AI Security
Master the art and science of securing AI and machine learning systems. From adversarial attacks and prompt injection to governance frameworks and zero trust architectures — learn to protect AI systems at every layer.
AI Security is the adversarial lens on everything else on the site. For any AI system exposed to untrusted input, whether that is a user prompt, a retrieved document, an API payload, or a web page an agent browses, there is a growing research and attacker literature on how to subvert it. Prompt injection (direct and indirect), jailbreaks, data exfiltration via model output, supply-chain attacks on open model weights, and agent hijacking are no longer theoretical; they have been demonstrated on real systems.
The track covers both sides: the attacks (MITRE ATLAS, OWASP LLM Top 10, published red-team findings from the major labs) and the defensive patterns that are actually effective (input/output filtering, structured outputs with validation, sandboxing of tool execution, least-privilege agent design, continuous red teaming, confidential computing for sensitive inference). The stance is practical: we cover what attackers are actually doing and what defenders have shipped that works, not what the textbooks hope will work.
All Courses
20 comprehensive courses covering every aspect of AI and ML security.
Core Security
AI Security Fundamentals
Build a solid foundation in AI security principles, threat landscapes, and defense strategies for machine learning systems.
7 LessonsModel Security & Protection
Protect ML models from theft, extraction, and inversion attacks with encryption, watermarking, and access controls.
7 LessonsAI Threat Modeling
Apply structured threat modeling methodologies like STRIDE to AI and ML systems for proactive security.
7 LessonsModel Watermarking & IP Protection
Protect intellectual property in ML models with watermarking, fingerprinting, and legal frameworks.
7 LessonsAttack & Defense
Adversarial Attacks & Defenses
Understand adversarial machine learning attacks including FGSM, PGD, and black-box methods, plus proven defense techniques.
7 LessonsLLM Security & Prompt Injection
Protect large language models from prompt injection, jailbreaking, and data exfiltration attacks.
7 LessonsDeepfake Detection
Detect AI-generated synthetic media including image, video, and audio deepfakes using modern detection techniques.
7 LessonsPrivacy & Compliance
Data Privacy for AI Systems
Implement data privacy protections for AI systems including GDPR compliance, anonymization, and privacy-preserving ML.
7 LessonsAI Governance & Compliance
Navigate AI regulations including the EU AI Act and NIST AI RMF, and build effective governance programs.
7 LessonsDifferential Privacy for ML
Apply differential privacy to machine learning with DP-SGD, privacy budgets, and practical implementation patterns.
7 LessonsApplied Security
Secure ML Pipelines
Build end-to-end secure machine learning pipelines from data ingestion to deployment with proper security controls.
7 LessonsFederated Learning Security
Secure federated learning systems against Byzantine attacks with secure aggregation and differential privacy.
7 LessonsAI Supply Chain Security
Manage risks in the AI supply chain including pre-trained models, datasets, and dependency vulnerabilities.
7 LessonsAI-Powered Cybersecurity
Leverage AI and ML for cybersecurity including intrusion detection, malware analysis, and automated threat response.
7 LessonsSecuring AI APIs
Protect AI-powered APIs with authentication, rate limiting, input validation, and comprehensive monitoring.
7 LessonsSecure Data Labeling
Implement security controls for data labeling workflows including PII protection, access controls, and audit trails.
7 LessonsSecurity Operations
AI Red Teaming
Plan and execute AI red team exercises to test model robustness, safety, and security before deployment.
7 LessonsAI Incident Response
Prepare for and respond to AI security incidents with detection, triage, containment, and recovery procedures.
7 LessonsZero Trust for AI Systems
Apply zero trust architecture principles to AI workloads with identity, micro-segmentation, and continuous verification.
7 LessonsAI Security Auditing
Conduct comprehensive AI security audits covering technical assessment, data security, and compliance verification.
7 LessonsWhat You'll Learn
Skills you will gain across these 20 AI security courses.
Threat Analysis
Identify and model threats to AI systems including adversarial attacks, data poisoning, model extraction, and prompt injection vulnerabilities.
Defense Implementation
Build robust defenses with adversarial training, input sanitization, output filtering, differential privacy, and secure ML pipelines.
Governance & Compliance
Navigate AI regulations like the EU AI Act and NIST AI RMF. Build governance programs with audit practices and compliance automation.
Security Operations
Conduct red team exercises, respond to AI incidents, perform security audits, and implement zero trust architectures for AI workloads.