AI Security
Module 15 Lesson 4: Custom Guardrail Dev
Building the shield yourself. Learn how to write custom Python-based guardrails to enforce your organization's unique security and business policies.
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AI Security Articles
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AI Security
Module 15 Lesson 5: Hardening Guardrails
Breaking the muzzle. Learn the techniques attackers use to bypass AI guardrails (obfuscation, translation, multi-turn) and how to harden your defenses.
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AI Security
Module 16 Lesson 1: Securing Cloud AI APIs
Locking the gate. Learn the specific security configurations and best practices for using enterprise AI services like Azure OpenAI and AWS Bedrock.
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AI Security
Module 16 Lesson 2: IAM for AI
Least privilege for models. Learn how to use IAM roles, policies, and identities to control which users and applications can access your AI models.
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AI Security
Module 16 Lesson 3: AI Network Isolation
Air-gapping the brain. Learn how to use VNETs, VPCs, and Firewalls to ensure your AI infrastructure is never exposed to the public internet.
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AI Security
Module 16 Lesson 4: AI Cost Monitoring
Protecting the wallet. Learn how to set up alerts and quotas to prevent 'Denial of Wallet' attacks and runaway AI spending.
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AI Security
Module 16 Lesson 5: Encryption & Residency
Sovereign AI. Learn the technical and legal requirements for keeping AI data within specific geographic boundaries and encrypted at every stage.
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AI Security
Module 17 Lesson 1: Orchestrator Risks
The glue that breaks. Learn how framework orchestrators like LangChain and LlamaIndex introduce new security vulnerabilities through complex chaining and data handling.
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AI Security
Module 17 Lesson 2: Securing LangChain
Hardening the chains. Learn specific security configurations for LangChain Agents, including sandboxing, tool limiting, and secure memory management.
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AI Security
Module 17 Lesson 3: Securing LlamaIndex
Data bridge security. Learn how to secure LlamaIndex data loaders, prevent context poisoning, and implement private data connectors.
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AI Security
Module 17 Lesson 4: AI Security Proxies
The intelligent firewall. Learn how to use Middleware and Proxies (like LiteLLM, Portkey) to centralize security, logging, and access control for all your AI models.
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AI Security
Module 17 Lesson 5: Framework-Specific Exploits
Poking the glue. Learn how to identify and test for vulnerabilities unique to LangChain, LlamaIndex, and other AI orchestration frameworks.
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AI Security
Module 18 Lesson 1: Membership Inference
Were you in the dataset? Learn the mathematical attacks used to determine if a specific individual's data was used to train a machine learning model.
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AI Security
Module 18 Lesson 2: Model Inversion
Re-creating the secret. Learn how attackers use 'Model Inversion' to reconstruct raw images and text from a machine learning model's output.
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AI Security
Module 18 Lesson 3: Adversarial Reprogramming
New task, old model. Learn how attackers 'Reprogram' pre-trained models to perform entirely different (and potentially malicious) tasks without changing any weights.
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AI Security
Module 18 Lesson 4: Quantization Risks
Smaller is more vulnerable. Learn how technical optimizations like Quantization and Pruning can accidentally introduce new security vulnerabilities and 'Backdoors' into AI models.
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AI Security
Module 18 Lesson 5: Poisoning at Scale
The global hack. Learn how attackers influence the behavior of the world's most powerful Foundation Models (like GPT-4, Llama 3) by poisoning the public internet.
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AI Security
Module 19 Lesson 1: AI Risk Management
Managing the chaos. Learn how to build a formal Risk Management Framework specifically for AI, based on NIST and ISO standards.
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AI Security
Module 19 Lesson 2: AI Ethics & Bias
Fairness as a security feature. Learn how to audit AI models for bias, toxicity, and unethical behavior to prevent legal and reputational damage.
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AI Security
Module 19 Lesson 3: AI Security Policy
Rules of the road. Learn how to write a formal AI Security Policy that defines allowed usage, data handling, and responsibilities for your employees.
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AI Security
Module 19 Lesson 4: AI Vendor Risk
Who are you trusting? Learn how to evaluate the security of AI vendors (OpenAI, Anthropic, Midjourney) before integrating them into your business.
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AI Security
Module 19 Lesson 5: Passing an AI Audit
Proving your safety. Learn how to prepare for formal AI security audits and earn certifications like the 'EU AI Act' compliance or ISO 42001.
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AI Security
Module 2 Lesson 1: AI System Components
Deconstruct the components of modern AI systems, from data layers to infrastructure, to understand the critical pieces that require security monitoring.
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AI Security
Module 2 Lesson 2: Trust Boundaries in AI Systems
Understand the collapse of the traditional 'Data vs. Instructions' boundary in AI and how to redraw trust lines in LLM-powered applications.
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AI Security
Module 2 Lesson 3: Expanded Attack Surface
Why LLMs make your application harder to defend. Explore the new attack vectors introduced by prompt manipulation, tool use, and long-term memory.
