AI Red & Blue Teaming: Adversarial AI Security

About this course

This course contains the use of artificial intelligence.

Here's a crisper version:

This course provides a comprehensive, hands-on introduction to AI Red Teaming and Blue Teaming, covering the essential frameworks, attack techniques, and defense strategies required to secure modern AI and LLM-based systems. Whether you're a cybersecurity professional, AI engineer, risk manager, consultant, or technology leader, this course equips you with the practical skills needed to assess, attack, defend, and strengthen AI systems against real-world threats.

Includes a downloadable professional toolkit with attack scenario libraries, vulnerability scorecards, assessment checklists, security playbooks, methodology flowcharts, and enterprise-ready templates for immediate deployment.

The course explores the following key topics:

AI Security Fundamentals and the Modern AI Stack, helping learners understand LLM architecture, multimodal models, vector databases, RAG pipelines, and the broader AI threat landscape.

AI Attack Surface and Red Teaming Techniques, including prompt injection, jailbreaking, data poisoning, adversarial example generation, model inversion, model extraction, and supply-chain threats.

MITRE ATLAS and OWASP Top 10 for LLMs, providing a structured approach to identifying, mapping, and exploiting vulnerabilities in AI systems using globally recognized frameworks.

Blue Team Defense and AI Security Controls, covering detection techniques, input validation, anomaly monitoring, content safety layers, SIEM integration, incident response playbooks, and forensic analysis for AI-driven environments.

RAG, LLM, and Enterprise AI Hardening, focusing on securing vector databases, retrieval pipelines, API layers, content moderation, access controls, and model monitoring strategies.

AI Incident Management and Response, enabling learners to classify

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