The Immersive Workforce Exercises scenarios offer a diverse range of interactive exercises and content designed to enhance workforce awareness, preparedness, and skills in cybersecurity and privacy matters. These scenarios cover various topics such as workforce exercising, data subject rights requests, smishing threats, and more, providing a comprehensive learning experience for users to strengthen their cybersecurity knowledge and practices.
Standard Scenarios
These scenarios cover multiple risk areas. Written with a rich, realistic narrative, the participant makes decisions based on an evolving storyline.
| Title | Risk Areas | Description |
Operational Threat (Standard) scenario
Note: You will need an Immersive OT/ICS license package for this scenario
| Title | Risk Areas | Description |
| Orchid Gas: Gas Distribution Incident | Unauthorized Access | In this scenario, the topics of industrial control system (ICS) security, incident response during active shifts, and thwarting criminal groups are covered as an external threat targets your engineering workstations during normal operations. |
Multi-role scenarios
The participant makes decisions across multiple job roles, as the storyline evolves. These scenarios can cover multiple risk areas.
| Title | Risk Areas | Description |
Baselining scenario
An assessment-focused scenario. We recommend assigning this scenario at the beginning of your exercising journey to provide baseline data, identify priority areas for interventions, and monitor your human cyber risk profile over time.
| Title | Risk Areas | Description |
| Security Hygiene Compass | Authentication Device security Physical security Security reporting and responsiveness Data handling Digital footprint Social engineering Browsing securely | This scenario is a series of 16 "mini-scenarios" that cover all topic areas. It has been designed to focus on data quality and enable you to understand your current human cyber risk profile in a single exercise. We recommend that this scenario is used at the beginning of your Immersive Labs journey to provide baseline data, identify priority areas for interventions, and monitor your human cyber risk profile over time. |
Template scenario
A standard scenario that follows a narrative storyline but requires customization. Replace the business names, logos, documents, and more, to personalize the scenario to your organization.
| Title | Risk Areas | Description |
| A Successful Event Template | Data handling Security reporting and responsiveness | You’ve recently been joined in your team by a new colleague who’s just coming to the end of their first week and have been busy helping them understand your organization’s systems and processes. This is a template scenario focused on data privacy and data handling. It has been designed to enable you to easily customize the content and is accompanied by a user guide and editable rich media. |
Policy & Regulation scenarios
Simple scenarios to deliver a new or updated policy/regulation to your workforce and collect acknowledgement and agreement.
| Title | Risk Areas | Description |
Phishing Assessment scenarios
Participants face multiple decisions around phishing, smishing, vishing, etc.
| Title | Risk Areas | Description |
Spotcheck scenarios
These micro-scenarios provide targeted and responsive content to enhance workforce awareness and preparedness in cybersecurity and privacy matters.
| Title | Risk Areas | Description |
Enablement scenarios
These scenarios use an exercise format to guide managers and learners through the benefits and importance of workforce exercising, as well as how to effectively utilize the content to achieve desired outcomes. These scenarios aim to enhance understanding and engagement with workforce exercising practices for both managers and learners within the organization.
| Title | Risk Areas | Description |
AI Essentials
On the Immersive Labs cybersecurity training platform, the AI Essentials category builds practical knowledge and skills for understanding, using, and securing artificial intelligence—especially modern generative AI and large language models. It blends core AI concepts with hands-on defense techniques, governance considerations, and threat-focused scenarios so learners can safely adopt AI and respond to emerging risks.
Learners explore the OWASP Top 10 for LLMs and GenAI, a 10‑lab collection that develops the ability to identify, exploit, and mitigate risks such as prompt injection, sensitive information disclosure, supply chain weaknesses, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption. AI Fundamentals lays a strong base across AI concepts, data ethics and responsible use, emerging threats, TensorFlow, image classification, generative AI models, prompt injection attacks, and incident response, culminating in a skills demonstration. AI Foundations dives deeper into modern architectures and patterns—Large Language Models (LLMs), Retrieval Augmented Generation (RAG), Model Context Protocol (MCP), and Agentic AI—alongside a knowledge check. Fundamental AI Algorithms teaches practical machine learning with security-flavored use cases using K-Means, Decision Trees, and SVMs for tasks like beacon, script, and behavior detection. AI for Business equips decision‑makers with an understanding of what AI is, its benefits and risks, and how to use AI at work responsibly.
This category is designed for security practitioners, incident responders, detection engineers, developers building with LLMs, and business and risk leaders. By completing it, learners will be equipped to evaluate and securely deploy AI capabilities, recognize and mitigate LLM‑specific risks, implement guardrails and governance, and respond confidently to AI‑driven threats.
