task 3!"). The chatbot will give you hints and tips without giving you the exact answer. For example, if you're tackling enumeration, it might suggest commands like nmap, netstat, or cat. If you're working on privilege escalation, it may encourage you to think about commands like sudo, su, or to look for SUID binaries. Our sophisticated technology takes snapshots of practical and cloud labs, capturing the necessary context to understand activities on the virtual machine or the command line interface. ✅ Key boundaries and quick tips We want you to get the most out of the new AI In-Lab Assistant. It keeps to the following main points: It won’t give away the answer: We intentionally designed the chatbot to encourage independent completions, not to directly give you the answer. If you ask outright for the answer to a question, it’ll provide hints for you to figure it out yourself. It only applies to lab content: While you can input any query, the chatbot can only answer questions related to the lab you’re currently in. If you ask a non-lab-related question, such as "how’s the weather today in Bristol?", it’ll tell you it doesn’t have access to real-time data. Instead, it’ll encourage you to ask a question about the lab. History isn’t retained: To start a new chat, click the plus icon next to the New chat button in the top left of the chatbot window. This will clear the current history. Note that you’ll lose your conversation history when you exit a lab or start a new one. The chatbot can only retain seven questions before forgetting them. Exclusions: The AI In-Lab Assistant is available in most labs, but isn’t available in custom labs, adaptive assessments, or any demonstrate labs. 🔐 Data security and feedback you can trust We want to be upfront about our technology and security because we value authenticity. Our AI service was built using OpenAI. We prioritize your data security, so we only store your account UUID in the AI service database. We don’t store any other personally identifiable information (PII) except what you explicitly input as queries, which OpenAI will also receive. Crucially, we don’t ingest any custom content labs into our AI service, and the service doesn’t have access to any customer data. We rely on your input to keep improving! You can provide feedback on every response using the thumb-up or thumb-down icons found directly underneath the chatbot’s reply. We monitor this feedback to improve the AI In-Lab Assistant over time. 💁🏻♀️ And don’t forget… If you’d prefer to speak to real humans for hints and tips on your favorite labs, head to the Community and navigate to the Help Forum. If you aren’t satisfied with the AI In-Lab Assistant’s responses or have technical issues with the feature, reach out to Customer Support via the Help Center.New Labs - Malterminal: Malware Analysis
With artificial intelligence (AI) and large language models (LLMs) fast becoming a more popular and talked-about set of technologies in every industry in society, it's no surprise that LLM-enabled malware now exists that can dynamically generate code, query data, and offload malicious functionality to LLMs, lowering the barrier of entry for threat actors deploying malware. This lab introduces one of the first known malware samples to ever facilitate the use of LLMs to perform malicious functionality. Why should our customers care? Most, if not all, companies are looking into using AI to varying degrees, whether to make their workforce more efficient and productive or to build full models that facilitate technical processes. With this in mind, and with the advent of basic malware that can use API keys to query LLMs and AI services, we will likely see this particular malware set evolve over time. By doing this lab, you'll begin to see how these pieces of malware are just the stub and querier for AI and how they can be used maliciously. This will showcase what this threat is like in its current state. We shall be monitoring how this threat evolves, so stay tuned for more labs. Who is the defensive lab for? SOC Analysts Incident Responders Threat Hunting Here is a link to the lab: https://immersivelabs.online/v2/labs/malterminal-analysis
