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OP 09 July, 2025 - 06:57 PM
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 Requirements
  • Some exposure to OWASP or NIST frameworks
DescriptionThis comprehensive course on OWASP GenAI Red Teaming Complete Guide equips learners with practical and strategic expertise to test and secure generative AI systems. The curriculum begins with foundational concepts, introducing learners to the generative AI ecosystem, large language models (LLMs), and the importance of red teaming to uncover security, safety, and trust failures. It contrasts GenAI red teaming with traditional methods, highlighting how risks evolve across model architectures, human interfaces, and real-world deployments. Through in-depth risk taxonomy, students explore OWASP and NIST risk categories, STRIDE modeling, MITRE ATLAS tactics, and socio-technical frameworks like the RAG Triad. Key attack surfaces across LLMs, agents, and multi-modal inputs are mapped to emerging threat vectors. The course then presents a structured red teaming blueprint—guiding learners through scoping engagements, evaluation lifecycles, and defining metrics for success and brittleness.
Advanced modules dive into prompt injection, jailbreaks, adversarial prompt design, multi-turn exploits, and bias evaluation techniques. Students also assess model vulnerabilities such as hallucinations, cultural insensitivity, and alignment bypasses. Implementation-level risks are analyzed through tests on content filters, prompt firewalls, RAG vector manipulation, and access control abuse. System-level modules examine sandbox escapes, API attacks, logging gaps, and supply chain integrity. Learners are also introduced to runtime and agentic risks like overtrust, social engineering, multi-agent manipulation, and traceability breakdowns.
Practical tooling sessions feature hands-on red teaming with PyRIT, PromptBench, automation workflows, and playbook design. Finally, the course addresses operational maturity—showing how to build cross-functional red teams, align roles with RACI matrices, and apply red teaming within regulatory and cultural boundaries. With case-driven instruction and security-by-design thinking, this course prepares learners to operationalize GenAI red teaming at both the technical and governance levels.
Who this course is for:
  • AI Security Engineers looking to build red teaming capabilities for LLM systems
  • Cybersecurity Analysts and SOC teams responsible for detecting GenAI misuse
  • Red Team Professionals seeking to expand into AI-specific adversarial simulation
  • Risk, Compliance, and Governance Leads aiming to align GenAI systems with NIST, OWASP, or EU AI Act standards
  • Product Owners and Engineering Managers deploying GenAI copilots or RAG-based assistants
  • AI Researchers and Data Scientists focused on model safety, bias mitigation, and interpretability
  • Ethics, Policy, and Trust & Safety teams developing responsible AI frameworks and testing protocols
  • Advanced learners and cybersecurity students wanting hands-on exposure to adversarial GenAI evaluation
  • Organizations adopting LLMs in regulated domains such as finance, healthcare, legal, and government

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