Collections
| Collection Name | Lab Count |
|---|---|
| OWASP Top 10 for LLMs and GenAI | 10 |
| AI Fundamentals | 9 |
| AI Foundations | 7 |
| Fundamental AI Algorithms | 7 |
| AI for Business | 6 |
| AWS Bedrock Guardrails | 3 |
| Azure Foundry Guardrails | 3 |
| NVIDIA NeMo Guardrails | 4 |
| AI Agents Idendity | 3 |
OWASP Top 10 for LLMs and GenAI
| Lab | Difficulty | Format |
|---|---|---|
| OWASP Top 10 for LLMs and GenAI: Prompt Injection | 2 | theory |
| OWASP Top 10 for LLMs and GenAI: Sensitive Information Disclosure | 2 | theory |
| OWASP Top 10 for LLMs and GenAI: Supply Chain | 2 | theory |
| OWASP Top 10 for LLMs and GenAI: Data and Model Poisoning | 2 | theory |
| OWASP Top 10 for LLMs and GenAI: Improper Output Handling | 2 | theory |
| OWASP Top 10 for LLMs and GenAI: Excessive Agency | 2 | theory |
| OWASP Top 10 for LLMs and GenAI: System Prompt Leakage | 2 | theory |
| OWASP Top 10 for LLMs and GenAI: Vector and Embedding Weaknesses | 2 | theory |
| OWASP Top 10 for LLMs and GenAI: Misinformation | 2 | theory |
| OWASP Top 10 for LLMs and GenAI: Unbounded Consumption | 2 | theory |
AI Fundamentals
| Lab | Difficulty | Format |
|---|---|---|
| AI: Introduction to AI | 2 | theory |
| AI: Data Ethics and Responsible Use | 2 | theory |
| AI: Emerging Threats | 2 | theory |
| AI: TensorFlow for Machine Learning | 3 | practical |
| AI: Image Classification | 3 | practical |
| AI: Generative AI Models | 2 | practical |
| AI: Prompt Injection Attacks | 5 | practical |
| AI: Artificial Intelligence for Incident Responders | 2 | practical |
| AI: Demonstrate Your Skills | 4 | practical |
AI Foundations
| Lab | Difficulty | Format |
|---|---|---|
| AI Foundations: Artificial Intelligence | 1 | theory |
| AI Foundations: Core Components | 1 | theory |
| AI Foundations: Large Language Models (LLMs) | 1 | theory |
| AI Foundations: Retrieval Augmented Generation (RAG) | 2 | practical |
| AI Foundations: Model Context Protocol (MCP) | 2 | practical |
| AI Foundations: Agentic AI | 2 | practical |
| AI Foundations: Demonstrate Your Knowledge | 1 | theory |
Fundamental AI Algorithms
| Lab | Difficulty | Format |
|---|---|---|
| Fundamental AI Algorithms: Introduction | 3 | theory |
| Fundamental AI Algorithms: K-Means Introduction | 5 | practical |
| Fundamental AI Algorithms: K-Means Beacon Detection | 6 | practical |
| Fundamental AI Algorithms: Decision Trees Introduction | 5 | practical |
| Fundamental AI Algorithms: Decision Trees Script Detection | 6 | practical |
| Fundamental AI Algorithms: SVMs Introduction | 5 | practical |
| Fundamental AI Algorithms: SVMs Behavior Detection | 6 | practical |
AI for Business
| Lab | Difficulty | Format |
|---|---|---|
| AI for Business: Defining Artificial Intelligence | 1 | theory |
| AI for Business: Algorithms and Datasets | 1 | theory |
| AI for Business: The AI Ecosystem | 1 | theory |
| AI for Business: Risks and Responsible Integration | 1 | theory |
| AI for Business: Regulatory and Ethical Landscapes | 1 | theory |
| AI for Business: Real-World Applications | 1 | theory |
AWS Bedrock Guardrails
| Collection | Difficulty | Format |
|---|---|---|
| AWS Bedrock Guardrails: Jailbreak Protection | 5 | practical |
| AWS Bedrock Guardrails: Prompt Injection Protection | 5 | practical |
| AWS Bedrock Guardrails: PII Masking | 5 | practical |
Azure Foundry Guardrails
| Collection | Difficulty | Format |
|---|---|---|
| Azure Foundry Guardrails: Jailbreak Protection | 5 | practical |
| Azure Foundry Guardrails: Prompt Injection Protection | 5 | practical |
| Azure Foundry Guardrails: PII Masking | 5 | practical |
NVIDIA NeMo Guardrails
| Collection | Difficulty | Format |
|---|---|---|
| NVIDIA NeMo Guardrails: LLM-as-a-judge | 4 | practical |
| NVIDIA NeMo Guardrails: Jailbreak Protection | 4 | practical |
| NVIDIA NeMo Guardrails: Prompt Injection Protection | 5 | practical |
| NVIDIA Guardrails: PII Masking | 5 | practical |
Agent Identity
| Collection | Difficulty | Format |
|---|---|---|
| Agent Identity: Token Scoping | 5 | practical |
| Agent Identity: Multi-Agent Authentication | 5 | practical |
| Agent Identity: Demonstrate Your Knowledge | 4 | theory |
Building with AI
The Building with AI category on the Immersive Labs cybersecurity training platform guides practitioners through designing, implementing, and securing AI-enabled applications and agent workflows from first prompt to production. Through hands-on labs, you’ll build proficiency in manual prompting and spec-driven development, safe tool invocation via the Model Context Protocol (MCP) and extensions, multi-agent patterns, plugin and slash-command interfaces, sandboxing, hooks, and skills. You will also learn to implement policy engines and guardrails that deliver governance, auditability, and risk controls for real-world use.
