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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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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AI Security
Module 4 Lesson 5: Data Provenance & Integrity
Know your sources. Learn how to implement data lineage and integrity checks to ensure that your training data hasn't been tampered with or replaced.
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AI Security
Module 5 Lesson 1: Model Extraction & Stealing
Your model is your IP. Learn how attackers use 'Query-Answer' pairs to clone your proprietary models for a fraction of the original training cost.
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AI Security
Module 5 Lesson 2: Membership Inference Attacks
Is your data in there? Learn how attackers can determine if a specific record (like a medical file) was used to train a model, violating user privacy.
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AI Security
Module 5 Lesson 3: Training Data Leakage
How LLMs recite their training data. Explore the 'Memorization vs. Learning' trade-off and how to prevent your model from leaking secrets.
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AI Security
Module 5 Lesson 4: Model Inversion Attacks
Reverse-engineering the training set. Learn how attackers work backwards from a model's outputs to reconstruct the sensitive images or text used in training.
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AI Security
Module 5 Lesson 5: AI Intellectual Property Risks
Is your model legally protected? Explore the legal and technical landscape of AI IP, from copyright issues to the dangers of using 'License-Violating' data.
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AI Security
Module 6 Lesson 1: What are Adversarial Examples?
Why models misidentify pandas as gibbons. Explore the phenomenon of adversarial examples and how imperceptible noise can fool neural networks.
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AI Security
Module 6 Lesson 2: Evasion Attacks
Slip past the guards. Learn about evasion attacks where AI models are bypassed in real-time to allow malicious files or actors through security filters.
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AI Security
Module 6 Lesson 3: Gradient vs. Black-Box
How to craft the perfect attack. Understand the difference between having the model's 'Code' (White-Box) and only having its 'Answers' (Black-Box).
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AI Security
Module 6 Lesson 4: Robustness Limitations
Why we can't just 'Patch' AI. Explore the fundamental reasons why deep neural networks are inherently fragile and vulnerable to adversarial noise.
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AI Security
Module 6 Lesson 5: AI Defense Strategies
How to fight back. Explore the most effective ways to defend against adversarial attacks, from adversarial training to input transformation and certified robustness.
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AI Security
Module 7 Lesson 1: What is Prompt Injection?
The #1 AI security threat. Learn the foundations of prompt injection—how attackers hijack an LLM's logic by blending instructions with data.
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AI Security
Module 7 Lesson 2: Direct vs. Indirect Injection
Know your vectors. Learn the difference between a user attacking their own session (Direct) and an attacker poisoning external data (Indirect).
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AI Security
Module 7 Lesson 3: System Prompt Leakage
Your secret instructions, revealed. Learn how attackers trick LLMs into reciting their internal guidelines, codenames, and proprietary logic.
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AI Security
Module 7 Lesson 4: Jailbreak Techniques
Breaking the rules. Explore the history and mechanics of AI jailbreaks, from 'DAN' and 'Do Anything Now' to sophisticated persona adoption and adversarial suffixes.
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AI Security
Module 7 Lesson 5: Prompt Chaining Risks
The chain is only as strong as its weakest prompt. Learn how vulnerabilities propagate through multi-step AI workflows (chains) and how to break the cycle.
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AI Security
Module 8 Lesson 1: Why You Can't Trust AI Output
The 'Implicit Trust' trap. Learn why AI-generated content must be treated as untrusted user input and the dangers of bypassing conventional security checks.
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AI Security
Module 8 Lesson 2: XSS via AI Responses
How AI becomes an XSS vector. Learn how attackers use prompt injection to trick LLM-powered websites into rendering malicious scripts for other users.
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AI Security
Module 8 Lesson 3: SSRF & RCE via Tools
When AI gets a shell. Learn how attackers use tool-calling AIs to perform Server-Side Request Forgery and Remote Code Execution inside your infrastructure.
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AI Security
Module 8 Lesson 4: Sanitizing AI Content
The digital car wash. Learn the technical techniques for cleaning AI output before it touches your users, your database, or your infrastructure.
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