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AI Security
Module 2 Lesson 4: AI Supply Chain Risks
Who built your model? Explore the security risks associated with third-party model weights, poisoned datasets, and malicious Python libraries in the AI ecosystem.
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AI Security
Module 20 Lesson 1: AI in Banking Security
Protecting the money. Learn the unique requirements for AI security in the finance sector, from Anti-Money Laundering (AML) to fraud detection.
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AI Security
Module 20 Lesson 2: AI in Healthcare Security
Protecting the patient. Learn the critical security and privacy requirements for AI in healthcare, from HIPAA compliance to securing medical diagnostic models.
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AI Security
Module 20 Lesson 3: AI in E-commerce Security
Protecting the shop. Learn how to secure AI in e-commerce, from preventing price manipulation in chatbots to securing recommendation engines.
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AI Security
Module 20 Lesson 4: AI in Government Security
Protecting the public trust. Learn the unique requirements for AI security in the public sector, from FedRAMP compliance to securing citizen data.
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AI Security
Module 20 Lesson 5: AI in Critical Infrastructure
Protecting the grid. Learn the high-stakes security requirements for AI in Industrial Control Systems (ICS), energy grids, and manufacturing.
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AI Security
Module 21 Lesson 1: AI as an Attacker
The automated adversary. Explore how attackers use LLMs to automate vulnerability discovery, write malware, and launch massive social engineering campaigns.
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AI Security
Module 21 Lesson 2: AGI Existential Risk
The ultimate security challenge. Explore the theories of AGI (Artificial General Intelligence) risk, the 'Inscrutability' of superintelligence, and the 'Stop-Button' problem.
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AI Security
Module 21 Lesson 3: Self-Defending AI
Fighting fire with fire. Explore the emerging field of 'Self-Defending' AI architectures that can detect and respond to attacks without external guardrails.
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AI Security
Module 21 Lesson 4: Decentralized AI Security
Security without a center. Explore the risks and defenses for decentralized AI marketplaces (like Bittensor) and Web3-integrated LLMs.
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AI Security
Module 21 Lesson 5: The Path Forward in AI Security
Mastering the shift. A strategic look at the evolving skills, certifications, and mindsets required to lead in the field of AI Security.
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AI Security
Module 22 Lesson 1: The AI Security Professional
Defining the role. A deep dive into the day-to-day responsibilities, toolsets, and team dynamics of a professional AI Security Engineer.
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AI Security
Module 22 Lesson 2: AI Security Capstone
Put it all together. Design a complete security architecture for a hypothetical enterprise AI application, from supply chain to guardrails.
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AI Security
Module 22 Lesson 3: Course Summary
The big picture. A comprehensive review of the 110 lessons covered in this course and the core principles of AI Security.
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AI Security
Module 22 Lesson 4: Final Exam
Test your knowledge. A comprehensive final exam covering all 22 modules of the AI Security course.
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AI Security
Module 22 Lesson 5: Certification
Mission accomplished. Learn how to claim your certificate, join the AI security community, and continue your professional journey.
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AI Security
Module 3 Lesson 1: Limits of Traditional Threat Models
Why firewalls and input validation aren't enough. Learn why traditional security frameworks need to evolve to address the unique challenges of AI.
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AI Security
Module 3 Lesson 2: STRIDE Adapted for AI
The industry standard for threat modeling, updated for the era of intelligence. Learn how to map Spoofing, Tampering, and Elevation of Privilege to AI systems.
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AI Security
Module 3 Lesson 3: AI-Specific Threat Categories
Meet the new class of vulnerabilities. Explore unique AI threats recognized by OWASP and MITRE ATLAS, including Membership Inference and Model Extraction.
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AI Security
Module 3 Lesson 4: Adversarial Thinking
How to think like a manipulator. Master the mental model of 'prompt manipulation' and learn why the best AI hackers are often social engineers, not coders.
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AI Security
Module 3 Lesson 5: AI Risk Prioritization
Not all threats are equal. Learn how to use the 'Likelihood vs. Impact' matrix to prioritize AI security risks and manage your resource allocation effectively.
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AI Security
Module 4 Lesson 1: Training Data as a Security Asset
Data is the code of AI. Learn why your training datasets must be protected with the same rigor as your production source code to prevent long-term vulnerabilities.
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AI Security
Module 4 Lesson 2: Data Poisoning Attacks
How attackers inject malicious behavior into models. Explore the mechanics of data poisoning and how small amounts of bad data can compromise global models.
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AI Security
Module 4 Lesson 3: Label Flipping & Backdoors
Precision poisoning. Learn how to execute label flipping attacks and how 'triggers' are used to create dormant backdoors in neural networks.
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AI Security
Module 4 Lesson 4: Data Leakage Risks
Why models shouldn't talk about their past. Explore the risks of personal data leaking from training sets and the 'over-memorization' problem in LLMs.
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