In the Building with AI: Claude Code collection, learners progress from foundational prompting to advanced topics including Tools and MCP, Slash Commands, Claude Skills, Subagents, Hooks, Plugins, and Guardrails, culminating in a Demonstrate Your Knowledge capstone. Building with AI: Gemini CLI adds agent skills, sandboxes, hooks, a policy engine, and guardrails to help you design resilient, governed agent workflows, while Building with AI: Codex CLI focuses on practical prompting, spec-driven development, Tools and MCP, Slash Commands, and Guardrails for streamlined, secure delivery. This category is ideal for software engineers, security engineers, DevSecOps practitioners, and platform teams who need to ship AI features responsibly; by the end, you’ll be equipped to prototype and integrate AI, apply guardrails and policies, govern tool use, and operate AI systems that are robust, auditable, and aligned with security and compliance requirements.
Collections
| Collection Name | Lab Count |
|---|---|
| Building with AI: Claude Code | 11 |
| Building with AI: Gemini CLI | 10 |
| Building with AI: Codex CLI | 7 |
| AI Agent Governance | 3 |
| Model Evaluation | 3 |
Building with AI: Claude Code
| Lab | Difficulty | Format |
|---|---|---|
| Building with AI: Claude Code – Introduction | 3 | practical |
| Building with AI: Claude Code – Manual Prompting | 3 | practical |
| Building with AI: Claude Code – Spec-Driven Development | 4 | practical |
| Building with AI: Claude Code – Tools and MCP | 4 | practical |
| Building with AI: Claude Code – Slash Commands | 4 | practical |
| Building with AI: Claude Code – Claude Skills | 4 | practical |
| Building with AI: Claude Code – Subagents | 4 | practical |
| Building with AI: Claude Code – Hooks | 4 | practical |
| Building with AI: Claude Code – Plugins | 4 | practical |
| Building with AI: Claude Code – Guardrails | 3 | practical |
| Building with AI: Claude Code – Demonstrate Your Knowledge | 4 | theory |
Building with AI: Gemini CLI
| Lab | Difficulty | Format |
|---|---|---|
| Building with AI: Gemini CLI – Introduction | 3 | practical |
| Building with AI: Gemini CLI – Manual Prompting | 3 | practical |
| Building with AI: Gemini CLI – Spec-Driven Development (Conductor) | 3 | practical |
| Building with AI: Gemini CLI – Agent Skills | 4 | practical |
| Building with AI: Gemini CLI – Sandboxes | 4 | practical |
| Building with AI: Gemini CLI – Hooks | 4 | practical |
| Building with AI: Gemini CLI – Policy Engine | 4 | practical |
| Building with AI: Gemini CLI – Guardrails | 3 | practical |
| Building with AI: Gemini CLI – Tools, MCP, and Extensions | 4 | practical |
| Building with AI: Gemini CLI – Demonstrate Your Knowledge | 4 | theory |
Building with AI: Codex CLI
| Lab | Difficulty | Format |
|---|---|---|
| Building with AI: Codex CLI – Introduction | 3 | practical |
| Building with AI: Codex CLI – Manual Prompting | 4 | practical |
| Building with AI: Codex CLI – Spec-Driven Development | 4 | practical |
| Building with AI: Codex CLI – Tools and MCP | 3 | theory |
| Building with AI: Codex CLI – Slash Commands | 4 | practical |
| Building with AI: Codex CLI – Guardrails | 3 | practical |
| Building with AI: Codex CLI – Demonstrate Your Knowledge | 4 | theory |
Secure AI Adoption
Regulated industries and large organizations are trying to find ways to implement AI without it scaring leadership and these collections can help bridge the knowledge gaps required to effectively answer these questions.
Secure AI Adoption will empower your teams to:
- Enforce "Secure by Design" principles: Wrap unpredictable models in a verified security layer.
- Accelerate Innovation: Move to production faster by neutralizing poor implementation of AI inside an organization.
Collections
| Collection Name | Lab Count |
|---|---|
| AI Governance | 3 |
| AI Data Protection | 3 |
| Agentic Observability | 3 |
AI Governance
| Lab | Difficulty | Format |
| AI Governance: AI Lifecycles and Determining ROI | 2 | theory |
| AI Governance: AI Frameworks - NIST AI RMF and ISO/IEC 42001 | 2 | theory |
| AI Governance: Demonstrate Your Knowledge | 3 | theory |
AI Data Protection
| Lab | Difficulty | Format |
| AI Data Protection: Data Lineage | 2 | theory |
| AI Data Protection: Data Loss Prevention (DLP) | 2 | theory |
| AI Data Protection: Demonstrate Your Knowledge | 3 | theory |
Agentic Observability
| Lab | Difficulty | Format |
| Agentic Observability: AI Observability Principles | 3 | practical |
| Agentic Observability: Observability Analysis | 5 | practical |
| Agentic Observability: Demonstrate Your Knowledge | 3 | theory |
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