AI Red & Blue Teaming: Adversarial AI Security

This course contains the use of artificial intelligence.

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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 incidents, assess business impact, conduct investigations, perform containment, and implement remediation actions.

AI Governance, Compliance, and Risk Management, addressing responsible AI practices, model documentation, transparency, bias assessments, and alignment with global regulations such as the EU AI Act, NIST AI RMF, and EO 14110.

AI Red Team and Blue Team Labs, offering guided hands-on exercises across LLM vulnerabilities, computer vision attacks, API exploitation, detection system configuration, and full-scale end-to-end security assessments.

Additionally, the course provides step-by-step guidance on building an enterprise-grade AI security program, including red teaming strategy, skills development, tooling, continuous monitoring, and integration with broader cybersecurity programs.

By the end of the course, learners will be able to:

• Understand the foundational concepts of AI security, LLM architecture, and adversarial threat models.

• Identify and analyze vulnerabilities across input, model, system, and supply-chain layers of AI applications.

• Execute Red Team attacks such as prompt injection, jailbreaks, adversarial examples, model extraction, and data poisoning.

• Map AI threats using MITRE ATLAS, OWASP Top 10 for LLMs, and structured threat modeling techniques.

• Implement Blue Team defenses including monitoring pipelines, anomaly detection, content filtering, and runtime protection.

• Conduct AI-focused incident response, forensic investigations, and post-incident reviews.

• Apply risk assessment methodologies to evaluate business, technical, and ethical impacts of AI vulnerabilities.

• Build secure RAG systems by protecting embeddings, retrieval pipelines, knowledge bases, and API integrations.

• Navigate emerging AI regulations, governance requirements, and responsible AI practices.

• Develop and manage an AI Red Team or Blue Team program with workflows, metrics, reporting, and continuous improvement.

• Strengthen enterprise AI systems using robust security controls, hardening techniques, and best practices.

Through practical labs, real-world case studies, interactive exercises, and a professional toolkit, this masterclass empowers learners to design, test, defend, and govern AI systems with confidence—ensuring organizational safety, resilience, and regulatory readiness in an AI-driven world.